Model recommendations per task and hardware

The current-generation lead at 8GB, a 2026 Qwen3.5 multimodal model, Apache 2.0.

• Objective test A strong general-knowledge figure for a 4B model, vendor-reported, and well above the older small models at this size. MMLU-Pro — 79.1 · Qwen model card (vendor) · March 2026
• Objective test 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. Artificial Analysis Intelligence Index — 27 · Artificial Analysis (independent) · March 2026

An older (2025) alternative at 8GB, still a dependable all-rounder.

• Objective test A reasoning-and-knowledge figure (thinking mode) standing in as a general capability proxy; strong for its size. GPQA Diamond (thinking) — 62.0 · Qwen3 technical report (vendor) · May 2025
• Public opinion Widely recommended as a dependable all-round model that fits an 8GB card. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

The current-generation lead at 16GB.

• Objective test A strong general-knowledge figure for a model that fits around 16GB. MMLU-Pro (thinking) — 82.6 · Gemma 4 technical report (vendor) · April 2026

A current-generation Qwen3.5 multimodal model, Apache 2.0; fits 16GB at Q8 (or 8GB at Q4).

• Objective test A strong general-knowledge figure for a 9B, vendor-reported, which Qwen puts level with some much larger older models. MMLU-Pro — 82.5 · Qwen model card (vendor) · March 2026
• Objective test An independent composite index as a cross-check. Artificial Analysis Intelligence Index — 32 · Artificial Analysis (independent) · March 2026

An older (2025) alternative, still a dependable 16GB general model.

• Objective test A reasoning-and-knowledge proxy, a clear step up from the 8B. GPQA Diamond (thinking) — 64.0 · Qwen3 technical report (vendor) · May 2025

A current-generation lead at 24GB, and a top coder too (see the coding list).

• Objective test A strong science-reasoning figure for a single-card general model. GPQA Diamond (thinking) — 89.2 · Qwen model card (vendor) · August 2026

Google's dense flagship; the top open model in its dense category by Arena.

• Objective test A strong general-knowledge figure; the model also tops the dense open category on human-preference Arena. MMLU-Pro (thinking) — 85.2 · Gemma 4 technical report (vendor) · July 2026
• Objective test A human-preference figure, vendor-reported via Arena; style-controlled Arena is preference, not correctness, so read it as "people liked its answers". LMArena Elo — 1451 (top dense open model) · Gemma 4 technical report, citing LMArena (vendor-reported) · July 2026

A fast current-generation mixture-of-experts that fits 24GB.

• Objective test A strong general-knowledge figure for a sparse model you can run on one card. GPQA — 86.0 · Qwen model card (vendor) · April 2026

An older (2025) alternative, still solid and doubling as a reasoning model.

• Objective test A reasoning-and-knowledge proxy; dependable, a generation behind the leads above. GPQA Diamond (thinking) — 68.4 · Qwen3 technical report (vendor) · May 2025

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.

• Objective test A strong science-reasoning figure, run here at near-lossless Q8. GPQA Diamond (thinking) — 89.2 · Qwen model card (vendor) · August 2026

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.

• Objective test A strong general-knowledge figure, run here at near-lossless Q8. MMLU-Pro (thinking) — 85.2 · Gemma 4 technical report (vendor) · July 2026

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.

• Objective test A respectable general-knowledge figure for its day, now behind current models. MMLU-Pro (CoT) — 68.9 · Meta model card (vendor) · December 2024

The community favourite for a fast, capable general model on 128GB unified memory.

• Objective test An independent composite index placing it well above the median of comparable models; the per-benchmark splits are not publicly broken out. Artificial Analysis Intelligence Index — 40 (rank #7 of 118) · Artificial Analysis (independent) · 5 October 2026
• Public opinion Repeatedly singled out as the standout general and agentic model for a 128GB machine. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

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.

• Objective test A strong science-reasoning figure (medium reasoning, tools enabled) from OpenAI's card. GPQA Diamond — 80.81 (medium reasoning, tools enabled) · OpenAI gpt-oss model card (vendor) · August 2025

Z.ai's cost-optimised frontier model. At ~4-bit (about 165GB) it fits a 256GB unified machine, not only a server.

