{
  "name": "Local AI model-recommendation dataset",
  "description": "Best open-weight models per task per hardware tier, for local inference. Provenance-tagged, sourced, and dated. A curated starting point, not a verdict.",
  "schemaVersion": "1.0",
  "generatedAt": "2026-10-27T12:09:32.519Z",
  "homepage": "https://local-ai.net/models/recommendations",
  "licence": "CC-BY-4.0",
  "cadence": "quarterly",
  "taskVocabulary": [
    {
      "id": "general-chat",
      "label": "General chat",
      "scope": "Everyday assistant use: questions, drafting, summarising."
    },
    {
      "id": "coding",
      "label": "Coding",
      "scope": "Code generation, editing, and agentic coding tools."
    },
    {
      "id": "reasoning",
      "label": "Reasoning",
      "scope": "Maths, logic, and multi-step problem solving."
    },
    {
      "id": "creative",
      "label": "Creative writing",
      "scope": "Prose, fiction, and tone. Taste-led, so weigh opinion accordingly."
    },
    {
      "id": "rag",
      "label": "RAG",
      "scope": "Answering over your own documents: grounding and faithfulness."
    },
    {
      "id": "agentic",
      "label": "Agentic",
      "scope": "Tool use, planning, and long multi-step workflows."
    }
  ],
  "hardwareBands": [
    {
      "id": "8gb",
      "label": "~8GB",
      "maps_to": "An entry GPU or a laptop."
    },
    {
      "id": "16gb",
      "label": "~16GB",
      "maps_to": "A mid-range GPU."
    },
    {
      "id": "24gb",
      "label": "~24GB",
      "maps_to": "An RTX 4090 / 3090 class card."
    },
    {
      "id": "48gb",
      "label": "~48GB",
      "maps_to": "A single large card or a dual-card setup."
    },
    {
      "id": "128gb-unified",
      "label": "128GB unified",
      "maps_to": "Apple Silicon, AMD Strix Halo, or NVIDIA Spark."
    },
    {
      "id": "256gb-unified",
      "label": "256GB unified",
      "maps_to": "A higher Mac Studio Ultra configuration."
    },
    {
      "id": "512gb-unified",
      "label": "512GB unified",
      "maps_to": "The top Mac Studio Ultra configuration."
    },
    {
      "id": "server",
      "label": "Server / multi-GPU",
      "maps_to": "Datacentre cards or multi-GPU rigs."
    }
  ],
  "provenances": {
    "objective-test": "Attributed benchmark or evaluation results. Carry source and date.",
    "public-opinion": "Community sentiment, stated as opinion rather than measurement. Carry source and date.",
    "user-verdict": "Bradley-Terry over the user's own verdicts. Computed locally in the client over the user's own material; never present in this dataset. Join it in client-side, do not blend it into the others."
  },
  "joinKey": "model",
  "views": {
    "any-quant": "Best model that fits a band at its best sensible quant (the entry's `tier` and `quant`). Maximum capability for the memory budget.",
    "native": "Model placed by its highest-precision version, no community down-quantisation (`tierNative` and `nativeQuant`). Apples-to-apples, no quant-quality trade. Filter on these fields to exclude quantised placements."
  },
  "entries": [
    {
      "task": "general-chat",
      "tier": "8gb",
      "quant": "Q8_0",
      "tierNative": "16gb",
      "nativeQuant": "BF16",
      "model": "qwen3-5-4b",
      "releaseDate": "2026-03",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "The current-generation lead at 8GB, a 2026 Qwen3.5 multimodal model, Apache 2.0.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong general-knowledge figure for a 4B model, vendor-reported, and well above the older small models at this size.",
          "benchmark": "MMLU-Pro",
          "score": "79.1",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.5-4B",
          "asOf": "2026-03"
        },
        {
          "provenance": "objective-test",
          "assessment": "An independent composite index as a cross-check, and a reminder that a 4B still knows far less than a larger model however it scores.",
          "benchmark": "Artificial Analysis Intelligence Index",
          "score": "27",
          "source": "Artificial Analysis (independent)",
          "sourceUrl": "https://artificialanalysis.ai/articles/qwen3-5-small-models",
          "asOf": "2026-03"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "8gb",
      "quant": "Q4_K_M",
      "tierNative": "24gb",
      "nativeQuant": "FP16",
      "model": "qwen3-8b",
      "releaseDate": "2025-04-29",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "An older (2025) alternative at 8GB, still a dependable all-rounder.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A reasoning-and-knowledge figure (thinking mode) standing in as a general capability proxy; strong for its size.",
          "benchmark": "GPQA Diamond (thinking)",
          "score": "62.0",
          "source": "Qwen3 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2505.09388v1",
          "asOf": "2025-05"
        },
        {
          "provenance": "public-opinion",
          "assessment": "Widely recommended as a dependable all-round model that fits an 8GB card.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "16gb",
      "quant": null,
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "gemma-4-26b",
      "releaseDate": "2026-04-02",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "The current-generation lead at 16GB.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong general-knowledge figure for a model that fits around 16GB.",
          "benchmark": "MMLU-Pro (thinking)",
          "score": "82.6",
          "source": "Gemma 4 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2607.02770v1",
          "asOf": "2026-04"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "16gb",
      "quant": "Q4_K_M",
      "tierNative": "48gb",
      "nativeQuant": "FP16",
      "model": "qwen3-14b",
      "releaseDate": "2025-04-29",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "An older (2025) alternative, still a dependable 16GB general model.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A reasoning-and-knowledge proxy, a clear step up from the 8B.",
          "benchmark": "GPQA Diamond (thinking)",
          "score": "64.0",
          "source": "Qwen3 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2505.09388v1",
