Model comparison
GPT-5.1-Codex-Max vs Mistral 7B v0.3
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: GPT-5.1-Codex-Max #90 (Estimated); Mistral 7B v0.3 #173 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.1-Codex-Max and Mistral 7B v0.3 share 0 comparable benchmark results. 0 of 8 categories are comparable. 13 results are unique to GPT-5.1-Codex-Max; 0 to Mistral 7B v0.3.
Updated July 21, 2026- Shared results
- 0
- GPT-5.1-Codex-Max only
- 13
- Mistral 7B v0.3 only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.1-Codex-Max and Mistral 7B v0.3 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 7B v0.3 yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
GPT-5.1-Codex-Max is priced at $1.25 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Mistral 7B v0.3. GPT-5.1-Codex-Max has the larger context window at 400K, compared with 32K for Mistral 7B v0.3.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.1-Codex-Max | Mistral 7B v0.3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.1-Codex-Max$1.25 input / $10 output | Mistral 7B v0.3$0 input / $0 output | Mistral 7B v0.3 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.1-Codex-MaxNot available | Mistral 7B v0.3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.1-Codex-MaxNot available | Mistral 7B v0.3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.1-Codex-Max400K | Mistral 7B v0.332K | GPT-5.1-Codex-Max lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | GPT-5.1-Codex-Max | Mistral 7B v0.3 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 83% | — | Not comparable |
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | GPT-5.1-Codex-Max | Mistral 7B v0.3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 34.7% | — | Not comparable |
| AA-GPQA DiamondSource | 86.0% | — | Not comparable |
| AA-HLESource | 23.4% | — | Not comparable |
| AA-Omniscience IndexSource | -6.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 39.2% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 74.4% | — | Not comparable |
Multimodal1 benchmarks
| Benchmark | GPT-5.1-Codex-Max | Mistral 7B v0.3 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 72.5% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.1-Codex-Max | Mistral 7B v0.3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 70.0% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GPT-5.1-Codex-Max and Mistral 7B v0.3 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 7B v0.3 today?
GPT-5.1-Codex-Max: $1.25 input / $10.00 output per 1M tokens Mistral 7B v0.3: $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.
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