• Objective test A strong independent general-knowledge figure from Vals AI. MMLU-Pro — 86.06% (±0.34) · Vals AI (independent) · 5 October 2026

DeepSeek's cost and latency-optimised frontier model. At ~4-bit (about 280GB) it fits a 512GB unified machine.

• Objective test An independent composite index well above the median of comparable models. Artificial Analysis Intelligence Index — 39 (rank #7 of 117) · Artificial Analysis (independent) · 5 October 2026

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.

• Objective test Among the highest independent general-knowledge figures of any open model on Vals AI. MMLU-Pro — 86.77% (±0.34) · Vals AI (independent) · 5 October 2026
• Objective test An independent composite index ranking it near the top of all models. Artificial Analysis Intelligence Index — 45 (rank #2 of 117) · Artificial Analysis (independent) · 5 October 2026

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.

• Objective test An independent science-reasoning figure from Vals AI (its own composite index is lower, weighted towards agentic and cost), corroborated by the vendor card. GPQA Diamond — 87.12% (±1.78) · Vals AI (independent) · 5 October 2026

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.

• Objective test An independent composite index placing it among the very top models. Artificial Analysis Intelligence Index — 44 (rank #3 of 117) · Artificial Analysis (independent) · 5 October 2026

The current-generation lead at 8GB, a 2026 Qwen3.5 multimodal model, Apache 2.0.

• Objective test A strong general-knowledge figure for a 4B model, vendor-reported, and well above the older small models at this size. MMLU-Pro — 79.1 · Qwen model card (vendor) · March 2026
• Objective test 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. Artificial Analysis Intelligence Index — 27 · Artificial Analysis (independent) · March 2026

A current-generation Qwen3.5 multimodal model, Apache 2.0; fits 16GB at Q8 (or 8GB at Q4).

• Objective test A strong general-knowledge figure for a 9B, vendor-reported, which Qwen puts level with some much larger older models. MMLU-Pro — 82.5 · Qwen model card (vendor) · March 2026
• Objective test An independent composite index as a cross-check. Artificial Analysis Intelligence Index — 32 · Artificial Analysis (independent) · March 2026

An older (2025) alternative at 8GB, still a dependable all-rounder.

• Objective test A reasoning-and-knowledge figure (thinking mode) standing in as a general capability proxy; strong for its size. GPQA Diamond (thinking) — 62.0 · Qwen3 technical report (vendor) · May 2025
• Public opinion Widely recommended as a dependable all-round model that fits an 8GB card. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

An older (2025) alternative, still a dependable 16GB general model.

• Objective test A reasoning-and-knowledge proxy, a clear step up from the 8B. GPQA Diamond (thinking) — 64.0 · Qwen3 technical report (vendor) · May 2025

A current-generation lead at 24GB, and a top coder too (see the coding list).

• Objective test A strong science-reasoning figure for a single-card general model. GPQA Diamond (thinking) — 89.2 · Qwen model card (vendor) · August 2026

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.

• Objective test A strong science-reasoning figure, run here at near-lossless Q8. GPQA Diamond (thinking) — 89.2 · Qwen model card (vendor) · August 2026

Google's dense flagship; the top open model in its dense category by Arena.

• Objective test A strong general-knowledge figure; the model also tops the dense open category on human-preference Arena. MMLU-Pro (thinking) — 85.2 · Gemma 4 technical report (vendor) · July 2026
• Objective test A human-preference figure, vendor-reported via Arena; style-controlled Arena is preference, not correctness, so read it as "people liked its answers". LMArena Elo — 1451 (top dense open model) · Gemma 4 technical report, citing LMArena (vendor-reported) · July 2026

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.

• Objective test A strong general-knowledge figure, run here at near-lossless Q8. MMLU-Pro (thinking) — 85.2 · Gemma 4 technical report (vendor) · July 2026

A fast current-generation mixture-of-experts that fits 24GB.

