Codestral 2508 vs GPT-6.1 Sol
Codestral 2508 costs 11.1x less per output token than GPT-6.1 Sol ($0.90 vs $10.00 per million). Updated October 21, 2026; 7 benchmarks have a result for both.
Codestral 2508 or GPT-6.1 Sol: which to pick
Only GPT-6.1 Sol has a coding score, 82.4 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding.
Pick Codestral 2508 for output tokens 11.1x cheaper ($0.90 vs $10.00 per million).
- Shared results
- 7
- Codestral 2508 only
- 0
- GPT-6.1 Sol only
- 89
- Like-for-like categories
- 2 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
Only GPT-6.1 Sol has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-6.1 Sol has a AA Intelligence Index result, 51.8.
Directional only
Web design
Design Arena: websites
GPT-6.1 Sol
GPT-6.1 Sol leads, 1321 against 1013.
Same board, both measured
General
Vals Index
No call
Only GPT-6.1 Sol has a Vals Index result, 63.9%.
Directional only
Long documents
Listed context window
GPT-6.1 Sol
GPT-6.1 Sol takes 1.05M tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, GPT-6.1 Sol $0.0070 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, GPT-6.1 Sol 82.4 from 5 benchmarks.
- Widest category
- Coding, 86.9 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Codestral 2508 137.8 against GPT-6.1 Sol 2743.8, 86.9 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GPT-6.1 Sol $10.00 per million output tokens, 11.1x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-6.1 Sol 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-6.1 Sol | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Web design | 0 | 6 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Codestral 2508 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 96.0% | Only GPT-6.1 Sol measured |
| LMArena WebDev | Not measured | 1753 | Only GPT-6.1 Sol measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 55.0% | Only GPT-6.1 Sol measured |
| Vibe Code Bench (Vals AI) | Not measured | 88.9% | Only GPT-6.1 Sol measured |
| Code migration (Vals AI) | Not measured | 65.1% | Only GPT-6.1 Sol measured |
| ProgramBench (Vals AI) | Not measured | 3.0% | Only GPT-6.1 Sol measured |
| IOI (Vals AI) | Not measured | 96.9% | Only GPT-6.1 Sol measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 43.5% | Only GPT-6.1 Sol measured |
| CyberBench (Vals AI) | Not measured | 39.3% | Only GPT-6.1 Sol measured |
| SciCode | Not measured | 55.8% | Only GPT-6.1 Sol measured |
| FrontierCode | Not measured | 50.2% | Only GPT-6.1 Sol measured |
| ALE-Bench | 137.8 | 2743.8 | GPT-6.1 Sol |
| LMArena Text: coding | Not measured | 1478 | Only GPT-6.1 Sol measured |
| LMArena Text: software and IT | Not measured | 1476 | Only GPT-6.1 Sol measured |
| LMArena WebDev: React | Not measured | 1754 | Only GPT-6.1 Sol measured |
| LMArena WebDev: HTML | Not measured | 1800 | Only GPT-6.1 Sol measured |
| LMArena WebDev: from an image | Not measured | 1702 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 51.8 | Only GPT-6.1 Sol measured |
| Epoch Capabilities Index | Not measured | 168.4 | Only GPT-6.1 Sol measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | Codestral 2508 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1306 | Only GPT-6.1 Sol measured |
| Design Arena: agents building mobile apps | Not measured | 1204 | Only GPT-6.1 Sol measured |
| Design Arena: spreadsheets | Not measured | 1242 | Only GPT-6.1 Sol measured |
| LiveBench research | Not measured | 83.5% | Only GPT-6.1 Sol measured |
| LiveBench browser | Not measured | 88.0% | Only GPT-6.1 Sol measured |
| LiveBench long agentic | Not measured | 73.0% | Only GPT-6.1 Sol measured |
| SREBench (Vals AI) | Not measured | 50.8% | Only GPT-6.1 Sol measured |
