GPT-5.1-Codex-Mini vs Mistral Large 2407
GPT-5.1-Codex-Mini leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 20.4 to 7.6, and GPT-5.1-Codex-Mini costs 3.0x less per output token ($2.00 vs $6.00 per million). Updated October 21, 2026; 10 benchmarks have a result for both.
GPT-5.1-Codex-Mini or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Mini for output tokens 3.0x cheaper ($2.00 vs $6.00 per million) and a larger context window (400K against 131K tokens).
- Shared results
- 10
- GPT-5.1-Codex-Mini only
- 12
- Mistral Large 2407 only
- 7
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 against 7.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex-Mini has a Design Arena: websites result, 1111.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini $0.0013, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Math, 91.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Math Index: GPT-5.1-Codex-Mini 91.7 against Mistral Large 2407 0.0, 91.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Mistral Large 2407 $6.00 per million output tokens, 3.0x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Mistral Large 2407 131K.
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-Mini | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 10 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | Not measured | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | 26.7% | GPT-5.1-Codex-Mini |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| LMArena WebDev: HTML | 1253 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | 7.6 | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | 33.0% | GPT-5.1-Codex-Mini |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | 47.2% | GPT-5.1-Codex-Mini |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | 2.9% | GPT-5.1-Codex-Mini |
| GPQA Diamond (Epoch AI run) | Not measured | 49.0% | Only Mistral Large 2407 measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | 91.7 | 0.0 | GPT-5.1-Codex-Mini |
| AIME 2025 (Artificial Analysis run) | 91.7% | 0.0% | GPT-5.1-Codex-Mini |
| OTIS Mock AIME | Not measured | 8.5% | Only Mistral Large 2407 measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | 68.3% | GPT-5.1-Codex-Mini |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: UI components | 1081 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: game dev | 1092 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: data viz | 1097 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: 3D | 988 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: all code categories | 1094 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: SVG | 978 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | 1105 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | 67.9% | 31.6% | GPT-5.1-Codex-Mini |
| Long context reasoning (AA-LCR) | 66.7% | 2.0% | GPT-5.1-Codex-Mini |
| DTBench | Not measured | 61.2% | Only Mistral Large 2407 measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Mistral Large 2407 | 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-Mini costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex-Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex-Mini 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-Mini | Mistral Large 2407 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | November 19, 2024 |
| Context window | 400K | 131K |
| Input $/Mtok | $0.25 | $2.00 |
| Output $/Mtok | $2.00 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Mini better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Mini for output tokens 3.0x cheaper ($2.00 vs $6.00 per million) and a larger context window (400K against 131K tokens).
Which is cheaper, GPT-5.1-Codex-Mini or Mistral Large 2407?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Mistral Large 2407?
GPT-5.1-Codex-Mini, with 400K tokens against 131K.
How many benchmarks is the GPT-5.1-Codex-Mini vs Mistral Large 2407 comparison based on?
10 benchmarks have a result for both models as of October 21, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark and Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.