          "asOf": "2025-05"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "16gb",
      "quant": "Q8_0",
      "tierNative": "24gb",
      "nativeQuant": "BF16",
      "model": "qwen3-5-9b",
      "releaseDate": "2026-03",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A current-generation Qwen3.5 multimodal model, Apache 2.0; fits 16GB at Q8 (or 8GB at Q4).",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong general-knowledge figure for a 9B, vendor-reported, which Qwen puts level with some much larger older models.",
          "benchmark": "MMLU-Pro",
          "score": "82.5",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.5-9B",
          "asOf": "2026-03"
        },
        {
          "provenance": "objective-test",
          "assessment": "An independent composite index as a cross-check.",
          "benchmark": "Artificial Analysis Intelligence Index",
          "score": "32",
          "source": "Artificial Analysis (independent)",
          "sourceUrl": "https://artificialanalysis.ai/articles/qwen3-5-small-models",
          "asOf": "2026-03"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "gemma-4-31b",
      "releaseDate": "2026-07-30",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Google's dense flagship; the top open model in its dense category by Arena.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong general-knowledge figure; the model also tops the dense open category on human-preference Arena.",
          "benchmark": "MMLU-Pro (thinking)",
          "score": "85.2",
          "source": "Gemma 4 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2607.02770v1",
          "asOf": "2026-07"
        },
        {
          "provenance": "objective-test",
          "assessment": "A human-preference figure, vendor-reported via Arena; style-controlled Arena is preference, not correctness, so read it as \"people liked its answers\".",
          "benchmark": "LMArena Elo",
          "score": "1451 (top dense open model)",
          "source": "Gemma 4 technical report, citing LMArena (vendor-reported)",
          "sourceUrl": "https://arxiv.org/html/2607.02770v1",
          "asOf": "2026-07"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-32b",
      "releaseDate": "2025-04-29",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "An older (2025) alternative, still solid and doubling as a reasoning model.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A reasoning-and-knowledge proxy; dependable, a generation behind the leads above.",
          "benchmark": "GPQA Diamond (thinking)",
          "score": "68.4",
          "source": "Qwen3 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2505.09388v1",
          "asOf": "2025-05"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "qwen3-6-35b-a3b",
      "releaseDate": "2026-04-16",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A fast current-generation mixture-of-experts that fits 24GB.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong general-knowledge figure for a sparse model you can run on one card.",
          "benchmark": "GPQA",
          "score": "86.0",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
          "asOf": "2026-04"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "24gb",
      "quant": "Q5_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-8-27b",
      "releaseDate": "2026-08-13",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A current-generation lead at 24GB, and a top coder too (see the coding list).",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong science-reasoning figure for a single-card general model.",
          "benchmark": "GPQA Diamond (thinking)",
          "score": "89.2",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-27B",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "48gb",
      "quant": "Q8_0",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "gemma-4-31b",
      "releaseDate": "2026-07-30",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Another current-generation 48GB option: the dense 31B at Q8 (about 33GB, near-lossless), for people who prefer Gemma's style, with headroom for context.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong general-knowledge figure, run here at near-lossless Q8.",
          "benchmark": "MMLU-Pro (thinking)",
          "score": "85.2",
          "source": "Gemma 4 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2607.02770v1",
          "asOf": "2026-07"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "48gb",
      "quant": "Q4_K_M",
      "tierNative": "256gb-unified",
      "nativeQuant": "FP16",
      "model": "llama-3-3-70b",
      "releaseDate": "2024-12-06",
      "licence": "Llama 3.3 Community License",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://www.llama.com/llama3_3/license/",
      "note": "The other way to spend 48GB: a larger model quantised to fit (Q4 about 43GB, Q5 about 50GB). More parameters and breadth than a 27B, but dated (late 2024) and the ~70B dense open class has thinned out, so weigh the extra size against older knowledge. No current-generation 100B+ model quantises cleanly into 48GB.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A respectable general-knowledge figure for its day, now behind current models.",
          "benchmark": "MMLU-Pro (CoT)",
          "score": "68.9",
          "source": "Meta model card (vendor)",
          "sourceUrl": "https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct",
          "asOf": "2024-12"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "48gb",
      "quant": "Q8_0",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-8-27b",
      "releaseDate": "2026-08-13",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "The current-generation way to spend 48GB: run the 27B at Q8 (about 29GB, near-lossless) and give the headroom to context. Newer knowledge than a quantised older 70B, at higher fidelity than the same model on a 24GB card.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong science-reasoning figure, run here at near-lossless Q8.",
          "benchmark": "GPQA Diamond (thinking)",
          "score": "89.2",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-27B",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "128gb-unified",