• Objective test A strong general-knowledge figure for a sparse model you can run on one card. GPQA — 86.0 · Qwen model card (vendor) · April 2026

The current-generation lead at 16GB.

• Objective test A strong general-knowledge figure for a model that fits around 16GB. MMLU-Pro (thinking) — 82.6 · Gemma 4 technical report (vendor) · April 2026

An older (2025) alternative, still solid and doubling as a reasoning model.

• Objective test A reasoning-and-knowledge proxy; dependable, a generation behind the leads above. GPQA Diamond (thinking) — 68.4 · Qwen3 technical report (vendor) · May 2025

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.

• Objective test A strong science-reasoning figure (medium reasoning, tools enabled) from OpenAI's card. GPQA Diamond — 80.81 (medium reasoning, tools enabled) · OpenAI gpt-oss model card (vendor) · August 2025

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.

• Objective test A respectable general-knowledge figure for its day, now behind current models. MMLU-Pro (CoT) — 68.9 · Meta model card (vendor) · December 2024

The community favourite for a fast, capable general model on 128GB unified memory.

• Objective test An independent composite index placing it well above the median of comparable models; the per-benchmark splits are not publicly broken out. Artificial Analysis Intelligence Index — 40 (rank #7 of 118) · Artificial Analysis (independent) · 5 October 2026
• Public opinion Repeatedly singled out as the standout general and agentic model for a 128GB machine. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

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.

• Objective test An independent science-reasoning figure from Vals AI (its own composite index is lower, weighted towards agentic and cost), corroborated by the vendor card. GPQA Diamond — 87.12% (±1.78) · Vals AI (independent) · 5 October 2026

DeepSeek's cost and latency-optimised frontier model. At ~4-bit (about 280GB) it fits a 512GB unified machine.

• Objective test An independent composite index well above the median of comparable models. Artificial Analysis Intelligence Index — 39 (rank #7 of 117) · Artificial Analysis (independent) · 5 October 2026

Z.ai's cost-optimised frontier model. At ~4-bit (about 165GB) it fits a 256GB unified machine, not only a server.

• Objective test A strong independent general-knowledge figure from Vals AI. MMLU-Pro — 86.06% (±0.34) · Vals AI (independent) · 5 October 2026

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.

• Objective test Among the highest independent general-knowledge figures of any open model on Vals AI. MMLU-Pro — 86.77% (±0.34) · Vals AI (independent) · 5 October 2026
• Objective test An independent composite index ranking it near the top of all models. Artificial Analysis Intelligence Index — 45 (rank #2 of 117) · Artificial Analysis (independent) · 5 October 2026

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.

• Objective test An independent composite index placing it among the very top models. Artificial Analysis Intelligence Index — 44 (rank #3 of 117) · Artificial Analysis (independent) · 5 October 2026

• Objective test Strong on classic code-generation benchmarks for a small model, though weaker on the harder agentic and multi-file tests. HumanEval+ (EvalPlus) — 84.1% · Qwen2.5-Coder technical report (vendor) · November 2024
• Public opinion The common small local coding pick, used for autocomplete and quick edits where only around 8GB is available. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

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.

• Objective test 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. SWE-bench Verified (Mini-SWE-agent) — 86.00% (rank 15) · Vals AI (independent) · 1 September 2026
• Objective test 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. LiveCodeBench v6 (thinking) — 90.3 · Qwen model card (vendor) · August 2026
• Objective test 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. SWE-bench Pro — 61.7 · Qwen model card (vendor) · August 2026
• Public opinion Increasingly mentioned as a general model worth reaching for to code, not only to chat, on a single 24GB card. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

A fast current-generation mixture-of-experts that also codes well on one card.

