Model comparison
MiniMax M2.7 vs Mistral Medium 3.5 128B
Head-to-head evidence from 16 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: MiniMax M2.7 #40 (Supported); Mistral Medium 3.5 128B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. MiniMax M2.7 and Mistral Medium 3.5 128B share 16 comparable benchmark results. 1 of 8 categories are comparable. 19 results are unique to MiniMax M2.7; 9 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 16
- MiniMax M2.7 only
- 19
- Mistral Medium 3.5 128B only
- 9
- Comparable categories
- 1 / 8
Treat this as a split decision. MiniMax M2.7 makes more sense if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model; Mistral Medium 3.5 128B is the better fit if coding is the priority or you need the larger 256K context window.
Confidence note. This is a partial-evidence comparison with 16 shared benchmark results across 5 evidence categories; 1 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 and Mistral Medium 3.5 128B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Mistral Medium 3.5 128B is also the more expensive model on tokens at $1.50 input / $7.50 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 6.3x on output cost alone. Mistral Medium 3.5 128B 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. Mistral Medium 3.5 128B gives you the larger context window at 256K, 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 | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | MiniMax M2.753.3 | Margin→ 24.3 | Mistral Medium 3.5 128B77.6 |
| Agentic | MiniMax M2.757.0 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | MiniMax M2.7 | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | MiniMax M2.7$0.3 input / $1.2 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | MiniMax M2.7 has the lower combined listed price. |
| Generation speedtokens per second | MiniMax M2.745 tok/s | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | MiniMax M2.72.53 s | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | MiniMax M2.7200K | Mistral Medium 3.5 128B256K | Mistral Medium 3.5 128B lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | MiniMax M2.7 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 57% | — | Not comparable |
| τ²-bench resultsSource | 84.8% | 94.2% | Mistral Medium 3.5 128B 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% | 19.0% | MiniMax M2.7 leads |
| APEX-Agents-AASource | 10.6% | — | Not comparable |
| GDPval-AASource | 32.9% | 21.6% | MiniMax M2.7 leads |
| GDPval-AASource | 1159 | 933 | MiniMax M2.7 leads |
| Gert LabsSource | 40.40% | 39.10% | MiniMax M2.7 leads |
| τ³-bench resultsSource | — | 91.4% | Not comparable |
| AA EnterpriseOps-GymSource | — | 33.7% | Not comparable |
| AA Harvey LABSource | — | 69.1% | Not comparable |
| terminalBenchHardSource | — | 33.3% | Not comparable |
| AA BriefcaseSource | — | 516 | Not comparable |
| AA Tau3 BankingSource | — | 14.4% | Not comparable |
CodingMistral Medium 3.5 128B wins12 benchmarks
| Benchmark | MiniMax M2.7 | Mistral Medium 3.5 128B | 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% | — | Not comparable |
| React Native EvalsSource | 71.4% | — | Not comparable |
| AA Coding IndexSource | 52.6% | 46.9% | MiniMax M2.7 leads |
| AA-SciCodeSource | 47.0% | 39.6% | MiniMax M2.7 leads |
| SWE-bench VerifiedSource | — | 77.6% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | MiniMax M2.7 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| GPQA-DSource | 87.0% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 80.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 38.1% | 29.9% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 87.4% | 74.8% | MiniMax M2.7 leads |
| AA-HLESource | 28.1% | 12.8% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | 0.7% | -36.3% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 26.1% | 25.1% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 34.4% | 82.0% | MiniMax M2.7 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math1 benchmarks
| Benchmark | MiniMax M2.7 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | 80.0% | — | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | MiniMax M2.7 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.7% | 68.8% | MiniMax M2.7 leads |
Frequently Asked Questions (2)
Which is better, MiniMax M2.7 or Mistral Medium 3.5 128B?
MiniMax M2.7 and Mistral Medium 3.5 128B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for coding, MiniMax M2.7 or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 53.3. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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