Model comparison
GPT-5.5 vs Mistral Medium 3.5 128B
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: GPT-5.5 #11 (Estimated); 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-5.5 and Mistral Medium 3.5 128B share 18 comparable benchmark results. 1 of 8 categories are comparable. 35 results are unique to GPT-5.5; 7 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 18
- GPT-5.5 only
- 35
- Mistral Medium 3.5 128B only
- 7
- Comparable categories
- 1 / 8
Treat this as a split decision. GPT-5.5 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 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
GPT-5.5 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-5.5 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-5.5 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-5.5 | Δ | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | GPT-5.558.6 | Margin→ 19.0 | Mistral Medium 3.5 128B77.6 |
| Agentic | GPT-5.581.6 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Reasoning | GPT-5.585.0 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Knowledge | GPT-5.557.8 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Math | GPT-5.547.6 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
| Multimodal | GPT-5.570.4 | 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-5.5 | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$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-5.5Not available | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | Mistral Medium 3.5 128B256K | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
Agentic24 benchmarks
| Benchmark | GPT-5.5 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | — | Not comparable |
| CyberGymSource | 81.8% | — | Not comparable |
| BrowseCompSource | 84.4% | — | Not comparable |
| OSWorld-VerifiedSource | 78.7% | — | Not comparable |
| MCP AtlasSource | 75.3% | — | Not comparable |
| ToolathlonSource | 55.6% | — | Not comparable |
| τ²-bench resultsSource | 93.9% | 94.2% | Mistral Medium 3.5 128B leads |
| AA Agentic IndexSource | 44.9% | 19.0% | GPT-5.5 leads |
| APEX-Agents-AASource | 37.7% | — | Not comparable |
| GDPval-AASource | 49.6% | 21.6% | GPT-5.5 leads |
| GDPval-AASource | 1491 | 933 | GPT-5.5 leads |
| Gert LabsSource | 72.93% | 39.10% | GPT-5.5 leads |
| ResearchClawBenchSource | 17.0% | — | Not comparable |
| OSWorld 2.0Source | 13.0% | — | Not comparable |
| JobBenchSource | 42.7% | — | Not comparable |
| ExploitGymSource | 13.4% | — | Not comparable |
| AA AutomationBenchSource | 42.1% | — | Not comparable |
| AA EnterpriseOps-GymSource | 46.6% | 33.7% | GPT-5.5 leads |
| AA ITBenchSource | 45.8% | — | Not comparable |
| τ³-bench resultsSource | — | 91.4% | 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 wins10 benchmarks
| Benchmark | GPT-5.5 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | — | Not comparable |
| Terminal-Bench 2.0Source | 82.0% | — | Not comparable |
| Vibe Code BenchSource | 69.85% | — | Not comparable |
| React Native EvalsSource | 84.7% | — | Not comparable |
| cursorBench31Source | 59.2% | — | Not comparable |
| cursorBench32Source | 58.4% | — | Not comparable |
| AA Coding IndexSource | 74.9% | 46.9% | GPT-5.5 leads |
| AA-SciCodeSource | 56.1% | 39.6% | GPT-5.5 leads |
| FrontierCode 1.1 MainSource | 43.0% | — | Not comparable |
| SWE-bench VerifiedSource | — | 77.6% | Not comparable |
Reasoning6 benchmarks
Knowledge11 benchmarks
| Benchmark | GPT-5.5 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| GPQASource | 93.6% | — | Not comparable |
| GPQA-DSource | 93.6% | — | Not comparable |
| HLESource | 52.2% | — | Not comparable |
| HLE w/o toolsSource | 41.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 54.8% | 29.9% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 93.5% | 74.8% | GPT-5.5 leads |
| AA-HLESource | 44.3% | 12.8% | GPT-5.5 leads |
| AA-Omniscience IndexSource | 20.1% | -36.3% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 56.9% | 25.1% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 85.5% | 82.0% | Mistral Medium 3.5 128B leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math3 benchmarks
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.5 | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | 68.8% | GPT-5.5 leads |
Frequently Asked Questions (2)
Which is better, GPT-5.5 or Mistral Medium 3.5 128B?
GPT-5.5 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-5.5 or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 58.6. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
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