Model comparison
Mistral 8x7B 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: Mistral 8x7B #146 (Estimated); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Mistral 8x7B and Qwen3.6-27B share 0 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to Mistral 8x7B; 54 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 0
- Mistral 8x7B only
- 0
- Qwen3.6-27B only
- 54
- Comparable categories
- 0 / 8
Benchmark data for Mistral 8x7B 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 does not have sourced benchmark coverage for Mistral 8x7B yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
Qwen3.6-27B has the larger context window at 262K, compared with 32K for Mistral 8x7B.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Mistral 8x7B | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Mistral 8x7B$0 input / $0 output | Qwen3.6-27B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | Mistral 8x7BNot available | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Mistral 8x7BNot available | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Mistral 8x7B32K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
Agentic10 benchmarks
| Benchmark | Mistral 8x7B | 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 |
Coding8 benchmarks
| Benchmark | Mistral 8x7B | Qwen3.6-27B | Result |
|---|---|---|---|
| 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 | Mistral 8x7B | 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 | Mistral 8x7B | 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 | Mistral 8x7B | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 67.6% | Not comparable |
Frequently Asked Questions (3)
Can I compare Mistral 8x7B 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 Mistral 8x7B and Qwen3.6-27B today?
Mistral 8x7B: $0.00 input / $0.00 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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