Model comparison
GPT-5.6 Sol vs Mistral Medium 3.5 128B
Head-to-head evidence from 20 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.6 Sol #3 (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. GPT-5.6 Sol and Mistral Medium 3.5 128B share 20 comparable benchmark results. 1 of 8 categories are comparable. 26 results are unique to GPT-5.6 Sol; 5 to Mistral Medium 3.5 128B.
Updated July 23, 2026- Shared results
- 20
- GPT-5.6 Sol only
- 26
- Mistral Medium 3.5 128B only
- 5
- Comparable categories
- 1 / 8
Treat this as a split decision. GPT-5.6 Sol makes more sense if you need the larger 1M context window; Mistral Medium 3.5 128B is the better fit if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 20 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
GPT-5.6 Sol 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.
GPT-5.6 Sol is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $1.50 input / $7.50 output per 1M tokens for Mistral Medium 3.5 128B. That is roughly 4.0x on output cost alone. GPT-5.6 Sol gives you the larger context window at 1M, 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 | GPT-5.6 Sol | Δ | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | GPT-5.6 Sol64.6 | Margin→ 13.0 | Mistral Medium 3.5 128B77.6 |
| Agentic | GPT-5.6 Sol92.0 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Knowledge | GPT-5.6 Sol94.6 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Math | GPT-5.6 Sol87.5 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Multimodal | GPT-5.6 Sol83.0 | 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 | GPT-5.6 Sol | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$5 input / $30 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | Mistral Medium 3.5 128B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.6 SolNot available | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | Mistral Medium 3.5 128B256K | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
Agentic20 benchmarks
| Benchmark | GPT-5.6 Sol | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| BrowseCompSource | 92.2% | — | Not comparable |
| OSWorld 2.0Source | 62.6% | — | Not comparable |
| CyberGymSource | 84.5% | — | Not comparable |
| ExploitGymSource | 33.7% | — | Not comparable |
| ToolathlonSource | 58% | — | Not comparable |
| AA Agentic IndexSource | 54.0% | 19.0% | GPT-5.6 Sol leads |
| τ²-bench resultsSource | 85.1% | 94.2% | Mistral Medium 3.5 128B leads |
| GDPval-AASource | 61.8% | 21.4% | GPT-5.6 Sol leads |
| GDPval-AASource | 1736 | 929 | GPT-5.6 Sol leads |
| AA BriefcaseSource | 1501 | 506 | GPT-5.6 Sol leads |
| AA ITBenchSource | 56.2% | — | Not comparable |
| AA Tau3 BankingSource | 33.0% | 14.4% | GPT-5.6 Sol leads |
| AA AutomationBenchSource | 51.2% | — | Not comparable |
| AA Harvey LABSource | 87.2% | 69.1% | GPT-5.6 Sol leads |
| terminalBenchHardSource | 65.9% | 33.3% | GPT-5.6 Sol leads |
| aaTerminalBench21Source | 88% | — | Not comparable |
| τ³-bench resultsSource | — | 91.4% | Not comparable |
| Gert LabsSource | — | 39.10% | Not comparable |
| AA EnterpriseOps-GymSource | — | 33.7% | Not comparable |
CodingMistral Medium 3.5 128B wins9 benchmarks
| Benchmark | GPT-5.6 Sol | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| SWE-bench ProSource | 64.6% | — | Not comparable |
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| deepSweSource | 72.7% | — | Not comparable |
| FrontierCode 1.1 ExtendedSource | 60.6% | — | Not comparable |
| cursorBench32Source | 67.2% | — | Not comparable |
| VulcanBench v3Source | 87.0% | — | Not comparable |
| AA Coding IndexSource | 77.4% | 46.9% | GPT-5.6 Sol leads |
| AA-SciCodeSource | 56.1% | 39.6% | GPT-5.6 Sol leads |
| SWE-bench VerifiedSource | — | 77.6% | Not comparable |
Reasoning4 benchmarks
Knowledge11 benchmarks
| Benchmark | GPT-5.6 Sol | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| GPQASource | 94.6% | — | Not comparable |
| GPQA-DSource | 94.6% | — | Not comparable |
| HealthBench ProfessionalSource | 60.5% | — | Not comparable |
| HealthBench HardSource | 33.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 58.9% | 29.9% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 94.1% | 74.8% | GPT-5.6 Sol leads |
| AA-HLESource | 47.2% | 12.8% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 21.7% | -36.3% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 58.5% | 25.1% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 88.8% | 82.0% | Mistral Medium 3.5 128B leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math3 benchmarks
Multimodal3 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.6 Sol | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-IFBenchSource | 72.7% | 68.8% | GPT-5.6 Sol leads |
Frequently Asked Questions (2)
Which is better, GPT-5.6 Sol or Mistral Medium 3.5 128B?
GPT-5.6 Sol 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, GPT-5.6 Sol or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 64.6. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
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