GPT-5.1-Codex-Max vs Mistral Large 4
Updated October 21, 2026. 3 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Mistral Large 4 has a coding score, 56.4 on the Vibe Coding Index; GPT-5.1-Codex-Max has none, so the two cannot be ranked on coding.
GPT-5.1-Codex-Max has the edge where the benchmark that matters is ALE-Bench, where it leads.
- Shared results
- 3
- GPT-5.1-Codex-Max only
- 3
- Mistral Large 4 only
- 78
- Like-for-like categories
- 2 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Mistral Large 4 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Mistral Large 4 has a AA Intelligence Index result, 38.4.
Directional only
Web design
Design Arena: websites
No call
Only Mistral Large 4 has a Design Arena: websites result, 1258.
Directional only
Long documents
Listed context window
Mistral Large 4
Mistral Large 4 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 4
GPT-5.1-Codex-Max $0.0063, Mistral Large 4 $0.0034 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Mistral Large 4 56.4 from 5 benchmarks.
- Widest category
- Coding, 28.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.1-Codex-Max 22.2% against Mistral Large 4 78.4%, 56.2 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Max $10.00 against Mistral Large 4 $4.18 per million output tokens, 2.4x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Mistral Large 4 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-5.1-Codex-Max | Bars | Mistral Large 4 | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Math | 0 | 1 | ||
| All shared boards | 1 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 60.0% | Only Mistral Large 4 measured |
| LMArena WebDev | Not measured | 1543 | Only Mistral Large 4 measured |
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 27.8% | Only Mistral Large 4 measured |
| Vibe Code Bench (Vals AI) | 22.2% | 78.4% | Mistral Large 4 |
| Code migration (Vals AI) | Not measured | 30.6% | Only Mistral Large 4 measured |
| IOI (Vals AI) | Not measured | 45.3% | Only Mistral Large 4 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.1% | Only Mistral Large 4 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.3% | Only Mistral Large 4 measured |
| SciCode | Not measured | 54.2% | Only Mistral Large 4 measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| FrontierCode | Not measured | 39.4% | Only Mistral Large 4 measured |
| ALE-Bench | 1228.3 | 1213.2 | GPT-5.1-Codex-Max |
| LMArena Text: coding | Not measured | 1478 | Only Mistral Large 4 measured |
| LMArena Text: software and IT | Not measured | 1467 | Only Mistral Large 4 measured |
| LMArena WebDev: React | Not measured | 1559 | Only Mistral Large 4 measured |
| LMArena WebDev: HTML | Not measured | 1487 | Only Mistral Large 4 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 38.4 | Only Mistral Large 4 measured |
| Epoch Capabilities Index | Not measured | 150.0 | Only Mistral Large 4 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 28.1% | Only Mistral Large 4 measured |
| LiveBench browser | Not measured | 8.0% | Only Mistral Large 4 measured |
| LiveBench long agentic | Not measured | 11.8% | Only Mistral Large 4 measured |
| Finance Agent (Vals AI) | Not measured | 54.7% | Only Mistral Large 4 measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.4% | Only Mistral Large 4 measured |
| Legal Research Bench (Vals AI) | Not measured | 31.7% | Only Mistral Large 4 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 17.5% | Only Mistral Large 4 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.2% | Only Mistral Large 4 measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Mistral Large 4 measured |
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 83.9% | Only Mistral Large 4 measured |
| MysteryMechanism (Vals AI) | Not measured | 12.2% | Only Mistral Large 4 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 35.0% | Only Mistral Large 4 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 88.1% | Only Mistral Large 4 measured |
| Chess puzzles | Not measured | 33.0% | Only Mistral Large 4 measured |
| Mystery game puzzles | Not measured | 17.0% | Only Mistral Large 4 measured |
| LMArena Text: hard prompts | Not measured | 1444 | Only Mistral Large 4 measured |
| LMArena Text: expert | Not measured | 1444 | Only Mistral Large 4 measured |
| LMArena Text: hard prompts in English | Not measured | 1442 | Only Mistral Large 4 measured |
| LMArena Vision: diagrams | Not measured | 1288 | Only Mistral Large 4 measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 98.3% | Only Mistral Large 4 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 58.6% | Only Mistral Large 4 measured |
| FrontierMath, tier 4 | Not measured | 26.8% | Only Mistral Large 4 measured |
| ProofBench | 9.0% | 10.0% | Mistral Large 4 |
| LMArena Text: math | Not measured | 1469 | Only Mistral Large 4 measured |
