Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
MiniMax M2.7
~66
0/8 categoriesQwen3.6 Plus
69
Winner · 2/8 categoriesMiniMax M2.7· Qwen3.6 Plus
Pick Qwen3.6 Plus if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6 Plus has the cleaner overall profile here, landing at 69 versus 66. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.6 Plus's sharpest advantage is in coding, where it averages 64.9 against 56.2. The single biggest benchmark swing on the page is Claw-Eval, 51.9% to 58.7%.
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 Qwen3.6 Plus. That is roughly Infinityx on output cost alone. Qwen3.6 Plus 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. Qwen3.6 Plus gives you the larger context window at 1M, 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 | Qwen3.6 Plus |
|---|---|---|
| AgenticQwen3.6 Plus wins | ||
| Terminal-Bench 2.0 | 57% | 61.6% |
| Tau2-Airline | 80.0% | — |
| Tau2-Telecom | 84.8% | — |
| PinchBench | 89.8% | — |
| BFCL v4 | 70.6% | — |
| Toolathlon | 46.3% | 39.8% |
| MLE-Bench Lite | 66.6% | — |
| MM-ClawBench | 62.7% | — |
| Claw-Eval | 51.9% | 58.7% |
| QwenClawBench | — | 57.2% |
| QwenWebBench | — | 1502 |
| TAU3-Bench | — | 70.7% |
| VITA-Bench | — | 44.3% |
| DeepPlanning | — | 41.5% |
| MCP Atlas | — | 48.2% |
| MCP-Tasks | — | 74.1% |
| WideResearch | — | 74.3% |
| OSWorld-Verified | — | 62.5% |
| CodingQwen3.6 Plus wins | ||
| SWE-bench Verified* | 75.4% | — |
| SWE-bench Pro | 56.2% | 56.6% |
| SWE Multilingual | 76.5% | 73.8% |
| Multi-SWE Bench | 52.7% | — |
| VIBE-Pro | 55.6% | — |
| NL2Repo | 39.8% | 37.9% |
| SWE-bench Verified | — | 78.8% |
| LiveCodeBench v6 | — | 87.1% |
| Multimodal & Grounded | ||
| GDPval-AA | 1495 | — |
| MMMU | — | 86.0% |
| MMMU-Pro | — | 78.8% |
| RealWorldQA | — | 85.4% |
| OmniDocBench 1.5 | — | 91.2% |
| Video-MME (with subtitle) | — | 87.8% |
| Video-MME (w/o subtitle) | — | 84.2% |
| MathVision | — | 88.0% |
| We-Math | — | 89.0% |
| DynaMath | — | 88.0% |
| MStar | — | 83.3% |
| SimpleVQA | — | 67.3% |
| ChatCVQA | — | 81.5% |
| MMLongBench-Doc | — | 62.0% |
| CC-OCR | — | 83.4% |
| AI2D_TEST | — | 94.4% |
| CountBench | — | 97.6% |
| RefCOCO (avg) | — | 93.5% |
| ODINW13 | — | 51.8% |
| ERQA | — | 65.7% |
| VideoMMMU | — | 84.0% |
| MLVU (M-Avg) | — | 86.7% |
| ScreenSpot Pro | — | 68.2% |
| Reasoning | ||
| AI-Needle | — | 68.3% |
| LongBench v2 | — | 62% |
| Knowledge | ||
| GPQA-D | 86.2% | — |
| MMLU-Pro (Arcee) | 80.8% | — |
| GPQA | — | 90.4% |
| SuperGPQA | — | 71.6% |
| MMLU-Pro | — | 88.5% |
| MMLU-Redux | — | 94.5% |
| C-Eval | — | 93.3% |
| HLE | — | 28.8% |
| Instruction Following | ||
| IFBench | 75.7% | 74.2% |
| IFEval | — | 94.3% |
| Multilingual | ||
| MMLU-ProX | — | 84.7% |
| NOVA-63 | — | 57.9% |
| INCLUDE | — | 85.1% |
| PolyMath | — | 77.4% |
| VWT2k-lite | — | 84.3% |
| MAXIFE | — | 88.2% |
| Mathematics | ||
| AIME25 (Arcee) | 80.0% | — |
| AIME26 | — | 95.3% |
| HMMT Feb 2025 | — | 96.7% |
| HMMT Nov 2025 | — | 94.6% |
| HMMT Feb 2026 | — | 87.8% |
| MMAnswerBench | — | 83.8% |
Qwen3.6 Plus is ahead overall, 69 to 66. The biggest single separator in this matchup is Claw-Eval, where the scores are 51.9% and 58.7%.
Qwen3.6 Plus has the edge for coding in this comparison, averaging 64.9 versus 56.2. Inside this category, SWE Multilingual is the benchmark that creates the most daylight between them.
Qwen3.6 Plus has the edge for agentic tasks in this comparison, averaging 62 versus 57. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.