Model comparison
GPT-5.1-Codex-Max vs Mistral Large 3
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.1-Codex-Max #90 (Estimated); Mistral Large 3 #113 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.1-Codex-Max and Mistral Large 3 share 12 comparable benchmark results. 0 of 8 categories are comparable. 1 result is unique to GPT-5.1-Codex-Max; 4 to Mistral Large 3.
Updated July 20, 2026- Shared results
- 12
- GPT-5.1-Codex-Max only
- 1
- Mistral Large 3 only
- 4
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.1-Codex-Max and Mistral Large 3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 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 has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GPT-5.1-Codex-Max is priced at $1.25 input / $10.00 output per 1M tokens, versus $0.50 input / $1.50 output per 1M tokens for Mistral Large 3. GPT-5.1-Codex-Max has the larger context window at 400K, compared with 128K for Mistral Large 3.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.1-Codex-Max | Mistral Large 3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.1-Codex-Max$1.25 input / $10 output | Mistral Large 3$0.5 input / $1.5 output | Mistral Large 3 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.1-Codex-MaxNot available | Mistral Large 348 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.1-Codex-MaxNot available | Mistral Large 31.04 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.1-Codex-Max400K | Mistral Large 3128K | GPT-5.1-Codex-Max lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding3 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 34.7% | 15.9% | GPT-5.1-Codex-Max leads |
| AA-GPQA DiamondSource | 86.0% | 68.0% | GPT-5.1-Codex-Max leads |
| AA-HLESource | 23.4% | 4.1% | GPT-5.1-Codex-Max leads |
| AA-Omniscience IndexSource | -6.0% | -39.4% | GPT-5.1-Codex-Max leads |
| AA-Omniscience AccuracySource | 39.2% | 24.1% | GPT-5.1-Codex-Max leads |
| AA-Omniscience Hallucination RateSource | 74.4% | 83.7% | GPT-5.1-Codex-Max leads |
Multimodal1 benchmarks
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 3 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 72.5% | 55.7% | GPT-5.1-Codex-Max leads |
Inst. Following1 benchmarks
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 70.0% | 36.2% | GPT-5.1-Codex-Max leads |
Frequently Asked Questions (3)
Can I compare GPT-5.1-Codex-Max and Mistral Large 3 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 GPT-5.1-Codex-Max and Mistral Large 3 today?
GPT-5.1-Codex-Max: $1.25 input / $10.00 output per 1M tokens Mistral Large 3: $0.50 input / $1.50 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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