| LMArena Text: mathematical work | Not measured | 1462 | Only Mistral Large 4 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 40.7% | Only Mistral Large 4 measured |
| SimpleQA Verified | Not measured | 20.0% | Only Mistral Large 4 measured |
| LMArena Text: science | Not measured | 1448 | Only Mistral Large 4 measured |
| LMArena Text: medicine and healthcare | Not measured | 1433 | Only Mistral Large 4 measured |
| LMArena Text: legal and government | Not measured | 1425 | Only Mistral Large 4 measured |
| LMArena Text: business and finance | Not measured | 1439 | Only Mistral Large 4 measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1258 | Only Mistral Large 4 measured |
| Design Arena: UI components | Not measured | 1275 | Only Mistral Large 4 measured |
| Design Arena: game dev | Not measured | 1226 | Only Mistral Large 4 measured |
| Design Arena: data viz | Not measured | 1274 | Only Mistral Large 4 measured |
| Design Arena: 3D | Not measured | 1253 | Only Mistral Large 4 measured |
| Design Arena: all code categories | Not measured | 1259 | Only Mistral Large 4 measured |
| Design Arena: SVG | Not measured | 1233 | Only Mistral Large 4 measured |
| Design Arena: ASCII art | Not measured | 1203 | Only Mistral Large 4 measured |
| LiveBench design | Not measured | 65.6% | Only Mistral Large 4 measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.5% | Only Mistral Large 4 measured |
| MedScribe (Vals AI) | Not measured | 80.4% | Only Mistral Large 4 measured |
| Long context reasoning (AA-LCR) | Not measured | 81.3% | Only Mistral Large 4 measured |
| DTBench | Not measured | 88.2% | Only Mistral Large 4 measured |
| LMCA | Not measured | 35.8% | Only Mistral Large 4 measured |
| Surface Evolver Bench | Not measured | 28.1% | Only Mistral Large 4 measured |
| Furniture assembly | Not measured | 20.8% | Only Mistral Large 4 measured |
| LMArena Text | Not measured | 1431 | Only Mistral Large 4 measured |
| LMArena Text: instruction following | Not measured | 1422 | Only Mistral Large 4 measured |
| LMArena Text: creative writing | Not measured | 1363 | Only Mistral Large 4 measured |
| LMArena Vision | Not measured | 1272 | Only Mistral Large 4 measured |
| LMArena Text: English | Not measured | 1433 | Only Mistral Large 4 measured |
| LMArena Text: non-English | Not measured | 1422 | Only Mistral Large 4 measured |
| LMArena Text: Chinese | Not measured | 1492 | Only Mistral Large 4 measured |
| LMArena Text: Japanese | Not measured | 1415 | Only Mistral Large 4 measured |
| LMArena Text: French | Not measured | 1492 | Only Mistral Large 4 measured |
| LMArena Text: Spanish | Not measured | 1455 | Only Mistral Large 4 measured |
| LMArena Text: Russian | Not measured | 1417 | Only Mistral Large 4 measured |
| LMArena Text: multi-turn | Not measured | 1435 | Only Mistral Large 4 measured |
| LMArena Text: longer queries | Not measured | 1428 | Only Mistral Large 4 measured |
| LMArena Text: writing and language | Not measured | 1386 | Only Mistral Large 4 measured |
| LMArena Text: entertainment and media | Not measured | 1378 | Only Mistral Large 4 measured |
| LMArena Vision: English | Not measured | 1283 | Only Mistral Large 4 measured |
| LMArena Vision: reading text | Not measured | 1284 | Only Mistral Large 4 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Max | Mistral Large 4 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Mistral Large 4 costs less
Repository review
50K input + 3K output tokens
Mistral Large 4 costs less
Agent loop
200K input + 10K output tokens
Mistral Large 4 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.1-Codex-Max | Mistral Large 4 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | December 4, 2025 | October 6, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.25 | $1.36 |
| Output $/Mtok | $10.00 | $4.18 |
| Coding index | Not benchmarked | 56.4 |
| Independent measurements behind it | 1 | 5 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max better than Mistral Large 4 for coding?
Only Mistral Large 4 has a coding score, 56.4 on the Vibe Coding Index; GPT-5.1-Codex-Max has none, so the two cannot be ranked on coding. GPT-5.1-Codex-Max has the edge where the benchmark that matters is ALE-Bench, where it leads.
Which is cheaper, GPT-5.1-Codex-Max or Mistral Large 4?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Mistral Large 4 costs $1.36 and $4.18. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Max or Mistral Large 4?
Mistral Large 4, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.1-Codex-Max vs Mistral Large 4 comparison based on?
3 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex-Max's coding index rests on 1 independent benchmark and Mistral Large 4's on 5. Where only one model has a result, the page says so instead of naming a winner.