Model comparison
MiniMax M2.5 vs Qwen3.6-27B
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MiniMax M2.5 #55 (Supported); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MiniMax M2.5 and Qwen3.6-27B share 0 comparable benchmark results. 0 of 8 categories are comparable. 1 result is unique to MiniMax M2.5; 54 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 0
- MiniMax M2.5 only
- 1
- Qwen3.6-27B only
- 54
- Comparable categories
- 0 / 8
Benchmark data for MiniMax M2.5 and Qwen3.6-27B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
MiniMax M2.5 is priced at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. Qwen3.6-27B has the larger context window at 262K, compared with 128K for MiniMax M2.5.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MiniMax M2.5 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MiniMax M2.5$0.3 input / $1.2 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | MiniMax M2.546 tok/s | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MiniMax M2.52.12 s | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MiniMax M2.5128K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
Agentic10 benchmarks
| Benchmark | MiniMax M2.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| Claw-EvalSource | — | 72.4% | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
| AA Agentic IndexSource | — | 27.0% | Not comparable |
| τ²-bench resultsSource | — | 94.2% | Not comparable |
| GDPval-AASource | — | 32.0% | Not comparable |
| GDPval-AASource | — | 1140 | Not comparable |
| Gert LabsSource | — | 54.84% | Not comparable |
Coding9 benchmarks
| Benchmark | MiniMax M2.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| Vibe Code BenchSource | 14.85% | — | Not comparable |
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| SWE MultilingualSource | — | 71.3% | Not comparable |
| SWE-bench ProSource | — | 53.5% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
| NL2RepoSource | — | 36.2% | Not comparable |
| AA Coding IndexSource | — | 53.7% | Not comparable |
| AA-SciCodeSource | — | 39.8% | Not comparable |
Reasoning2 benchmarks
Knowledge12 benchmarks
| Benchmark | MiniMax M2.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| MMLU-ProSource | — | 86.2% | Not comparable |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
| GPQASource | — | 87.8% | Not comparable |
| HLESource | — | 24% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 37.0% | Not comparable |
| AA-GPQA DiamondSource | — | 84.2% | Not comparable |
| AA-HLESource | — | 21.6% | Not comparable |
| AA-Omniscience IndexSource | — | -19.8% | Not comparable |
| AA-Omniscience AccuracySource | — | 19.2% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 48.3% | Not comparable |
Math5 benchmarks
Multimodal16 benchmarks
| Benchmark | MiniMax M2.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| MMMUSource | — | 82.9% | Not comparable |
| MMMU-ProSource | — | 75.8% | Not comparable |
| RealWorldQASource | — | 84.1% | Not comparable |
| DynaMathSource | — | 85.6% | Not comparable |
| MStarSource | — | 81.4% | Not comparable |
| SimpleVQASource | — | 56.1% | Not comparable |
| CharXivSource | — | 78.4% | Not comparable |
| CC-OCRSource | — | 81.2% | Not comparable |
| CountBenchSource | — | 97.8% | Not comparable |
| RefCOCO (avg)Source | — | 92.5% | Not comparable |
| ERQASource | — | 62.5% | Not comparable |
| Video-MME (with subtitle)Source | — | 87.7% | Not comparable |
| VideoMMMUSource | — | 84.4% | Not comparable |
| MLVU (M-Avg)Source | — | 86.6% | Not comparable |
| V*Source | — | 94.7% | Not comparable |
| AA-MMMU-ProSource | — | 74.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | MiniMax M2.5 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 67.6% | Not comparable |
Frequently Asked Questions (3)
Can I compare MiniMax M2.5 and Qwen3.6-27B on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for MiniMax M2.5 and Qwen3.6-27B today?
MiniMax M2.5: $0.30 input / $1.20 output per 1M tokens Qwen3.6-27B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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