• Objective test A strong contest-coding figure for a sparse model that fits 24GB. LiveCodeBench v6 — 80.4 · Qwen model card (vendor) · April 2026

• Objective test 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. SWE-bench Verified — 53.6% · Mistral AI model card, Devstral Small 1.1 (vendor) · July 2025
• Public opinion Favoured specifically for agentic coding in tools such as Cline and Aider, where tool-use behaviour matters as much as raw code quality. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

• Objective test 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. SWE-bench Verified (OpenHands, 100 turns) — 51.9% Pass@1 · Nebius SWE-rebench (third-party) · 5 October 2026
• Public opinion Increasingly recommended over the older 32B coder: a fast mixture-of-experts that many now reach for first on a 24GB card. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

A long-standing community favourite for local coding on a 24GB card.

• Objective test 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. HumanEval+ (EvalPlus) — 87.2% · Qwen2.5-Coder technical report (vendor) · November 2024
• Objective test Lower on LiveCodeBench, a harder contest-style benchmark, which is the honest counterweight to the high HumanEval figure. LiveCodeBench — 31.4% · Qwen2.5-Coder technical report (vendor) · November 2024
• Public opinion For a long time the default recommendation for serious local coding on a single 24GB card, still widely trusted. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

The 128GB standout; among the strongest coders you can run on unified memory.

• Objective test A very strong contest-coding figure, vendor-reported in thinking mode. LiveCodeBench v6 (thinking) — 91.9 · Qwen model card (vendor) · August 2026

A notable fully-open coding model; at Q4 (~71GB) it fits a 128GB unified machine, not only a server.

• Objective test 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. Terminal-Bench 2.1 (max thinking) — 70.2% (60.4% without max thinking) · Poolside blog (vendor) · 21 July 2026
• Public opinion Of interest for being a genuinely open coding model from a commercial lab, though community reports mention occasional looping; treat as early. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

Tencent's Apache-2.0 frontier model with a coding and agentic focus; at ~4-bit (~385GB) it fits a 512GB machine.

• Objective test A strong figure on the harder, contamination-resistant SWE-bench Pro, vendor-reported. SWE-bench Pro — 65.7 · Tencent model card (vendor) · August 2026

A current-generation frontier model with strong independent coding coverage; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra.

• Objective test A high independent contest-coding figure from Vals AI. LiveCodeBench — 80.53% (±1.06) · Vals AI (independent) · 5 October 2026
• Objective test 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. SWE-bench (Vals AI harness) — 95.40% (±0.94, rank 6/88) · Vals AI (independent) · 5 October 2026

Moonshot's frontier MoE, strong at agentic coding.

• Objective test A strong agentic-coding figure, vendor-reported at max reasoning via the Kimi Code harness. DeepSWE — 67.5 (max reasoning) · Moonshot model card (vendor) · July 2026

• Objective test Strong on classic code-generation benchmarks for a small model, though weaker on the harder agentic and multi-file tests. HumanEval+ (EvalPlus) — 84.1% · Qwen2.5-Coder technical report (vendor) · November 2024
• Public opinion The common small local coding pick, used for autocomplete and quick edits where only around 8GB is available. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

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.

• Objective test 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. SWE-bench Verified (Mini-SWE-agent) — 86.00% (rank 15) · Vals AI (independent) · 1 September 2026
• Objective test 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. LiveCodeBench v6 (thinking) — 90.3 · Qwen model card (vendor) · August 2026
• Objective test 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. SWE-bench Pro — 61.7 · Qwen model card (vendor) · August 2026
• Public opinion Increasingly mentioned as a general model worth reaching for to code, not only to chat, on a single 24GB card. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

A fast current-generation mixture-of-experts that also codes well on one card.

• Objective test A strong contest-coding figure for a sparse model that fits 24GB. LiveCodeBench v6 — 80.4 · Qwen model card (vendor) · April 2026

• Objective test 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. SWE-bench Verified — 53.6% · Mistral AI model card, Devstral Small 1.1 (vendor) · July 2025
• Public opinion Favoured specifically for agentic coding in tools such as Cline and Aider, where tool-use behaviour matters as much as raw code quality. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

• Objective test 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. SWE-bench Verified (OpenHands, 100 turns) — 51.9% Pass@1 · Nebius SWE-rebench (third-party) · 5 October 2026
• Public opinion Increasingly recommended over the older 32B coder: a fast mixture-of-experts that many now reach for first on a 24GB card. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

A long-standing community favourite for local coding on a 24GB card.