| Finance Agent (Vals AI) | Not measured | 52.0% | Only GPT-6.1 Sol measured |
| Tax Agent Bench (Vals AI) | Not measured | 21.8% | Only GPT-6.1 Sol measured |
| Legal Research Bench (Vals AI) | Not measured | 38.5% | Only GPT-6.1 Sol measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 6.3% | Only GPT-6.1 Sol measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 70.8% | Only GPT-6.1 Sol measured |
| BioMysteryBench (Vals AI) | Not measured | 79.6% | Only GPT-6.1 Sol measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 52.9% | Only GPT-6.1 Sol measured |
| Vals Science Index | Not measured | 59.6% | Only GPT-6.1 Sol measured |
| Robo-bench (Vals AI) | Not measured | 28.3% | Only GPT-6.1 Sol measured |
| APEX-Agents | Not measured | 60.0% | Only GPT-6.1 Sol measured |
| SpireClimb | Not measured | 24.7% | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | Codestral 2508 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 92.6% | Only GPT-6.1 Sol measured |
| MysteryMechanism (Vals AI) | Not measured | 46.4% | Only GPT-6.1 Sol measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 52.9% | Only GPT-6.1 Sol measured |
| GPQA Diamond (Epoch AI run) | Not measured | 95.4% | Only GPT-6.1 Sol measured |
| ARC-AGI | Not measured | 98.5% | Only GPT-6.1 Sol measured |
| ARC-AGI-2 | Not measured | 94.2% | Only GPT-6.1 Sol measured |
| Chess puzzles | Not measured | 68.0% | Only GPT-6.1 Sol measured |
| Mystery game puzzles | Not measured | 80.0% | Only GPT-6.1 Sol measured |
| EBR-bench | Not measured | 54.3% | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts | Not measured | 1463 | Only GPT-6.1 Sol measured |
| LMArena Text: expert | Not measured | 1489 | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts in English | Not measured | 1465 | Only GPT-6.1 Sol measured |
| LMArena Vision: diagrams | Not measured | 1306 | Only GPT-6.1 Sol measured |
| LMArena Vision: homework | Not measured | 1356 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Codestral 2508 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 100.0% | Only GPT-6.1 Sol measured |
| FrontierMath, tiers 1 to 3 | Not measured | 93.7% | Only GPT-6.1 Sol measured |
| FrontierMath, tier 4 | Not measured | 100.0% | Only GPT-6.1 Sol measured |
| ProofBench | Not measured | 99.0% | Only GPT-6.1 Sol measured |
| LMArena Text: math | Not measured | 1489 | Only GPT-6.1 Sol measured |
| LMArena Text: mathematical work | Not measured | 1513 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Codestral 2508 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 48.8% | Only GPT-6.1 Sol measured |
| SimpleQA Verified | Not measured | 73.9% | Only GPT-6.1 Sol measured |
| LMArena Text: science | Not measured | 1444 | Only GPT-6.1 Sol measured |
| LMArena Text: medicine and healthcare | Not measured | 1433 | Only GPT-6.1 Sol measured |
| LMArena Text: legal and government | Not measured | 1471 | Only GPT-6.1 Sol measured |
| LMArena Text: business and finance | Not measured | 1445 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Codestral 2508 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 1321 | GPT-6.1 Sol |
| Design Arena: UI components | 1017 | 1330 | GPT-6.1 Sol |
| Design Arena: game dev | 968 | 1389 | GPT-6.1 Sol |
| Design Arena: data viz | 1017 | 1280 | GPT-6.1 Sol |
| Design Arena: 3D | 1020 | 1463 | GPT-6.1 Sol |
| Design Arena: all code categories | 1007 | 1346 | GPT-6.1 Sol |
| LiveBench design | Not measured | 82.1% | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index26 boards, none shared
| Benchmark | Codestral 2508 | GPT-6.1 Sol | Leader |
|---|---|---|---|
| Vals Index | Not measured | 63.9% | Only GPT-6.1 Sol measured |
| LiveBench data analysis | Not measured | 82.7% | Only GPT-6.1 Sol measured |
| