Model comparison
MiniMax M2.7 vs Qwen3.6-27B
Head-to-head evidence from 21 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MiniMax M2.7 #36 (Supported); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MiniMax M2.7 and Qwen3.6-27B share 21 comparable benchmark results. 2 of 8 categories are comparable. 14 results are unique to MiniMax M2.7; 33 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 21
- MiniMax M2.7 only
- 14
- Qwen3.6-27B only
- 33
- Comparable categories
- 2 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 21 shared benchmark results across 5 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
MiniMax M2.7 is clearly ahead on the BenchAlign aggregate, 64.11 to 53.82. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
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 Qwen3.6-27B. That is roughly Infinityx on output cost alone. Qwen3.6-27B 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. Qwen3.6-27B gives you the larger context window at 262K, compared with 200K for MiniMax M2.7.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | MiniMax M2.7 | Δ | Qwen3.6-27B |
|---|---|---|---|
| Coding | MiniMax M2.753.3 | Margin→ 24.2 | Qwen3.6-27B77.5 |
| Agentic | MiniMax M2.757.0 | Margin→ 2.3 | Qwen3.6-27B59.3 |
| Knowledge | MiniMax M2.7Not measured | MarginNo overlap | Qwen3.6-27B53.3 |
| Math | MiniMax M2.7Not measured | MarginNo overlap | Qwen3.6-27B89.2 |
| Multimodal | MiniMax M2.7Not measured | MarginNo overlap | Qwen3.6-27B76.7 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Pro
CodingA 56.2%B 53.5%Winner: MiniMax M2.7Δ 2.7SWE-bench Pro: MiniMax M2.7 scored 56.2%; Qwen3.6-27B scored 53.5%. MiniMax M2.7 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 57%B 59.3%Winner: Qwen3.6-27BΔ 2.3Terminal-Bench 2.0: MiniMax M2.7 scored 57%; Qwen3.6-27B scored 59.3%. Qwen3.6-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MiniMax M2.7 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MiniMax M2.7$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 M2.745 tok/s | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MiniMax M2.72.53 s | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MiniMax M2.7200K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
AgenticQwen3.6-27B wins14 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3.6-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 57% | 59.3% | Qwen3.6-27B leads |
| τ²-bench resultsSource | 84.8% | 94.2% | Qwen3.6-27B leads |
| ToolathlonSource | 46.3% | — | Not comparable |
| MLE-Bench LiteSource | 66.6% | — | Not comparable |
| MM-ClawBenchSource | 62.7% | — | Not comparable |
| Claw-EvalSource | 48.7% | 72.4% | Qwen3.6-27B leads |
| AA Agentic IndexSource | 25.6% | 27.0% | Qwen3.6-27B leads |
| APEX-Agents-AASource | 10.6% | — | Not comparable |
| GDPval-AASource | 32.9% | 32.0% | MiniMax M2.7 leads |
| GDPval-AASource | 1158 | 1140 | MiniMax M2.7 leads |
| Gert LabsSource | 40.40% | 54.84% | Qwen3.6-27B leads |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
CodingQwen3.6-27B wins14 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-bench Verified*Source | 75.4% | — | Not comparable |
| SWE-bench ProSource | 56.2% | 53.5% | MiniMax M2.7 leads |
| SWE-RebenchSource | 51.9% | — | Not comparable |
| SWE MultilingualSource | 76.5% | 71.3% | MiniMax M2.7 leads |
| Multi-SWE BenchSource | 52.7% | — | Not comparable |
| VIBE-ProSource | 55.6% | — | Not comparable |
| NL2RepoSource | 39.8% | 36.2% | MiniMax M2.7 leads |
| Vibe Code BenchSource | 27.04% | — | Not comparable |
| React Native EvalsSource | 71.4% | — | Not comparable |
| AA Coding IndexSource | 52.6% | 53.7% | Qwen3.6-27B leads |
| AA-SciCodeSource | 47.0% | 39.8% | MiniMax M2.7 leads |
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
Reasoning2 benchmarks
Knowledge14 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3.6-27B | Result |
|---|---|---|---|
| GPQA-DSource | 87.0% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 80.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 38.1% | 37.0% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 87.4% | 84.2% | MiniMax M2.7 leads |
| AA-HLESource | 28.1% | 21.6% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | 0.7% | -19.8% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 26.1% | 19.2% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 34.4% | 48.3% | MiniMax M2.7 leads |
| 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 |
Math6 benchmarks
Multimodal17 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3.6-27B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1275 | — | Not comparable |
| 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 M2.7 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.7% | 67.6% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Which is better, MiniMax M2.7 or Qwen3.6-27B?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 53.82. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 56.2% and 53.5%.
Which is better for coding, MiniMax M2.7 or Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 53.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, MiniMax M2.7 or Qwen3.6-27B?
Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 57. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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