• Objective test 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. HumanEval+ (EvalPlus) — 87.2% · Qwen2.5-Coder technical report (vendor) · November 2024
• Objective test Lower on LiveCodeBench, a harder contest-style benchmark, which is the honest counterweight to the high HumanEval figure. LiveCodeBench — 31.4% · Qwen2.5-Coder technical report (vendor) · November 2024
• Public opinion For a long time the default recommendation for serious local coding on a single 24GB card, still widely trusted. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

A notable fully-open coding model; at Q4 (~71GB) it fits a 128GB unified machine, not only a server.

• Objective test 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. Terminal-Bench 2.1 (max thinking) — 70.2% (60.4% without max thinking) · Poolside blog (vendor) · 21 July 2026
• Public opinion Of interest for being a genuinely open coding model from a commercial lab, though community reports mention occasional looping; treat as early. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

The 128GB standout; among the strongest coders you can run on unified memory.

• Objective test A very strong contest-coding figure, vendor-reported in thinking mode. LiveCodeBench v6 (thinking) — 91.9 · Qwen model card (vendor) · August 2026

Tencent's Apache-2.0 frontier model with a coding and agentic focus; at ~4-bit (~385GB) it fits a 512GB machine.

• Objective test A strong figure on the harder, contamination-resistant SWE-bench Pro, vendor-reported. SWE-bench Pro — 65.7 · Tencent model card (vendor) · August 2026

A current-generation frontier model with strong independent coding coverage; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra.

• Objective test A high independent contest-coding figure from Vals AI. LiveCodeBench — 80.53% (±1.06) · Vals AI (independent) · 5 October 2026
• Objective test 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. SWE-bench (Vals AI harness) — 95.40% (±0.94, rank 6/88) · Vals AI (independent) · 5 October 2026

Moonshot's frontier MoE, strong at agentic coding.

• Objective test A strong agentic-coding figure, vendor-reported at max reasoning via the Kimi Code harness. DeepSWE — 67.5 (max reasoning) · Moonshot model card (vendor) · July 2026

Current-generation, and a capable reasoner as well as a top coder.

• Objective test A strong science-reasoning figure in thinking mode. GPQA Diamond (thinking) — 89.2 · Qwen model card (vendor) · August 2026

Google's dense flagship, a strong reasoner at 24GB.

• Objective test A strong competition-maths figure in thinking mode, no tools. AIME 2026 (no tools) — 89.2 · Gemma 4 technical report (vendor) · July 2026

A current-generation lead at 24GB for reasoning, and fast with it.

• Objective test A very strong competition-maths figure for a model that fits one card. AIME 2026 — 92.7 · Qwen model card (vendor) · April 2026

An older (early 2025) dedicated reasoning model, still a common single-card pick.

• Objective test A strong competition-maths figure for its day; independent testing has put its GPQA lower. AIME 2024 — 79.5 · Qwen3 technical report (vendor) · May 2025

An older (early 2025) distilled reasoning model, still popular on one card.

• Objective test A strong competition-maths figure for a distilled 32B. AIME 2024 (pass@1) — 72.6 · DeepSeek model card (vendor) · January 2025

The 128GB standout, strong at reasoning as well as agentic work.

• Objective test A strong science-reasoning figure in thinking mode. GPQA Diamond (thinking) — 91.7 · Qwen model card (vendor) · August 2026

Z.ai's cost-optimised frontier model; at ~4-bit (~165GB) it fits a 256GB machine.

• Objective test A strong independent science-reasoning figure from Vals AI. GPQA Diamond — 86.36% (±2.13) · Vals AI (independent) · 5 October 2026

An older (early 2025) reference reasoning model that, quantised (~140GB), fits a 256GB machine; a later R1-0528 revision reports higher numbers.