      "quant": "MXFP4 (native)",
      "tierNative": "256gb-unified",
      "nativeQuant": "BF16",
      "model": "gpt-oss-120b",
      "releaseDate": "2025-08-05",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A mid-2025 model that, at its native ~4-bit (about 61GB), runs comfortably on a 128GB machine alongside the Qwen flagship. Older, but a solid open option here.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong science-reasoning figure (medium reasoning, tools enabled) from OpenAI's card.",
          "benchmark": "GPQA Diamond",
          "score": "80.81 (medium reasoning, tools enabled)",
          "source": "OpenAI gpt-oss model card (vendor)",
          "sourceUrl": "https://huggingface.co/openai/gpt-oss-120b",
          "asOf": "2025-08"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "128gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "512gb-unified",
      "nativeQuant": "BF16 (as released)",
      "model": "qwen3-8-flash-next",
      "releaseDate": "2026-08-26",
      "licence": "Qwen Community License 1.0",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE",
      "note": "The community favourite for a fast, capable general model on 128GB unified memory.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent composite index placing it well above the median of comparable models; the per-benchmark splits are not publicly broken out.",
          "benchmark": "Artificial Analysis Intelligence Index",
          "score": "40 (rank #7 of 118)",
          "source": "Artificial Analysis (independent)",
          "sourceUrl": "https://artificialanalysis.ai/models/qwen3-8-flash-next",
          "asOf": "2026-10-05"
        },
        {
          "provenance": "public-opinion",
          "assessment": "Repeatedly singled out as the standout general and agentic model for a 128GB machine.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "256gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16",
      "model": "glm-5-3-flash",
      "releaseDate": "2026-08-26",
      "licence": "MIT",
      "commercialUse": "permitted",
      "licenceUrl": "https://huggingface.co/zai-org/GLM-5.3-Flash/blob/main/LICENSE",
      "note": "Z.ai's cost-optimised frontier model. At ~4-bit (about 165GB) it fits a 256GB unified machine, not only a server.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong independent general-knowledge figure from Vals AI.",
          "benchmark": "MMLU-Pro",
          "score": "86.06% (±0.34)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/zai_glm-5.3-flash",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "512gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16",
      "model": "deepseek-v4-1-flash",
      "releaseDate": "2026-09-10",
      "licence": "MIT",
      "commercialUse": "permitted",
      "licenceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/main/LICENSE",
      "note": "DeepSeek's cost and latency-optimised frontier model. At ~4-bit (about 280GB) it fits a 512GB unified machine.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent composite index well above the median of comparable models.",
          "benchmark": "Artificial Analysis Intelligence Index",
          "score": "39 (rank #7 of 117)",
          "source": "Artificial Analysis (independent)",
          "sourceUrl": "https://artificialanalysis.ai/models/deepseek-v4-1-flash",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "512gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16",
      "model": "glm-5-3",
      "releaseDate": "2026-08-14",
      "licence": "GLM-5.3 License",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://huggingface.co/zai-org/GLM-5.3/blob/main/LICENSE",
      "note": "A current-generation frontier model with strong independent coverage. At ~4-bit (about 375GB) it runs on a 512GB Mac Studio Ultra, not only a datacentre.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "Among the highest independent general-knowledge figures of any open model on Vals AI.",
          "benchmark": "MMLU-Pro",
          "score": "86.77% (±0.34)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/zai_glm-5.3",
          "asOf": "2026-10-05"
        },
        {
          "provenance": "objective-test",
          "assessment": "An independent composite index ranking it near the top of all models.",
          "benchmark": "Artificial Analysis Intelligence Index",
          "score": "45 (rank #2 of 117)",
          "source": "Artificial Analysis (independent)",
          "sourceUrl": "https://artificialanalysis.ai/models/glm-5-3",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "512gb-unified",
      "quant": "MXFP4 (community 4-bit)",
      "tierNative": "512gb-unified",
      "nativeQuant": "MXFP4 (community 4-bit)",
      "model": "inkling",
      "releaseDate": "2026-07-15",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Thinking Machines' first open-weights model, the best-documented frontier open model. At a good 4-bit (about 490GB) it fits a 512GB machine; it can be squeezed onto 256GB only at an aggressive 1-bit quant, with real quality loss.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent science-reasoning figure from Vals AI (its own composite index is lower, weighted towards agentic and cost), corroborated by the vendor card.",
          "benchmark": "GPQA Diamond",
          "score": "87.12% (±1.78)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/thinkingmachines_inkling",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "general-chat",
      "tier": "server",
      "quant": "MXFP4 (native 4-bit)",
      "tierNative": "server",
      "nativeQuant": "MXFP4 (native 4-bit)",
      "model": "kimi-k3",
      "releaseDate": "2026-07-27",
      "licence": "Not confirmed at launch",
      "commercialUse": "unclear",
      "licenceUrl": null,
      "note": "Moonshot's frontier MoE and the one genuinely server-only general model here: it is natively 4-bit at ~1.4TB, so even quantised it needs a multi-GPU server.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent composite index placing it among the very top models.",
          "benchmark": "Artificial Analysis Intelligence Index",
          "score": "44 (rank #3 of 117)",
          "source": "Artificial Analysis (independent)",
