MiniMax-M2.5
MiniMaxAI
Highest third-party SWE-bench Verified score among the 14 coding models measured there: 75.8% (swebench.com, 2026-10-25).
Sources: swebench.com 2026-10-25 · openrouter.ai 2026-10-24 · estimate method 2026-10-25
Up to date
Use cases › Doing things
Write, fix and review code with an agent in the loop.
Showing every model that fits the job; narrow it with the constraints below.
14 models with a third-party SWE-bench Verified score and a live price. The dashed teal line is the frontier: nothing scores higher for less.
Each pick is the highest third-party SWE-bench Verified score that meets its constraint; no model appears twice. Author-reported scores stay in the table below.
MiniMaxAI
Highest third-party SWE-bench Verified score among the 14 coding models measured there: 75.8% (swebench.com, 2026-10-25).
Sources: swebench.com 2026-10-25 · openrouter.ai 2026-10-24 · estimate method 2026-10-25
Qwen
Highest SWE-bench Verified score at $0.10 or less per 1M input tokens: 60.4% at $0.070 (SiliconFlow, 2026-10-24).
Sources: swebench.com 2026-10-25 · openrouter.ai 2026-10-24 · estimate method 2026-10-25
Qwen
Highest SWE-bench Verified score among models whose INT4 weights fit one 24 GB GPU: 47% at about 20 GB (estimate).
Sources: swebench.com 2026-10-25 · huggingface.co 2026-10-25 · estimate method 2026-10-25
zai-org
Highest SWE-bench Verified score under a permissive license (MIT): 72.8% (swebench.com, 2026-10-25).
Sources: swebench.com 2026-10-25 · huggingface.co 2026-10-25 · estimate method 2026-10-25
How people wire a model into this job. Notes are written by hand from the cited cards and docs; the model links go to the facts.
The first ten models with a coding score, in the scoreboard's order for this job. The full list, sortable, lives in the scoreboard with these constraints.
| # | Evidence | ||||||
|---|---|---|---|---|---|---|---|
| 1 | Kimi-K2.6moonshotai | 80.2 | — | $0.45 | 18 | ~616 GB | |
| 2 | Inkling-Smallthinkingmachines | 80.2 | — | $0.45 | 2 | ~160 GB | |
| 3 | Inklingthinkingmachines | 77.6 | — | $0.95 | 4 | ~571 GB | |
| 4 | Qwen3.6-27BQwen | 77.2 | — | $0.20 | 9 | ~17 GB | |
| 5 | Muse-Glimmer-30Bmeta-models | 76 | — | $0.30 | 3 | ~18 GB | |
| 6 | MiniMax-M2.5MiniMaxAI | 75.8 | — | $0.27 | 8 | ~137 GB | |
| 7 | Step-3.5-Flashstepfun-ai | 74.4 | — | $0.10 | 2 | ~120 GB | |
| 8 | MiniMax-M2.1MiniMaxAI | 74 | — | $0.30 | 2 | ~137 GB | |
| 9 | GLM-4.7zai-org | 73.8 | — | $0.32 | 7 | ~215 GB | |
| 10 | Qwen3.6-35B-A3BQwen | 73.4 | — | $0.050 | 12 | ~22 GB |
None of them measure how a model behaves in your own repository and harness. Scores depend on the agent scaffold and its settings, so compare within one benchmark, never across two.
Nearest scoreboard view: Coding agents. Follow changes to this job's evidence: RSS.