• Objective test The original R1's reference reasoning figure. AIME 2024 (pass@1) — 79.8% · DeepSeek-R1 paper (vendor) · January 2025

A current-generation frontier open model; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra, not only a datacentre.

• Objective test A high independent science-reasoning figure from Vals AI. GPQA Diamond — 88.13% (±1.85) · Vals AI (independent) · 5 October 2026

The best-documented frontier open model; reasoning is its strong suit. At a good 4-bit (~490GB) it fits a 512GB machine.

• Objective test An independent science-reasoning figure from Vals AI, corroborated by the vendor card. GPQA Diamond — 87.12% (±1.78) · Vals AI (independent) · 5 October 2026

Moonshot's frontier MoE; the highest science-reasoning figure of the set.

• Objective test A very high figure, vendor-reported at max reasoning effort, so read it as a claim pending independent reproduction. GPQA Diamond — 93.5 (max reasoning) · Moonshot model card (vendor) · July 2026

Current-generation, and a capable reasoner as well as a top coder.

• Objective test A strong science-reasoning figure in thinking mode. GPQA Diamond (thinking) — 89.2 · Qwen model card (vendor) · August 2026

Google's dense flagship, a strong reasoner at 24GB.

• Objective test A strong competition-maths figure in thinking mode, no tools. AIME 2026 (no tools) — 89.2 · Gemma 4 technical report (vendor) · July 2026

A current-generation lead at 24GB for reasoning, and fast with it.

• Objective test A very strong competition-maths figure for a model that fits one card. AIME 2026 — 92.7 · Qwen model card (vendor) · April 2026

An older (early 2025) dedicated reasoning model, still a common single-card pick.

• Objective test A strong competition-maths figure for its day; independent testing has put its GPQA lower. AIME 2024 — 79.5 · Qwen3 technical report (vendor) · May 2025

An older (early 2025) distilled reasoning model, still popular on one card.

• Objective test A strong competition-maths figure for a distilled 32B. AIME 2024 (pass@1) — 72.6 · DeepSeek model card (vendor) · January 2025

The 128GB standout, strong at reasoning as well as agentic work.

• Objective test A strong science-reasoning figure in thinking mode. GPQA Diamond (thinking) — 91.7 · Qwen model card (vendor) · August 2026

The best-documented frontier open model; reasoning is its strong suit. At a good 4-bit (~490GB) it fits a 512GB machine.

• Objective test An independent science-reasoning figure from Vals AI, corroborated by the vendor card. GPQA Diamond — 87.12% (±1.78) · Vals AI (independent) · 5 October 2026

Z.ai's cost-optimised frontier model; at ~4-bit (~165GB) it fits a 256GB machine.

• Objective test A strong independent science-reasoning figure from Vals AI. GPQA Diamond — 86.36% (±2.13) · Vals AI (independent) · 5 October 2026

A current-generation frontier open model; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra, not only a datacentre.

• Objective test A high independent science-reasoning figure from Vals AI. GPQA Diamond — 88.13% (±1.85) · Vals AI (independent) · 5 October 2026

Moonshot's frontier MoE; the highest science-reasoning figure of the set.

• Objective test A very high figure, vendor-reported at max reasoning effort, so read it as a claim pending independent reproduction. GPQA Diamond — 93.5 (max reasoning) · Moonshot model card (vendor) · July 2026

An older (early 2025) reference reasoning model that, quantised (~140GB), fits a 256GB machine; a later R1-0528 revision reports higher numbers.

• Objective test The original R1's reference reasoning figure. AIME 2024 (pass@1) — 79.8% · DeepSeek-R1 paper (vendor) · January 2025

Taste-led; treat the preference as opinion rather than a measured result.

• Public opinion The community's favourite for creative writing and prose at this size, consistently preferred for tone and natural phrasing. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

• Public opinion Also well liked for creative tone and instruction following, a common alternative to Gemma for prose. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

• Public opinion Still favoured by some for prose, and worth keeping in view where the newer Gemma 4 does not suit a particular style. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

Taste-led; treat the preference as opinion rather than a measured result.