          "sourceUrl": "https://artificialanalysis.ai/models/kimi-k3",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "8gb",
      "quant": "Q4_K_M",
      "tierNative": "24gb",
      "nativeQuant": "FP16",
      "model": "qwen2-5-coder-7b",
      "releaseDate": "2024-11-12",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": null,
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "Strong on classic code-generation benchmarks for a small model, though weaker on the harder agentic and multi-file tests.",
          "benchmark": "HumanEval+ (EvalPlus)",
          "score": "84.1%",
          "source": "Qwen2.5-Coder technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2409.12186v3",
          "asOf": "2024-11"
        },
        {
          "provenance": "public-opinion",
          "assessment": "The common small local coding pick, used for autocomplete and quick edits where only around 8GB is available.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "24gb",
      "quant": "Q5_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "devstral-small-24b",
      "releaseDate": "2025-07-25",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": null,
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "Built specifically for agentic software engineering, and its SWE-bench Verified figure reflects that rather than raw code generation. Vendor-reported, and plain SWE-bench Verified lost credibility to contamination in 2026, so read it as indicative rather than definitive.",
          "benchmark": "SWE-bench Verified",
          "score": "53.6%",
          "source": "Mistral AI model card, Devstral Small 1.1 (vendor)",
          "sourceUrl": "https://huggingface.co/mistralai/Devstral-Small-2507",
          "asOf": "2025-07"
        },
        {
          "provenance": "public-opinion",
          "assessment": "Favoured specifically for agentic coding in tools such as Cline and Aider, where tool-use behaviour matters as much as raw code quality.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen2-5-coder-32b",
      "releaseDate": "2024-11-12",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A long-standing community favourite for local coding on a 24GB card.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "Among the strongest open models on code-generation benchmarks that fit a 24GB card. Note the gap to the harder agentic tests, where it scores much lower, so read generation and agentic scores separately.",
          "benchmark": "HumanEval+ (EvalPlus)",
          "score": "87.2%",
          "source": "Qwen2.5-Coder technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2409.12186v3",
          "asOf": "2024-11"
        },
        {
          "provenance": "objective-test",
          "assessment": "Lower on LiveCodeBench, a harder contest-style benchmark, which is the honest counterweight to the high HumanEval figure.",
          "benchmark": "LiveCodeBench",
          "score": "31.4%",
          "source": "Qwen2.5-Coder technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2409.12186v3",
          "asOf": "2024-11"
        },
        {
          "provenance": "public-opinion",
          "assessment": "For a long time the default recommendation for serious local coding on a single 24GB card, still widely trusted.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "qwen3-6-35b-a3b",
      "releaseDate": "2026-04-16",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A fast current-generation mixture-of-experts that also codes well on one card.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong contest-coding figure for a sparse model that fits 24GB.",
          "benchmark": "LiveCodeBench v6",
          "score": "80.4",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
          "asOf": "2026-04"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "24gb",
      "quant": "Q5_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-8-27b",
      "releaseDate": "2026-08-13",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A general-purpose model, not a dedicated coder, but it posts top-tier coding figures and is more recent than the Qwen2.5-Coder models beside it. We found no sourced head-to-head against the dedicated coders, so we do not claim it beats them, only that it codes at a high level for a single-card model.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "The independent anchor: an unusually high SWE-bench Verified placing for a 27B model, from a third-party leaderboard rather than the vendor. Frontier models on the same board sit around 96 to 97 percent.",
          "benchmark": "SWE-bench Verified (Mini-SWE-agent)",
          "score": "86.00% (rank 15)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/benchmarks/swebench",
          "asOf": "2026-09-01"
        },
        {
          "provenance": "objective-test",
          "assessment": "Vendor self-reported in thinking mode: a very strong contest-coding figure, above the dedicated Qwen2.5-Coder generation, though a vendor claim until independently reproduced.",
          "benchmark": "LiveCodeBench v6 (thinking)",
          "score": "90.3",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-27B",
          "asOf": "2026-08"
        },
        {
          "provenance": "objective-test",
          "assessment": "Vendor self-reported on the harder, contamination-resistant SWE-bench Pro, run in the Claude Code harness. Lower than the Verified figure above, as expected for the harder benchmark.",
          "benchmark": "SWE-bench Pro",
          "score": "61.7",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-27B",
          "asOf": "2026-08"
        },
        {
          "provenance": "public-opinion",
          "assessment": "Increasingly mentioned as a general model worth reaching for to code, not only to chat, on a single 24GB card.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-coder-30b-a3b",
      "releaseDate": "2025-05-14",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": null,
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "Qwen publishes no benchmark figure for the 30B-A3B itself (only the much larger 480B sibling). This is a third-party SWE-bench Verified repro, not a vendor claim, and the widely-quoted 61.8% Aider figure belongs to the 480B, not this model.",
