Model comparison
GPT-5.2-Codex vs Mistral Large 2
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.2-Codex #58 (Supported); Mistral Large 2 #163 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.2-Codex and Mistral Large 2 share 11 comparable benchmark results. 0 of 8 categories are comparable. 4 results are unique to GPT-5.2-Codex; 0 to Mistral Large 2.
Updated July 20, 2026- Shared results
- 11
- GPT-5.2-Codex only
- 4
- Mistral Large 2 only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.2-Codex and Mistral Large 2 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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-5.2-Codex has the larger context window at 400K, compared with 128K for Mistral Large 2.
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-5.2-Codex | Mistral Large 2 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.2-Codex$1.75 input / $14 output | Mistral Large 2Not available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.2-Codex123 tok/s | Mistral Large 238 tok/s | GPT-5.2-Codex has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.2-Codex87.34 s | Mistral Large 21.45 s | Mistral Large 2 reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.2-Codex400K | Mistral Large 2128K | GPT-5.2-Codex lists the larger context window. |
Benchmark Deep Dive
Agentic3 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | GPT-5.2-Codex | Mistral Large 2 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 40.1% | 9.2% | GPT-5.2-Codex leads |
| AA-GPQA DiamondSource | 89.9% | 48.6% | GPT-5.2-Codex leads |
| AA-HLESource | 33.5% | 4.0% | GPT-5.2-Codex leads |
| AA-Omniscience IndexSource | -2.5% | -34.0% | GPT-5.2-Codex leads |
| AA-Omniscience AccuracySource | 40.7% | 20.1% | GPT-5.2-Codex leads |
| AA-Omniscience Hallucination RateSource | 72.8% | 67.8% | Mistral Large 2 leads |
Multimodal1 benchmarks
| Benchmark | GPT-5.2-Codex | Mistral Large 2 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 76.3% | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.2-Codex | Mistral Large 2 | Result |
|---|---|---|---|
| AA-IFBenchSource | 77.6% | 31.2% | GPT-5.2-Codex leads |
Frequently Asked Questions (3)
Can I compare GPT-5.2-Codex and Mistral Large 2 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-5.2-Codex and Mistral Large 2 today?
GPT-5.2-Codex: $1.75 input / $14.00 output per 1M tokens Mistral Large 2: Pricing unavailable Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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