• Public opinion The community's favourite for creative writing and prose at this size, consistently preferred for tone and natural phrasing. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

• Public opinion Also well liked for creative tone and instruction following, a common alternative to Gemma for prose. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

• Public opinion Still favoured by some for prose, and worth keeping in view where the newer Gemma 4 does not suit a particular style. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

Pair with a strong embedding model such as bge-m3 or qwen3-embedding.

• Objective test 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. RULER (average, non-thinking) — 94.6 · Qwen3 technical report (vendor) · May 2025
• Public opinion 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. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

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.

• Public opinion 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. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

An older (2025) alternative; the generator is only half of a RAG system, the embedding and retrieval matter as much.

• Objective test 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. RULER (average, non-thinking) — 93.7 · Qwen3 technical report (vendor) · May 2025
• Public opinion A common RAG generator choice for grounded answering on a 24GB card, where its instruction following keeps answers close to the retrieved context. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

Pair with a strong embedding model such as bge-m3 or qwen3-embedding.

• Objective test 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. RULER (average, non-thinking) — 94.6 · Qwen3 technical report (vendor) · May 2025
• Public opinion 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. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

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.

• Public opinion 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. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

An older (2025) alternative; the generator is only half of a RAG system, the embedding and retrieval matter as much.

• Objective test 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. RULER (average, non-thinking) — 93.7 · Qwen3 technical report (vendor) · May 2025
• Public opinion A common RAG generator choice for grounded answering on a 24GB card, where its instruction following keeps answers close to the retrieved context. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

Current-generation general model that also handles agentic terminal work.

• Objective test A strong agentic terminal-coding figure, vendor-reported. Terminal-Bench 2.1 (Terminus) — 73.0 · Qwen model card (vendor) · August 2026

NVIDIA's fast execution-layer model for long-running agents, fits 24GB.

• Objective test An independent composite index; this is an execution-speed agent model, so it trades peak capability for throughput on long agent loops. Artificial Analysis Intelligence Index — 24 · Artificial Analysis (independent) · 5 October 2026

Meta's purpose-built local agent (tool use, long tasks, failure recovery) that fits 24GB.

• Objective test A strong tool-use agent figure, vendor-reported; built specifically for agentic workflows. MCP Atlas — 75.5 · Meta model page (vendor) · August 2026

An older (2025) alternative; dependable for agentic use within its limits.

• Public opinion 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. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

The community standout for agentic work on 128GB unified memory.

• Objective test A strong tool-use figure (pass@1), vendor-reported. Toolathlon Verified — 73.5 · Qwen model card (vendor) · August 2026
• Public opinion Repeatedly highlighted for agentic, coding and design workflows on a 128GB unified-memory machine, where its speed makes long tool-use loops practical. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

An older (mid-2025) alternative that, at its native ~4-bit (~61GB), runs on a 128GB machine; still popular for agentic use.

• Objective test An agentic software-engineering figure. Vendor-reported, and plain SWE-bench Verified lost credibility to contamination in 2026, so read it as indicative. SWE-bench Verified (medium reasoning) — 52.6 · OpenAI gpt-oss model card (vendor) · August 2025

Z.ai's cost-optimised frontier model; at ~4-bit (~165GB) it fits a 256GB machine. Independently evaluated on agentic terminal tasks.

• Objective test An independent agentic terminal figure from Vals AI (Terminal-Bench 2.1 version). Terminal-Bench 2.1 (Vals AI) — 62.92% (±2.25) · Vals AI (independent) · 5 October 2026

Tencent's Apache-2.0 frontier model with an agentic focus; at ~4-bit (~385GB) it fits a 512GB machine.

• Objective test A strong tool-use agent figure, vendor-reported. Toolathlon Verified — 74.1 · Tencent model card (vendor) · August 2026

A current-generation frontier model; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra. Independently evaluated.