          "benchmark": "SWE-bench Verified (OpenHands, 100 turns)",
          "score": "51.9% Pass@1",
          "source": "Nebius SWE-rebench (third-party)",
          "sourceUrl": "https://huggingface.co/nebius/SWE-rebench-openhands-Qwen3-30B-A3B",
          "asOf": "2026-10-05"
        },
        {
          "provenance": "public-opinion",
          "assessment": "Increasingly recommended over the older 32B coder: a fast mixture-of-experts that many now reach for first on a 24GB card.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "128gb-unified",
      "quant": "Q8 (approx)",
      "tierNative": "256gb-unified",
      "nativeQuant": "BF16 (as released)",
      "model": "laguna-s-2-1",
      "releaseDate": "2026-08-24",
      "licence": "OpenMDW-1.1",
      "commercialUse": "permitted",
      "licenceUrl": "https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md",
      "note": "A notable fully-open coding model; at Q4 (~71GB) it fits a 128GB unified machine, not only a server.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "Vendor self-reported in max-thinking mode, which materially inflates the figure (60.4% without it), and with no independent cross-check yet. Treat as a vendor claim pending third-party testing.",
          "benchmark": "Terminal-Bench 2.1 (max thinking)",
          "score": "70.2% (60.4% without max thinking)",
          "source": "Poolside blog (vendor)",
          "sourceUrl": "https://poolside.ai/blog/introducing-laguna-s-2-1",
          "asOf": "2026-07-21"
        },
        {
          "provenance": "public-opinion",
          "assessment": "Of interest for being a genuinely open coding model from a commercial lab, though community reports mention occasional looping; treat as early.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "128gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "512gb-unified",
      "nativeQuant": "BF16 (as released)",
      "model": "qwen3-8-flash-next",
      "releaseDate": "2026-08-26",
      "licence": "Qwen Community License 1.0",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE",
      "note": "The 128GB standout; among the strongest coders you can run on unified memory.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A very strong contest-coding figure, vendor-reported in thinking mode.",
          "benchmark": "LiveCodeBench v6 (thinking)",
          "score": "91.9",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "512gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16",
      "model": "glm-5-3",
      "releaseDate": "2026-08-14",
      "licence": "GLM-5.3 License",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://huggingface.co/zai-org/GLM-5.3/blob/main/LICENSE",
      "note": "A current-generation frontier model with strong independent coding coverage; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A high independent contest-coding figure from Vals AI.",
          "benchmark": "LiveCodeBench",
          "score": "80.53% (±1.06)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/zai_glm-5.3",
          "asOf": "2026-10-05"
        },
        {
          "provenance": "objective-test",
          "assessment": "A very high SWE-bench placing, from Vals AI's own harness. Treat with care: it is far above typical SWE-bench Verified ranges, so read it as \"per Vals's harness\" and not comparable to other SWE-bench columns.",
          "benchmark": "SWE-bench (Vals AI harness)",
          "score": "95.40% (±0.94, rank 6/88)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/zai_glm-5.3",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "512gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16 (as released)",
      "model": "hy4-preview",
      "releaseDate": "2026-08-28",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Tencent's Apache-2.0 frontier model with a coding and agentic focus; at ~4-bit (~385GB) it fits a 512GB machine.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong figure on the harder, contamination-resistant SWE-bench Pro, vendor-reported.",
          "benchmark": "SWE-bench Pro",
          "score": "65.7",
          "source": "Tencent model card (vendor)",
          "sourceUrl": "https://huggingface.co/tencent/Hy4-preview",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "coding",
      "tier": "server",
      "quant": "MXFP4 (native 4-bit)",
      "tierNative": "server",
      "nativeQuant": "MXFP4 (native 4-bit)",
      "model": "kimi-k3",
      "releaseDate": "2026-07-27",
      "licence": "Not confirmed at launch",
      "commercialUse": "unclear",
      "licenceUrl": null,
      "note": "Moonshot's frontier MoE, strong at agentic coding.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong agentic-coding figure, vendor-reported at max reasoning via the Kimi Code harness.",
          "benchmark": "DeepSWE",
          "score": "67.5 (max reasoning)",
          "source": "Moonshot model card (vendor)",
          "sourceUrl": "https://huggingface.co/moonshotai/Kimi-K3",
          "asOf": "2026-07"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "deepseek-r1-distill-qwen-32b",
      "releaseDate": "2025-01-22",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "An older (early 2025) distilled reasoning model, still popular on one card.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong competition-maths figure for a distilled 32B.",
          "benchmark": "AIME 2024 (pass@1)",
          "score": "72.6",
          "source": "DeepSeek model card (vendor)",
          "sourceUrl": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
          "asOf": "2025-01"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "gemma-4-31b",
      "releaseDate": "2026-07-30",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Google's dense flagship, a strong reasoner at 24GB.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong competition-maths figure in thinking mode, no tools.",
          "benchmark": "AIME 2026 (no tools)",
          "score": "89.2",
          "source": "Gemma 4 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2607.02770v1",
          "asOf": "2026-07"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "qwen3-6-35b-a3b",
