Codestral 2508 vs GPT-4o
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
Neither model has a coding score yet, so the two cannot be ranked on coding.
Codestral 2508 has the edge where output tokens cost 11.1x less, and where the context window is larger, 256K against 128K tokens.
- Shared results
- 6
- Codestral 2508 only
- 1
- GPT-4o only
- 20
- Like-for-like categories
- 1 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
Neither has a coding score yet.
One side not measured
Web design
Design Arena: websites
Codestral 2508
Codestral 2508 leads, 1013 against 831.
Same board, both measured
Long documents
Listed context window
Codestral 2508
Codestral 2508 takes 256K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, GPT-4o $0.0075 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, GPT-4o not benchmarked.
- Widest category
- Web design, 17.3 points apart on average over 6 shared benchmarks.
- Widest single gap
- Design Arena: websites: Codestral 2508 1013 against GPT-4o 831, 22.8 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GPT-4o $10.00 per million output tokens, 11.1x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-4o 128K.
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-4o | Ties |
|---|---|---|---|---|
| Web design | 6 | 0 | ||
| All shared boards | 6 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | Codestral 2508 | GPT-4o | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 38.8% | Only GPT-4o measured |
| SWE-bench Lite | Not measured | 38.0% | Only GPT-4o measured |
| SWE-bench (full test set) | Not measured | 18.8% | Only GPT-4o measured |
| SWE-bench Multimodal | Not measured | 30.4% | Only GPT-4o measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 30.9% | Only GPT-4o measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 8.3% | Only GPT-4o measured |
| ALE-Bench | 137.8 | Not measured | Only Codestral 2508 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GPT-4o | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-4o | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 25.1% | Only GPT-4o measured |
| GDPval | Not measured | 9.9% | Only GPT-4o measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Codestral 2508 | GPT-4o | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 54.3% | Only GPT-4o measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 2.4% | Only GPT-4o measured |
| ARC-AGI | Not measured | 4.5% | Only GPT-4o measured |
| ARC-AGI-2 | Not measured | 0.0% | Only GPT-4o measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-4o | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 6.0 | Only GPT-4o measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 6.0% | Only GPT-4o measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-4o | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 74.8% | Only GPT-4o measured |
| MMLU | Not measured | 88.1% | Only GPT-4o measured |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Codestral 2508 | GPT-4o | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 831 | Codestral 2508 |
| Design Arena: UI components | 1017 | 885 | Codestral 2508 |
| Design Arena: game dev | 968 | 906 | Codestral 2508 |
| Design Arena: data viz | 1017 | 858 | Codestral 2508 |
| Design Arena: 3D | 1020 | 869 | Codestral 2508 |
| Design Arena: all code categories | 1007 | 861 | Codestral 2508 |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Codestral 2508 | GPT-4o | Leader |
|---|---|---|---|
| IFBench | Not measured | 34.3% | Only GPT-4o measured |
| Long context reasoning (AA-LCR) | Not measured | 49.3% | Only GPT-4o measured |
| DTBench | Not measured | 64.5% | Only GPT-4o measured |
| LMCA | Not measured | 16.6% | Only GPT-4o 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 | Codestral 2508 | GPT-4o | 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-4o |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | May 13, 2024 |
| Context window | 256K | 128K |
| Input $/Mtok | $0.30 | $2.50 |
| Output $/Mtok | $0.90 | $10.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than GPT-4o for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Codestral 2508 has the edge where output tokens cost 11.1x less, and where the context window is larger, 256K against 128K tokens.
Which is cheaper, Codestral 2508 or GPT-4o?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-4o costs $2.50 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or GPT-4o?
Codestral 2508, with 256K tokens against 128K.
How many benchmarks is the Codestral 2508 vs GPT-4o 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-4o's on 0. Where only one model has a result, the page says so instead of naming a winner.