Codestral 2508 vs GPT-5.1-Codex
Updated October 21, 2026. 6 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding.
Codestral 2508 has the edge where output tokens cost 11.1x less.
- Shared results
- 6
- Codestral 2508 only
- 1
- GPT-5.1-Codex only
- 20
- 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 GPT-5.1-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.1-Codex has a AA Intelligence Index result, 23.7.
Directional only
Web design
Design Arena: websites
GPT-5.1-Codex
GPT-5.1-Codex leads, 1162 against 1013.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex
GPT-5.1-Codex takes 400K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, GPT-5.1-Codex $0.0063 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
- Codestral 2508 not benchmarked, GPT-5.1-Codex 15.3 from 2 benchmarks.
- Widest category
- Coding, 36.9 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Codestral 2508 137.8 against GPT-5.1-Codex 1244.9, 36.9 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GPT-5.1-Codex $10.00 per million output tokens, 11.1x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-5.1-Codex 400K.
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 | Codestral 2508 | Bars | GPT-5.1-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Web design | 0 | 5 | ||
| All shared boards | 0 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Codestral 2508 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 66.0% | Only GPT-5.1-Codex measured |
| LMArena WebDev | Not measured | 1337 | Only GPT-5.1-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 85.5% | Only GPT-5.1-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 13.1% | Only GPT-5.1-Codex measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 84.9% | Only GPT-5.1-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only GPT-5.1-Codex measured |
| Terminal-Bench | Not measured | 57.8% | Only GPT-5.1-Codex measured |
| ALE-Bench | 137.8 | 1244.9 | GPT-5.1-Codex |
| LMArena WebDev: HTML | Not measured | 1346 | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 23.7 | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1018 | Only GPT-5.1-Codex measured |
| Design Arena: agents building full-stack apps | Not measured | 1086 | Only GPT-5.1-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1191 | Only GPT-5.1-Codex measured |
| τ²-Bench Telecom | Not measured | 83.0% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 86.0% | Only GPT-5.1-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 25.7% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 95.7 | Only GPT-5.1-Codex measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 95.7% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 86.0% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 16 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Codestral 2508 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 1162 | GPT-5.1-Codex |
| Design Arena: UI components | 1017 | 1237 | GPT-5.1-Codex |
| Design Arena: game dev | 968 | 1138 | GPT-5.1-Codex |
| Design Arena: data viz | 1017 | 1213 | GPT-5.1-Codex |
| Design Arena: 3D | 1020 | Not measured | Only Codestral 2508 measured |
| Design Arena: all code categories | 1007 | 1152 | GPT-5.1-Codex |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.1-Codex | Leader |
|---|---|---|---|
| IFBench | Not measured | 70.0% | Only GPT-5.1-Codex measured |
| Long context reasoning (AA-LCR) | Not measured | 69.3% | Only GPT-5.1-Codex measured |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.1-Codex | 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
Codestral 2508 costs less
Repository review
50K input + 3K output tokens
Codestral 2508 costs less
Agent loop
200K input + 10K output tokens
Codestral 2508 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 | Mistral Codestral 2508 | OpenAI GPT-5.1-Codex |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | November 13, 2025 |
| Context window | 256K | 400K |
| Input $/Mtok | $0.30 | $1.25 |
| Output $/Mtok | $0.90 | $10.00 |
| Coding index | Not benchmarked | 15.3 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.892 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than GPT-5.1-Codex for coding?
Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding. Codestral 2508 has the edge where output tokens cost 11.1x less.
Which is cheaper, Codestral 2508 or GPT-5.1-Codex?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-5.1-Codex costs $1.25 and $10.00. Cost per solved task is known for GPT-5.1-Codex only.
Which has the bigger context window, Codestral 2508 or GPT-5.1-Codex?
GPT-5.1-Codex, with 400K tokens against 256K.
How many benchmarks is the Codestral 2508 vs GPT-5.1-Codex comparison based on?
6 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 independent benchmarks and GPT-5.1-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.