      "releaseDate": "2026-04-16",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A current-generation lead at 24GB for reasoning, and fast with it.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A very strong competition-maths figure for a model that fits one card.",
          "benchmark": "AIME 2026",
          "score": "92.7",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.6-35B-A3B",
          "asOf": "2026-04"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "24gb",
      "quant": "Q5_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-8-27b",
      "releaseDate": "2026-08-13",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Current-generation, and a capable reasoner as well as a top coder.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong science-reasoning figure in thinking mode.",
          "benchmark": "GPQA Diamond (thinking)",
          "score": "89.2",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-27B",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwq-32b",
      "releaseDate": "2025-03-06",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "An older (early 2025) dedicated reasoning model, still a common single-card pick.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong competition-maths figure for its day; independent testing has put its GPQA lower.",
          "benchmark": "AIME 2024",
          "score": "79.5",
          "source": "Qwen3 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2505.09388v1",
          "asOf": "2025-05"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "128gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "512gb-unified",
      "nativeQuant": "BF16 (as released)",
      "model": "qwen3-8-flash-next",
      "releaseDate": "2026-08-26",
      "licence": "Qwen Community License 1.0",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE",
      "note": "The 128GB standout, strong at reasoning as well as agentic work.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong science-reasoning figure in thinking mode.",
          "benchmark": "GPQA Diamond (thinking)",
          "score": "91.7",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "256gb-unified",
      "quant": "Dynamic 1.5-2 bit (community)",
      "tierNative": "server",
      "nativeQuant": "FP8",
      "model": "deepseek-r1",
      "releaseDate": "2025-01-22",
      "licence": "MIT",
      "commercialUse": "permitted",
      "licenceUrl": "https://opensource.org/license/mit",
      "note": "An older (early 2025) reference reasoning model that, quantised (~140GB), fits a 256GB machine; a later R1-0528 revision reports higher numbers.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "The original R1's reference reasoning figure.",
          "benchmark": "AIME 2024 (pass@1)",
          "score": "79.8%",
          "source": "DeepSeek-R1 paper (vendor)",
          "sourceUrl": "https://arxiv.org/html/2501.12948v1",
          "asOf": "2025-01"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "256gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16",
      "model": "glm-5-3-flash",
      "releaseDate": "2026-08-26",
      "licence": "MIT",
      "commercialUse": "permitted",
      "licenceUrl": "https://huggingface.co/zai-org/GLM-5.3-Flash/blob/main/LICENSE",
      "note": "Z.ai's cost-optimised frontier model; at ~4-bit (~165GB) it fits a 256GB machine.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong independent science-reasoning figure from Vals AI.",
          "benchmark": "GPQA Diamond",
          "score": "86.36% (±2.13)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/zai_glm-5.3-flash",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "512gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16",
      "model": "glm-5-3",
      "releaseDate": "2026-08-14",
      "licence": "GLM-5.3 License",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://huggingface.co/zai-org/GLM-5.3/blob/main/LICENSE",
      "note": "A current-generation frontier open model; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra, not only a datacentre.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A high independent science-reasoning figure from Vals AI.",
          "benchmark": "GPQA Diamond",
          "score": "88.13% (±1.85)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/zai_glm-5.3",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "512gb-unified",
      "quant": "MXFP4 (community 4-bit)",
      "tierNative": "512gb-unified",
      "nativeQuant": "MXFP4 (community 4-bit)",
      "model": "inkling",
      "releaseDate": "2026-07-15",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "The best-documented frontier open model; reasoning is its strong suit. At a good 4-bit (~490GB) it fits a 512GB machine.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent science-reasoning figure from Vals AI, corroborated by the vendor card.",
          "benchmark": "GPQA Diamond",
          "score": "87.12% (±1.78)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/thinkingmachines_inkling",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "reasoning",
      "tier": "server",
      "quant": "MXFP4 (native 4-bit)",
      "tierNative": "server",
      "nativeQuant": "MXFP4 (native 4-bit)",
      "model": "kimi-k3",
      "releaseDate": "2026-07-27",
      "licence": "Not confirmed at launch",
      "commercialUse": "unclear",
      "licenceUrl": null,
      "note": "Moonshot's frontier MoE; the highest science-reasoning figure of the set.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A very high figure, vendor-reported at max reasoning effort, so read it as a claim pending independent reproduction.",
          "benchmark": "GPQA Diamond",
          "score": "93.5 (max reasoning)",
          "source": "Moonshot model card (vendor)",
          "sourceUrl": "https://huggingface.co/moonshotai/Kimi-K3",
          "asOf": "2026-07"
        }
      ]
    },
    {
      "task": "creative",
      "tier": "24gb",
      "quant": "Q5_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "gemma-3-27b",
      "releaseDate": "2025-03-12",
      "licence": "Gemma Terms of Use",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://ai.google.dev/gemma/terms",
      "note": null,
      "signals": [
        {
          "provenance": "public-opinion",
