Codestral 2508 vs GPT-4.1 Nano
GPT-4.1 Nano costs 2.3x less per output token than Codestral 2508 ($0.40 vs $0.90 per million). Updated October 21, 2026; 6 benchmarks have a result for both.
Codestral 2508 or GPT-4.1 Nano: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Codestral 2508 for work measured by Design Arena: game dev, where it leads.
- Shared results
- 6
- Codestral 2508 only
- 1
- GPT-4.1 Nano only
- 63
- Like-for-like categories
- 1 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
No call
Only GPT-4.1 Nano has a AA Intelligence Index result, 7.8.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4.1 Nano has a GPQA Diamond result, 50.8%.
Directional only
Math
AIME
No call
Only GPT-4.1 Nano has a AIME result, 26.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4.1 Nano has a MMLU Pro result, 63.5%.
Directional only
Web design
Design Arena: websites
Codestral 2508
Codestral 2508 leads, 1013 against 973.
Same board, both measured
Long documents
Listed context window
GPT-4.1 Nano
GPT-4.1 Nano takes 1.05M tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
Codestral 2508 $0.0008, GPT-4.1 Nano $0.0003 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, GPT-4.1 Nano not benchmarked.
- Widest category
- Web design, 8.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- Design Arena: data viz: Codestral 2508 1017 against GPT-4.1 Nano 890, 15.9 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GPT-4.1 Nano $0.40 per million output tokens, 2.3x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-4.1 Nano 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 | Codestral 2508 | Bars | GPT-4.1 Nano | Ties |
|---|---|---|---|---|
| Web design | 5 | 1 | ||
| All shared boards | 5 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | Codestral 2508 | GPT-4.1 Nano | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 8.9% | Only GPT-4.1 Nano measured |
| AA Coding Index | Not measured | 11.1 | Only GPT-4.1 Nano measured |
| LiveCodeBench (Vals AI) | Not measured | 42.7% | Only GPT-4.1 Nano measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 32.6% | Only GPT-4.1 Nano measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.8% | Only GPT-4.1 Nano measured |
| WeirdML | Not measured | 19.0% | Only GPT-4.1 Nano measured |
| ALE-Bench | 137.8 | Not measured | Only Codestral 2508 measured |
| LMArena Text: coding | Not measured | 1306 | Only GPT-4.1 Nano measured |
| LMArena Text: software and IT | Not measured | 1305 | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-4.1 Nano | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.8 | Only GPT-4.1 Nano measured |
| Epoch Capabilities Index | Not measured | 129.5 | Only GPT-4.1 Nano measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Codestral 2508 | GPT-4.1 Nano | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 17.3% | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Codestral 2508 | GPT-4.1 Nano | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 50.8% | Only GPT-4.1 Nano measured |
| MMMU Pro (Vals AI) | Not measured | 55.0% | Only GPT-4.1 Nano measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 51.2% | Only GPT-4.1 Nano measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.8% | Only GPT-4.1 Nano measured |
| GPQA Diamond (Epoch AI run) | Not measured | 48.9% | Only GPT-4.1 Nano measured |
| ARC-AGI | Not measured | 0.0% | Only GPT-4.1 Nano measured |
| ARC-AGI-2 | Not measured | 0.0% | Only GPT-4.1 Nano measured |
| Chess puzzles | Not measured | 0.0% | Only GPT-4.1 Nano measured |
| LMArena Text: hard prompts | Not measured | 1286 | Only GPT-4.1 Nano measured |
| LMArena Text: expert | Not measured | 1271 | Only GPT-4.1 Nano measured |
| LMArena Text: hard prompts in English | Not measured | 1293 | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Codestral 2508 | GPT-4.1 Nano | Leader |
|---|---|---|---|
| AIME | Not measured | 26.5% | Only GPT-4.1 Nano measured |
| MATH 500 (Vals AI) | Not measured | 80.2% | Only GPT-4.1 Nano measured |
| MGSM (Vals AI) | Not measured | 69.3% | Only GPT-4.1 Nano measured |
| AA Math Index | Not measured | 24.0 | Only GPT-4.1 Nano measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 24.0% | Only GPT-4.1 Nano measured |
| OTIS Mock AIME | Not measured | 28.9% | Only GPT-4.1 Nano measured |
| MATH level 5 | Not measured | 70.0% | Only GPT-4.1 Nano measured |
