GPT-5.1-Codex vs Mistral Large 4
Updated October 21, 2026. 12 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 12 shared
Decision reading
Pick Mistral Large 4 for coding: it scores 55.3 on the Vibe Coding Index against 50.5 for GPT-5.1-Codex.
Pick GPT-5.1-Codex when the benchmark that matters is ALE-Bench, where it leads.
Model AGPT-5.1-CodexOpenAI50.5/1002 independent benchmarks$0.892 per solved taskModel BMistral Large 4Mistral AI55.3/1002 independent benchmarksNo cost per solved task
- Shared results
- 12
- GPT-5.1-Codex only
- 14
- Mistral Large 4 only
- 69
- Like-for-like categories
- 5 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, four public coding benchmarks
Mistral Large 4
Mistral Large 4 scores 55.3 against 50.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
Mistral Large 4
Mistral Large 4 leads, 38.4 against 23.7.
Same board, both measured
Web design
Design Arena: websites
Mistral Large 4
Mistral Large 4 leads, 1258 against 1162.
Same board, both measured
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 $0.0063, Mistral Large 4 $0.0034 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.1-Codex has the number, $0.892.
Directional only
At a glance
- Coding index
- GPT-5.1-Codex 50.5 from 2 benchmarks, Mistral Large 4 55.3 from 2 benchmarks.
- Widest category
- Coding, 29.8 points apart on average over 4 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-5.1-Codex 13.1% against Mistral Large 4 78.4%, 65.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex $10.00 against Mistral Large 4 $4.18 per million output tokens, 2.4x apart.
- Context window
- GPT-5.1-Codex 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 | Bars | Mistral Large 4 | Ties |
|---|---|---|---|---|
| Coding | 1 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Web design | 0 | 5 | ||
| General | 0 | 1 | ||
| All shared boards | 1 | 11 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index13
| Benchmark | GPT-5.1-Codex | Mistral Large 4 | Leader |
|---|---|---|---|
| SWE-bench Verified | 66.0% | Not measured | Only GPT-5.1-Codex measured |
| LiveBench coding | Not measured | 60.0% | Only Mistral Large 4 measured |
| LMArena WebDev | 1337 | 1543 | Mistral Large 4 |
| LiveCodeBench (Vals AI) | 85.5% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 27.8% | Only Mistral Large 4 measured |
| Vibe Code Bench (Vals AI) | 13.1% | 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 |
| LiveCodeBench (Artificial Analysis run) | 84.9% | Not measured | Only GPT-5.1-Codex measured |
| SciCode | Not measured | 54.2% | Only Mistral Large 4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | Not measured | Only GPT-5.1-Codex measured |
| Terminal-Bench | 57.8% | Not measured | Only GPT-5.1-Codex measured |
| FrontierCode | Not measured | 39.4% | Only Mistral Large 4 measured |
| ALE-Bench | 1244.9 | 1213.2 | GPT-5.1-Codex |
| 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 | 1346 | 1487 | Mistral Large 4 |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.7 | 38.4 | Mistral Large 4 |
| Epoch Capabilities Index | Not measured | 150.0 | Only Mistral Large 4 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Large 4 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1018 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | 1086 | Not measured | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | 1191 | Not measured | Only GPT-5.1-Codex measured |
| 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 |
| τ²-Bench Telecom | 83.0% | Not measured | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex | 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 |
| GPQA Diamond (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 25.7% | 35.0% | Mistral Large 4 |
| 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 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Math Index | 95.7 | Not measured | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | 95.7% | Not measured | Only GPT-5.1-Codex measured |
| 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 | Not measured | 10.0% | Only Mistral Large 4 measured |
| 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 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.1-Codex | Mistral Large 4 | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 40.7% | Only Mistral Large 4 measured |
| MMLU Pro (Artificial Analysis run) | 86.0% | Not measured | Only GPT-5.1-Codex 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 10 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-5.1-Codex | Mistral Large 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | 1258 | Mistral Large 4 |
| Design Arena: UI components | 1237 | 1275 | Mistral Large 4 |
| Design Arena: game dev | 1138 | 1226 | Mistral Large 4 |
| Design Arena: data viz | 1213 | 1274 | Mistral Large 4 |
| Design Arena: 3D | Not measured | 1253 | Only Mistral Large 4 measured |
| Design Arena: all code categories | 1153 | 1259 | Mistral Large 4 |
| Design Arena: SVG | Not measured | 1232 | 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 |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex | 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 |
| IFBench | 70.0% | Not measured | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | 69.3% | 81.3% | Mistral Large 4 |
| 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 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex | 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
GPT-5.1-Codex$0.0063Fits in one request
Mistral Large 4$0.0034Fits in one request
Mistral Large 4 costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex$0.092Fits in one request
Mistral Large 4$0.081Fits in one request
Mistral Large 4 costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex$0.350Fits in one request
Mistral Large 4$0.314Fits in one request
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 | Mistral Large 4 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | October 6, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.25 | $1.36 |
| Output $/Mtok | $10.00 | $4.18 |
| Coding index | 50.5 | 55.3 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.892 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex better than Mistral Large 4 for coding?
Pick Mistral Large 4 for coding: it scores 55.3 on the Vibe Coding Index against 50.5 for GPT-5.1-Codex. Pick GPT-5.1-Codex when the benchmark that matters is ALE-Bench, where it leads.
Which is cheaper, GPT-5.1-Codex or Mistral Large 4?
GPT-5.1-Codex 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 GPT-5.1-Codex only.
Which has the bigger context window, GPT-5.1-Codex or Mistral Large 4?
Mistral Large 4, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.1-Codex vs Mistral Large 4 comparison based on?
12 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex's coding index rests on 2 independent benchmarks and Mistral Large 4's on 2. Where only one model has a result, the page says so instead of naming a winner.