          "assessment": "Still favoured by some for prose, and worth keeping in view where the newer Gemma 4 does not suit a particular style.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "creative",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "gemma-4-31b",
      "releaseDate": "2026-07-30",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Taste-led; treat the preference as opinion rather than a measured result.",
      "signals": [
        {
          "provenance": "public-opinion",
          "assessment": "The community's favourite for creative writing and prose at this size, consistently preferred for tone and natural phrasing.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "creative",
      "tier": "24gb",
      "quant": "Q5_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "mistral-small-3-2-24b",
      "releaseDate": "2025-06",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": null,
      "signals": [
        {
          "provenance": "public-opinion",
          "assessment": "Also well liked for creative tone and instruction following, a common alternative to Gemma for prose.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "rag",
      "tier": "16gb",
      "quant": "Q4_K_M",
      "tierNative": "48gb",
      "nativeQuant": "FP16",
      "model": "qwen3-14b",
      "releaseDate": "2025-04-29",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Pair with a strong embedding model such as bge-m3 or qwen3-embedding.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A long-context retrieval figure (RULER), which measures finding information in a long context rather than answer faithfulness. There is no widely published faithfulness benchmark for these sizes, so read this as a proxy.",
          "benchmark": "RULER (average, non-thinking)",
          "score": "94.6",
          "source": "Qwen3 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2505.09388v1",
          "asOf": "2025-05"
        },
        {
          "provenance": "public-opinion",
          "assessment": "A lighter RAG generator that fits around 16GB, commonly used where the retrieval does the heavy lifting and the model only has to stay faithful.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "rag",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-32b",
      "releaseDate": "2025-04-29",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "An older (2025) alternative; the generator is only half of a RAG system, the embedding and retrieval matter as much.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A long-context retrieval figure (RULER), a proxy for grounding rather than a faithfulness measure. Comparable to the 14B, so size buys you quality elsewhere more than retrieval here.",
          "benchmark": "RULER (average, non-thinking)",
          "score": "93.7",
          "source": "Qwen3 technical report (vendor)",
          "sourceUrl": "https://arxiv.org/html/2505.09388v1",
          "asOf": "2025-05"
        },
        {
          "provenance": "public-opinion",
          "assessment": "A common RAG generator choice for grounded answering on a 24GB card, where its instruction following keeps answers close to the retrieved context.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "rag",
      "tier": "24gb",
      "quant": "Q5_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-8-27b",
      "releaseDate": "2026-08-13",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "A current-generation generator with a 262k context and image input (useful for document and screenshot grounding). No faithfulness benchmark is published for it, so this is a capability-and-context steer, not a scored RAG result. Pair with a strong embedder (bge-m3, qwen3-embedding); the generator is only half of a RAG system.",
      "signals": [
        {
          "provenance": "public-opinion",
          "assessment": "A capable, current general model increasingly used as the generator in local RAG on a 24GB card, with a long context and document/image understanding.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "muse-glimmer-30b",
      "releaseDate": "2026-08-10",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Meta's purpose-built local agent (tool use, long tasks, failure recovery) that fits 24GB.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong tool-use agent figure, vendor-reported; built specifically for agentic workflows.",
          "benchmark": "MCP Atlas",
          "score": "75.5",
          "source": "Meta model page (vendor)",
          "sourceUrl": "https://dev.meta.ai/models/muse-glimmer/",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "24gb",
      "quant": "4-bit",
      "tierNative": "128gb-unified",
      "nativeQuant": "BF16",
      "model": "nemotron-3-5-lightning",
      "releaseDate": "2026-08-11",
      "licence": "OpenMDW-1.1",
      "commercialUse": "permitted",
      "licenceUrl": null,
      "note": "NVIDIA's fast execution-layer model for long-running agents, fits 24GB.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent composite index; this is an execution-speed agent model, so it trades peak capability for throughput on long agent loops.",
          "benchmark": "Artificial Analysis Intelligence Index",
          "score": "24",
          "source": "Artificial Analysis (independent)",
          "sourceUrl": "https://artificialanalysis.ai/articles/nemotron-3-5-lightning-launch",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "24gb",
      "quant": "Q4_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-32b",
      "releaseDate": "2025-04-29",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "An older (2025) alternative; dependable for agentic use within its limits.",
      "signals": [
        {
          "provenance": "public-opinion",
          "assessment": "Used for agentic workflows on a single 24GB card within its limits, where its tool use and instruction following are considered dependable for the size.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "24gb",
      "quant": "Q5_K_M",
      "tierNative": "128gb-unified",
      "nativeQuant": "FP16",
      "model": "qwen3-8-27b",
      "releaseDate": "2026-08-13",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Current-generation general model that also handles agentic terminal work.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong agentic terminal-coding figure, vendor-reported.",
