Codestral 2508 vs GPT-5 Mini
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 Mini has a coding score, 34.2 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 2.2x less.
- Shared results
- 6
- Codestral 2508 only
- 1
- GPT-5 Mini only
- 92
- 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
Only GPT-5 Mini has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5 Mini has a AA Intelligence Index result, 20.6.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5 Mini has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only GPT-5 Mini has a AIME result, 91.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 Mini has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
GPT-5 Mini
GPT-5 Mini leads, 1125 against 1013.
Same board, both measured
Long documents
Listed context window
GPT-5 Mini
GPT-5 Mini takes 400K tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, GPT-5 Mini $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 Mini has the number, $0.08.
Directional only
At a glance
- Coding index
- Codestral 2508 not benchmarked, GPT-5 Mini 34.2 from 2 benchmarks.
- Widest category
- Web design, 12.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- Design Arena: game dev: Codestral 2508 968 against GPT-5 Mini 1129, 20.1 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GPT-5 Mini $2.00 per million output tokens, 2.2x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-5 Mini 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 Mini | Ties |
|---|---|---|---|---|
| Web design | 0 | 6 | ||
| All shared boards | 0 | 6 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | Codestral 2508 | GPT-5 Mini | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 59.8% | Only GPT-5 Mini measured |
| SWE-bench Multilingual | Not measured | 39.7% | Only GPT-5 Mini measured |
| LiveCodeBench (Vals AI) | Not measured | 86.6% | Only GPT-5 Mini measured |
| SWE-bench Verified (Vals AI) | Not measured | 60.8% | Only GPT-5 Mini measured |
| Vibe Code Bench (Vals AI) | Not measured | 14.2% | Only GPT-5 Mini measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 83.8% | Only GPT-5 Mini measured |
| SciCode | Not measured | 39.0% | Only GPT-5 Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only GPT-5 Mini measured |
| Terminal-Bench | Not measured | 34.8% | Only GPT-5 Mini measured |
| WeirdML | Not measured | 52.7% | Only GPT-5 Mini measured |
| ALE-Bench | 137.8 | Not measured | Only Codestral 2508 measured |
| LMArena Text: coding | Not measured | 1406 | Only GPT-5 Mini measured |
| LMArena Text: software and IT | Not measured | 1394 | Only GPT-5 Mini measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-5 Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 20.6 | Only GPT-5 Mini measured |
| Epoch Capabilities Index | Not measured | 145.3 | Only GPT-5 Mini measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-5 Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 71.1% | Only GPT-5 Mini measured |
| Vending-Bench 2 | Not measured | -$31 | Only GPT-5 Mini measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | Codestral 2508 | GPT-5 Mini | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 80.3% | Only GPT-5 Mini measured |
| MMMU Pro (Vals AI) | Not measured | 78.9% | Only GPT-5 Mini measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 82.8% | Only GPT-5 Mini measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 21.5% | Only GPT-5 Mini measured |
| GPQA Diamond (Epoch AI run) | Not measured | 75.0% | Only GPT-5 Mini measured |
| ARC-AGI | Not measured | 54.3% | Only GPT-5 Mini measured |
| ARC-AGI-2 | Not measured | 4.4% | Only GPT-5 Mini measured |
| Chess puzzles | Not measured | 24.0% | Only GPT-5 Mini measured |
| Mystery game puzzles | Not measured | 10.0% | Only GPT-5 Mini measured |
| VPCT | Not measured | 40.2% | Only GPT-5 Mini measured |
| LMArena Text: hard prompts | Not measured | 1380 | Only GPT-5 Mini measured |
| LMArena Text: expert | Not measured | 1379 | Only GPT-5 Mini measured |
| LMArena Text: hard prompts in English | Not measured | 1383 | Only GPT-5 Mini measured |
| LMArena Vision: diagrams | Not measured | 1202 | Only GPT-5 Mini measured |
| LMArena Vision: homework | Not measured | 1210 | Only GPT-5 Mini measured |
| Neither model has a result on the other 6 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | Codestral 2508 | GPT-5 Mini | Leader |
|---|---|---|---|
| AIME | Not measured | 91.5% | Only GPT-5 Mini measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only GPT-5 Mini measured |
| MGSM (Vals AI) | Not measured | 92.6% | Only GPT-5 Mini measured |
| AA Math Index | Not measured | 90.7 | Only GPT-5 Mini measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 90.7% | Only GPT-5 Mini measured |
| OTIS Mock AIME | Not measured | 86.7% | Only GPT-5 Mini measured |
| FrontierMath, tiers 1 to 3 | Not measured | 46.7% | Only GPT-5 Mini measured |
| FrontierMath, tier 4 | Not measured | 12.2% | Only GPT-5 Mini measured |
| ProofBench | Not measured | 9.0% | Only GPT-5 Mini measured |
| MATH level 5 | Not measured | 97.8% | Only GPT-5 Mini measured |
| LMArena Text: math | Not measured | 1378 | Only GPT-5 Mini measured |
| LMArena Text: mathematical work | Not measured | 1380 | Only GPT-5 Mini measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | Codestral 2508 | GPT-5 Mini | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.2% | Only GPT-5 Mini measured |
