Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiniMax M2.7
~66
1/8 categoriesQwen3.5-122B-A10B
71
Winner · 1/8 categoriesMiniMax M2.7· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if agentic is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-122B-A10B's sharpest advantage is in coding, where it averages 76.3 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 57% to 49.4%. MiniMax M2.7 does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-122B-A10B. That is roughly Infinityx on output cost alone. Qwen3.5-122B-A10B is the reasoning model in the pair, while MiniMax M2.7 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 200K for MiniMax M2.7.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | MiniMax M2.7 | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticMiniMax M2.7 wins | ||
| Terminal-Bench 2.0 | 57% | 49.4% |
| Toolathlon | 46.3% | — |
| MLE-Bench Lite | 66.6% | — |
| MM-ClawBench | 62.7% | — |
| BrowseComp | — | 63.8% |
| OSWorld-Verified | — | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| SWE-bench Pro | 56.2% | — |
| SWE Multilingual | 76.5% | — |
| Multi-SWE Bench | 52.7% | — |
| VIBE-Pro | 55.6% | — |
| NL2Repo | 39.8% | — |
| SWE-bench Verified | — | 72% |
| LiveCodeBench | — | 78.9% |
| Multimodal & Grounded | ||
| GDPval-AA | 1495 | — |
| MMMU-Pro | — | 76.9% |
| Reasoning | ||
| LongBench v2 | — | 60.2% |
| Knowledge | ||
| MMLU-Pro | — | 86.7% |
| SuperGPQA | — | 67.1% |
| GPQA | — | 86.6% |
| Instruction Following | ||
| IFEval | — | 93.4% |
| Multilingual | ||
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| Coming soon | ||
Qwen3.5-122B-A10B is ahead overall, 71 to 66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 57% and 49.4%.
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 76.3 versus 56.2. MiniMax M2.7 stays close enough that the answer can still flip depending on your workload.
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 56. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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