Model comparison
Mistral Medium 3.5 128B vs Qwen3.6-27B
Head-to-head evidence from 18 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Mistral Medium 3.5 128B unranked (Not scored); Qwen3.6-27B #99 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Mistral Medium 3.5 128B and Qwen3.6-27B share 18 comparable benchmark results. 1 of 8 categories are comparable. 7 results are unique to Mistral Medium 3.5 128B; 36 to Qwen3.6-27B.
Updated July 27, 2026- Shared results
- 18
- Mistral Medium 3.5 128B only
- 7
- Qwen3.6-27B only
- 36
- Comparable categories
- 1 / 8
Treat this as a split decision. Mistral Medium 3.5 128B makes more sense if coding is the priority; Qwen3.6-27B is the better fit if you want the cheaper token bill or you need the larger 262K context window.
Confidence note. This is a partial-evidence comparison with 18 shared benchmark results across 6 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
Mistral Medium 3.5 128B and Qwen3.6-27B 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 $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. Qwen3.6-27B gives you the larger context window at 262K, compared with 256K for Mistral Medium 3.5 128B.
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 | Mistral Medium 3.5 128B | Δ | Qwen3.6-27B |
|---|---|---|---|
| Coding | Mistral Medium 3.5 128B77.6 | Margin← 0.1 | Qwen3.6-27B77.5 |
| Agentic | Mistral Medium 3.5 128BNot measured | MarginNo overlap | Qwen3.6-27B59.3 |
| Knowledge | Mistral Medium 3.5 128BNot measured | MarginNo overlap | Qwen3.6-27B53.3 |
| Math | Mistral Medium 3.5 128BNot measured | MarginNo overlap | Qwen3.6-27B89.2 |
| Multimodal | Mistral Medium 3.5 128BNot 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 Verified
CodingA 77.6%B 77.2%Winner: Mistral Medium 3.5 128BΔ 0.4SWE-bench Verified: Mistral Medium 3.5 128B scored 77.6%; Qwen3.6-27B scored 77.2%. Mistral Medium 3.5 128B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Mistral Medium 3.5 128B | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Mistral Medium 3.5 128B$1.5 input / $7.5 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | Mistral Medium 3.5 128BNot available | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Mistral Medium 3.5 128BNot available | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Mistral Medium 3.5 128B256K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Qwen3.6-27B | Result |
|---|---|---|---|
| τ³-bench resultsSource | 91.4% | — | Not comparable |
| AA Agentic IndexSource | 19.0% | 27.0% | Qwen3.6-27B leads |
| τ²-bench resultsSource | 94.2% | 94.2% | Tie |
| GDPval-AASource | 21.6% | 31.9% | Qwen3.6-27B leads |
| GDPval-AASource | 933 | 1138 | Qwen3.6-27B leads |
| Gert LabsSource | 39.10% | 54.84% | Qwen3.6-27B leads |
| 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 |
| 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 |
CodingMistral Medium 3.5 128B wins8 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.6% | 77.2% | Mistral Medium 3.5 128B leads |
| AA Coding IndexSource | 46.9% | 53.7% | Qwen3.6-27B leads |
| AA-SciCodeSource | 39.6% | 39.8% | Qwen3.6-27B leads |
| 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 |
Reasoning2 benchmarks
Knowledge13 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Qwen3.6-27B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 29.9% | 37.0% | Qwen3.6-27B leads |
| AA-GPQA DiamondSource | 74.8% | 84.2% | Qwen3.6-27B leads |
| AA-HLESource | 12.8% | 21.6% | Qwen3.6-27B leads |
| AA-Omniscience IndexSource | -36.3% | -19.8% | Qwen3.6-27B leads |
| AA-Omniscience AccuracySource | 25.1% | 19.2% | Mistral Medium 3.5 128B leads |
| AA-Omniscience Hallucination RateSource | 82.0% | 48.3% | Qwen3.6-27B leads |
| AA Openness IndexSource | 33.3% | — | Not comparable |
| 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 |
Math5 benchmarks
Multimodal16 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 64.9% | 74.6% | Qwen3.6-27B leads |
| 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 |
Inst. Following1 benchmarks
| Benchmark | Mistral Medium 3.5 128B | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 68.8% | 67.6% | Mistral Medium 3.5 128B leads |
Frequently Asked Questions (2)
Which is better, Mistral Medium 3.5 128B or Qwen3.6-27B?
Mistral Medium 3.5 128B and Qwen3.6-27B 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, Mistral Medium 3.5 128B or Qwen3.6-27B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 77.5. Inside this category, AA Coding Index 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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