| LegalBench (Vals AI) | Not measured | 81.8% | Only GPT-5 Mini measured |
| CaseLaw (Vals AI) | Not measured | 68.5% | Only GPT-5 Mini measured |
| CorpFin (Vals AI) | Not measured | 60.2% | Only GPT-5 Mini measured |
| TaxEval (Vals AI) | Not measured | 75.2% | Only GPT-5 Mini measured |
| MortgageTax (Vals AI) | Not measured | 66.9% | Only GPT-5 Mini measured |
| MedQA (Vals AI) | Not measured | 96.1% | Only GPT-5 Mini measured |
| MedCode (Vals AI) | Not measured | 43.0% | Only GPT-5 Mini measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 83.7% | Only GPT-5 Mini measured |
| SimpleQA Verified | Not measured | 21.6% | Only GPT-5 Mini measured |
| LMArena Text: science | Not measured | 1371 | Only GPT-5 Mini measured |
| LMArena Text: medicine and healthcare | Not measured | 1380 | Only GPT-5 Mini measured |
| LMArena Text: legal and government | Not measured | 1375 | Only GPT-5 Mini measured |
| LMArena Text: business and finance | Not measured | 1358 | Only GPT-5 Mini measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Codestral 2508 | GPT-5 Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 1125 | GPT-5 Mini |
| Design Arena: UI components | 1017 | 1106 | GPT-5 Mini |
| Design Arena: game dev | 968 | 1129 | GPT-5 Mini |
| Design Arena: data viz | 1017 | 1130 | GPT-5 Mini |
| Design Arena: 3D | 1020 | 1037 | GPT-5 Mini |
| Design Arena: all code categories | 1007 | 1115 | GPT-5 Mini |
| Design Arena: SVG | Not measured | 1091 | Only GPT-5 Mini measured |
| Design Arena: ASCII art | Not measured | 1124 | Only GPT-5 Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index33 boards, none shared
| Benchmark | Codestral 2508 | GPT-5 Mini | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 43.0% | Only GPT-5 Mini measured |
| MedScribe (Vals AI) | Not measured | 80.6% | Only GPT-5 Mini measured |
| IFBench | Not measured | 75.4% | Only GPT-5 Mini measured |
| Long context reasoning (AA-LCR) | Not measured | 72.3% | Only GPT-5 Mini measured |
| DTBench | Not measured | 80.5% | Only GPT-5 Mini measured |
| LMCA | Not measured | 34.2% | Only GPT-5 Mini measured |
| Fiction.LiveBench | Not measured | 69.4% | Only GPT-5 Mini measured |
| Creative writing (Lech Mazur) | Not measured | 83.1% | Only GPT-5 Mini measured |
| LMArena Text | Not measured | 1373 | Only GPT-5 Mini measured |
| LMArena Text: instruction following | Not measured | 1357 | Only GPT-5 Mini measured |
| LMArena Text: creative writing | Not measured | 1325 | Only GPT-5 Mini measured |
| LMArena Vision | Not measured | 1202 | Only GPT-5 Mini measured |
| LMArena Text: English | Not measured | 1379 | Only GPT-5 Mini measured |
| LMArena Text: non-English | Not measured | 1363 | Only GPT-5 Mini measured |
| LMArena Text: Chinese | Not measured | 1386 | Only GPT-5 Mini measured |
| LMArena Text: German | Not measured | 1366 | Only GPT-5 Mini measured |
| LMArena Text: Japanese | Not measured | 1341 | Only GPT-5 Mini measured |
| LMArena Text: Korean | Not measured | 1308 | Only GPT-5 Mini measured |
| LMArena Text: French | Not measured | 1386 | Only GPT-5 Mini measured |
| LMArena Text: Spanish | Not measured | 1354 | Only GPT-5 Mini measured |
| LMArena Text: Russian | Not measured | 1362 | Only GPT-5 Mini measured |
| LMArena Text: Polish | Not measured | 1378 | Only GPT-5 Mini measured |
| LMArena Text: multi-turn | Not measured | 1363 | Only GPT-5 Mini measured |
| LMArena Text: longer queries | Not measured | 1355 | Only GPT-5 Mini measured |
| LMArena Text: writing and language | Not measured | 1344 | Only GPT-5 Mini measured |
| LMArena Text: entertainment and media | Not measured | 1340 | Only GPT-5 Mini measured |
| LMArena Vision: English | Not measured | 1216 | Only GPT-5 Mini measured |
| LMArena Vision: Chinese | Not measured | 1214 | Only GPT-5 Mini measured |
| LMArena Vision: reading text | Not measured | 1199 | Only GPT-5 Mini measured |
| LMArena Vision: humor | Not measured | 1205 | Only GPT-5 Mini measured |
| LMArena Vision: captioning | Not measured | 1217 | Only GPT-5 Mini measured |
| LMArena Vision: recognizing things | Not measured | 1226 | Only GPT-5 Mini measured |
| LMArena Vision: creative writing | Not measured | 1181 | Only GPT-5 Mini measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GPT-5 Mini | 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 Mini |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | August 7, 2025 |
| Context window | 256K | 400K |
| Input $/Mtok | $0.30 | $0.25 |
| Output $/Mtok | $0.90 | $2.00 |
| Coding index | Not benchmarked | 34.2 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $0.08 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than GPT-5 Mini for coding?
Only GPT-5 Mini has a coding score, 34.2 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 2.2x less.
Which is cheaper, Codestral 2508 or GPT-5 Mini?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-5 Mini costs $0.25 and $2.00. Cost per solved task is known for GPT-5 Mini only.
Which has the bigger context window, Codestral 2508 or GPT-5 Mini?
GPT-5 Mini, with 400K tokens against 256K.
How many benchmarks is the Codestral 2508 vs GPT-5 Mini 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 Mini's on 2. Where only one model has a result, the page says so instead of naming a winner.