          "benchmark": "Terminal-Bench 2.1 (Terminus)",
          "score": "73.0",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-27B",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "128gb-unified",
      "quant": "MXFP4 (native)",
      "tierNative": "256gb-unified",
      "nativeQuant": "BF16",
      "model": "gpt-oss-120b",
      "releaseDate": "2025-08-05",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "An older (mid-2025) alternative that, at its native ~4-bit (~61GB), runs on a 128GB machine; still popular for agentic use.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An agentic software-engineering figure. Vendor-reported, and plain SWE-bench Verified lost credibility to contamination in 2026, so read it as indicative.",
          "benchmark": "SWE-bench Verified (medium reasoning)",
          "score": "52.6",
          "source": "OpenAI gpt-oss model card (vendor)",
          "sourceUrl": "https://huggingface.co/openai/gpt-oss-120b",
          "asOf": "2025-08"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "128gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "512gb-unified",
      "nativeQuant": "BF16 (as released)",
      "model": "qwen3-8-flash-next",
      "releaseDate": "2026-08-26",
      "licence": "Qwen Community License 1.0",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next/blob/main/LICENSE",
      "note": "The community standout for agentic work on 128GB unified memory.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong tool-use figure (pass@1), vendor-reported.",
          "benchmark": "Toolathlon Verified",
          "score": "73.5",
          "source": "Qwen model card (vendor)",
          "sourceUrl": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next",
          "asOf": "2026-08"
        },
        {
          "provenance": "public-opinion",
          "assessment": "Repeatedly highlighted for agentic, coding and design workflows on a 128GB unified-memory machine, where its speed makes long tool-use loops practical.",
          "source": "Community sentiment (r/LocalLLaMA and similar)",
          "sourceUrl": "https://www.reddit.com/r/LocalLLaMA/",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "256gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16",
      "model": "glm-5-3-flash",
      "releaseDate": "2026-08-26",
      "licence": "MIT",
      "commercialUse": "permitted",
      "licenceUrl": "https://huggingface.co/zai-org/GLM-5.3-Flash/blob/main/LICENSE",
      "note": "Z.ai's cost-optimised frontier model; at ~4-bit (~165GB) it fits a 256GB machine. Independently evaluated on agentic terminal tasks.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent agentic terminal figure from Vals AI (Terminal-Bench 2.1 version).",
          "benchmark": "Terminal-Bench 2.1 (Vals AI)",
          "score": "62.92% (±2.25)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/zai_glm-5.3-flash",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "512gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16",
      "model": "glm-5-3",
      "releaseDate": "2026-08-14",
      "licence": "GLM-5.3 License",
      "commercialUse": "permitted-conditional",
      "licenceUrl": "https://huggingface.co/zai-org/GLM-5.3/blob/main/LICENSE",
      "note": "A current-generation frontier model; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra. Independently evaluated.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent agentic terminal figure from Vals AI (Terminal-Bench 4.0 version; do not compare across Terminal-Bench versions).",
          "benchmark": "Terminal-Bench 4.0 (Vals AI)",
          "score": "38.89% (±1.82)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/zai_glm-5.3",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "512gb-unified",
      "quant": "Q4 (approx)",
      "tierNative": "server",
      "nativeQuant": "BF16 (as released)",
      "model": "hy4-preview",
      "releaseDate": "2026-08-28",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "Tencent's Apache-2.0 frontier model with an agentic focus; at ~4-bit (~385GB) it fits a 512GB machine.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A strong tool-use agent figure, vendor-reported.",
          "benchmark": "Toolathlon Verified",
          "score": "74.1",
          "source": "Tencent model card (vendor)",
          "sourceUrl": "https://huggingface.co/tencent/Hy4-preview",
          "asOf": "2026-08"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "512gb-unified",
      "quant": "MXFP4 (community 4-bit)",
      "tierNative": "512gb-unified",
      "nativeQuant": "MXFP4 (community 4-bit)",
      "model": "inkling",
      "releaseDate": "2026-07-15",
      "licence": "Apache 2.0",
      "commercialUse": "permitted",
      "licenceUrl": "https://www.apache.org/licenses/LICENSE-2.0",
      "note": "The best-documented frontier open model; a capable agent too. At a good 4-bit (~490GB) it fits a 512GB machine.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "An independent agentic software-engineering figure from Vals AI.",
          "benchmark": "SWE-bench (Vals AI)",
          "score": "77.60% (±1.87)",
          "source": "Vals AI (independent)",
          "sourceUrl": "https://www.vals.ai/models/thinkingmachines_inkling",
          "asOf": "2026-10-05"
        }
      ]
    },
    {
      "task": "agentic",
      "tier": "server",
      "quant": "MXFP4 (native 4-bit)",
      "tierNative": "server",
      "nativeQuant": "MXFP4 (native 4-bit)",
      "model": "kimi-k3",
      "releaseDate": "2026-07-27",
      "licence": "Not confirmed at launch",
      "commercialUse": "unclear",
      "licenceUrl": null,
      "note": "Moonshot's frontier MoE, strong at agentic terminal work.",
      "signals": [
        {
          "provenance": "objective-test",
          "assessment": "A very high agentic terminal figure, vendor-reported at max reasoning (Terminal-Bench 2.1 version).",
          "benchmark": "Terminal-Bench 2.1",
          "score": "88.3 (max reasoning)",
          "source": "Moonshot model card (vendor)",
          "sourceUrl": "https://huggingface.co/moonshotai/Kimi-K3",
          "asOf": "2026-07"
        }
      ]
    }
  ]
}