Model comparison
MiniMax M2.7 vs Qwen3 Max
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MiniMax M2.7 #36 (Supported); Qwen3 Max #128 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MiniMax M2.7 and Qwen3 Max share 14 comparable benchmark results. 0 of 8 categories are comparable. 21 results are unique to MiniMax M2.7; 0 to Qwen3 Max.
Updated July 23, 2026- Shared results
- 14
- MiniMax M2.7 only
- 21
- Qwen3 Max only
- 0
- Comparable categories
- 0 / 8
Benchmark data for MiniMax M2.7 and Qwen3 Max is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 6 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.
Qwen3 Max has the larger context window at 1M, 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 Max |
|---|---|---|---|
| Agentic | MiniMax M2.757.0 | MarginNo overlap | Qwen3 MaxNot measured |
| Coding | MiniMax M2.753.3 | MarginNo overlap | Qwen3 MaxNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MiniMax M2.7 | Qwen3 Max | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MiniMax M2.7$0.3 input / $1.2 output | Qwen3 MaxNot available | A complete price comparison is not available. |
| Generation speedtokens per second | MiniMax M2.745 tok/s | Qwen3 MaxNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MiniMax M2.72.53 s | Qwen3 MaxNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MiniMax M2.7200K | Qwen3 Max1M | Qwen3 Max lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3 Max | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 57% | — | Not comparable |
| τ²-bench resultsSource | 84.8% | 74.3% | MiniMax M2.7 leads |
| ToolathlonSource | 46.3% | — | Not comparable |
| MLE-Bench LiteSource | 66.6% | — | Not comparable |
| MM-ClawBenchSource | 62.7% | — | Not comparable |
| Claw-EvalSource | 48.7% | — | Not comparable |
| AA Agentic IndexSource | 25.6% | — | Not comparable |
| APEX-Agents-AASource | 10.6% | — | Not comparable |
| GDPval-AASource | 32.9% | — | Not comparable |
| GDPval-AASource | 1158 | — | Not comparable |
| Gert LabsSource | 40.40% | 43.74% | Qwen3 Max leads |
Coding11 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3 Max | Result |
|---|---|---|---|
| SWE-bench Verified*Source | 75.4% | — | Not comparable |
| SWE-bench ProSource | 56.2% | — | Not comparable |
| SWE-RebenchSource | 51.9% | — | Not comparable |
| SWE MultilingualSource | 76.5% | — | Not comparable |
| Multi-SWE BenchSource | 52.7% | — | Not comparable |
| VIBE-ProSource | 55.6% | — | Not comparable |
| NL2RepoSource | 39.8% | — | Not comparable |
| Vibe Code BenchSource | 27.04% | 3.51% | MiniMax M2.7 leads |
| React Native EvalsSource | 71.4% | — | Not comparable |
| AA Coding IndexSource | 52.6% | — | Not comparable |
| AA-SciCodeSource | 47.0% | 38.3% | MiniMax M2.7 leads |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3 Max | Result |
|---|---|---|---|
| GPQA-DSource | 87.0% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 80.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 38.1% | 24.0% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 87.4% | 76.4% | MiniMax M2.7 leads |
| AA-HLESource | 28.1% | 11.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | 0.7% | -43.1% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 26.1% | 24.4% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 34.4% | 89.4% | MiniMax M2.7 leads |
Math1 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3 Max | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | 80.0% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3 Max | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1275 | 1148 | MiniMax M2.7 leads |
Inst. Following1 benchmarks
| Benchmark | MiniMax M2.7 | Qwen3 Max | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.7% | 44.1% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Can I compare MiniMax M2.7 and Qwen3 Max 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 M2.7 and Qwen3 Max today?
MiniMax M2.7: $0.30 input / $1.20 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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