| LMArena Text: math | Not measured | 1274 | Only GPT-4.1 Nano measured |
| LMArena Text: mathematical work | Not measured | 1274 | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | Codestral 2508 | GPT-4.1 Nano | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 63.5% | Only GPT-4.1 Nano measured |
| LegalBench (Vals AI) | Not measured | 61.1% | Only GPT-4.1 Nano measured |
| CorpFin (Vals AI) | Not measured | 42.1% | Only GPT-4.1 Nano measured |
| TaxEval (Vals AI) | Not measured | 60.8% | Only GPT-4.1 Nano measured |
| MortgageTax (Vals AI) | Not measured | 52.8% | Only GPT-4.1 Nano measured |
| MedQA (Vals AI) | Not measured | 68.2% | Only GPT-4.1 Nano measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 65.7% | Only GPT-4.1 Nano measured |
| SimpleQA Verified | Not measured | 6.0% | Only GPT-4.1 Nano measured |
| LMArena Text: science | Not measured | 1297 | Only GPT-4.1 Nano measured |
| LMArena Text: medicine and healthcare | Not measured | 1276 | Only GPT-4.1 Nano measured |
| LMArena Text: legal and government | Not measured | 1300 | Only GPT-4.1 Nano measured |
| LMArena Text: business and finance | Not measured | 1267 | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | Codestral 2508 | GPT-4.1 Nano | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 973 | Codestral 2508 |
| Design Arena: UI components | 1017 | 916 | Codestral 2508 |
| Design Arena: game dev | 968 | 971 | GPT-4.1 Nano |
| Design Arena: data viz | 1017 | 890 | Codestral 2508 |
| Design Arena: 3D | 1020 | 925 | Codestral 2508 |
| Design Arena: all code categories | 1007 | 963 | Codestral 2508 |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
| Benchmark | Codestral 2508 | GPT-4.1 Nano | Leader |
|---|---|---|---|
| IFBench | Not measured | 32.0% | Only GPT-4.1 Nano measured |
| Long context reasoning (AA-LCR) | Not measured | 20.3% | Only GPT-4.1 Nano measured |
| DTBench | Not measured | 52.5% | Only GPT-4.1 Nano measured |
| LMCA | Not measured | 5.5% | Only GPT-4.1 Nano measured |
| Fiction.LiveBench | Not measured | 25.0% | Only GPT-4.1 Nano measured |
| LMArena Text | Not measured | 1285 | Only GPT-4.1 Nano measured |
| LMArena Text: instruction following | Not measured | 1266 | Only GPT-4.1 Nano measured |
| LMArena Text: creative writing | Not measured | 1260 | Only GPT-4.1 Nano measured |
| LMArena Vision | Not measured | 1063 | Only GPT-4.1 Nano measured |
| LMArena Text: English | Not measured | 1303 | Only GPT-4.1 Nano measured |
| LMArena Text: non-English | Not measured | 1260 | Only GPT-4.1 Nano measured |
| LMArena Text: Chinese | Not measured | 1270 | Only GPT-4.1 Nano measured |
| LMArena Text: German | Not measured | 1288 | Only GPT-4.1 Nano measured |
| LMArena Text: Japanese | Not measured | 1198 | Only GPT-4.1 Nano measured |
| LMArena Text: Russian | Not measured | 1261 | Only GPT-4.1 Nano measured |
| LMArena Text: multi-turn | Not measured | 1277 | Only GPT-4.1 Nano measured |
| LMArena Text: longer queries | Not measured | 1283 | Only GPT-4.1 Nano measured |
| LMArena Text: writing and language | Not measured | 1261 | Only GPT-4.1 Nano measured |
| LMArena Text: entertainment and media | Not measured | 1243 | Only GPT-4.1 Nano measured |
| LMArena Vision: English | Not measured | 1101 | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GPT-4.1 Nano | 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-4.1 Nano costs less
Repository review
50K input + 3K output tokens
GPT-4.1 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-4.1 Nano 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-4.1 Nano |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | April 14, 2025 |
| Context window | 256K | 1.05M |
| Input $/Mtok | $0.30 | $0.10 |
| Output $/Mtok | $0.90 | $0.40 |
| 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-4.1 Nano for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Codestral 2508 for work measured by Design Arena: game dev, where it leads.
Which is cheaper, Codestral 2508 or GPT-4.1 Nano?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-4.1 Nano costs $0.10 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or GPT-4.1 Nano?
GPT-4.1 Nano, with 1.05M tokens against 256K.
How many benchmarks is the Codestral 2508 vs GPT-4.1 Nano 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-4.1 Nano's on 0. Where only one model has a result, the page says so instead of naming a winner.