• Objective test An independent agentic terminal figure from Vals AI (Terminal-Bench 4.0 version; do not compare across Terminal-Bench versions). Terminal-Bench 4.0 (Vals AI) — 38.89% (±1.82) · Vals AI (independent) · 5 October 2026

The best-documented frontier open model; a capable agent too. At a good 4-bit (~490GB) it fits a 512GB machine.

• Objective test An independent agentic software-engineering figure from Vals AI. SWE-bench (Vals AI) — 77.60% (±1.87) · Vals AI (independent) · 5 October 2026

Moonshot's frontier MoE, strong at agentic terminal work.

• Objective test A very high agentic terminal figure, vendor-reported at max reasoning (Terminal-Bench 2.1 version). Terminal-Bench 2.1 — 88.3 (max reasoning) · Moonshot model card (vendor) · July 2026

Current-generation general model that also handles agentic terminal work.

• Objective test A strong agentic terminal-coding figure, vendor-reported. Terminal-Bench 2.1 (Terminus) — 73.0 · Qwen model card (vendor) · August 2026

NVIDIA's fast execution-layer model for long-running agents, fits 24GB.

• Objective test An independent composite index; this is an execution-speed agent model, so it trades peak capability for throughput on long agent loops. Artificial Analysis Intelligence Index — 24 · Artificial Analysis (independent) · 5 October 2026

Meta's purpose-built local agent (tool use, long tasks, failure recovery) that fits 24GB.

• Objective test A strong tool-use agent figure, vendor-reported; built specifically for agentic workflows. MCP Atlas — 75.5 · Meta model page (vendor) · August 2026

An older (2025) alternative; dependable for agentic use within its limits.

• Public opinion 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. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

An older (mid-2025) alternative that, at its native ~4-bit (~61GB), runs on a 128GB machine; still popular for agentic use.

• Objective test An agentic software-engineering figure. Vendor-reported, and plain SWE-bench Verified lost credibility to contamination in 2026, so read it as indicative. SWE-bench Verified (medium reasoning) — 52.6 · OpenAI gpt-oss model card (vendor) · August 2025

The community standout for agentic work on 128GB unified memory.

• Objective test A strong tool-use figure (pass@1), vendor-reported. Toolathlon Verified — 73.5 · Qwen model card (vendor) · August 2026
• Public opinion Repeatedly highlighted for agentic, coding and design workflows on a 128GB unified-memory machine, where its speed makes long tool-use loops practical. Community sentiment (r/LocalLLaMA and similar) · 5 October 2026

The best-documented frontier open model; a capable agent too. At a good 4-bit (~490GB) it fits a 512GB machine.

• Objective test An independent agentic software-engineering figure from Vals AI. SWE-bench (Vals AI) — 77.60% (±1.87) · Vals AI (independent) · 5 October 2026

Tencent's Apache-2.0 frontier model with an agentic focus; at ~4-bit (~385GB) it fits a 512GB machine.

• Objective test A strong tool-use agent figure, vendor-reported. Toolathlon Verified — 74.1 · Tencent model card (vendor) · August 2026

Z.ai's cost-optimised frontier model; at ~4-bit (~165GB) it fits a 256GB machine. Independently evaluated on agentic terminal tasks.

• Objective test An independent agentic terminal figure from Vals AI (Terminal-Bench 2.1 version). Terminal-Bench 2.1 (Vals AI) — 62.92% (±2.25) · Vals AI (independent) · 5 October 2026

A current-generation frontier model; at ~4-bit (~375GB) it fits a 512GB Mac Studio Ultra. Independently evaluated.

• Objective test An independent agentic terminal figure from Vals AI (Terminal-Bench 4.0 version; do not compare across Terminal-Bench versions). Terminal-Bench 4.0 (Vals AI) — 38.89% (±1.82) · Vals AI (independent) · 5 October 2026

Moonshot's frontier MoE, strong at agentic terminal work.

• Objective test A very high agentic terminal figure, vendor-reported at max reasoning (Terminal-Bench 2.1 version). Terminal-Bench 2.1 — 88.3 (max reasoning) · Moonshot model card (vendor) · July 2026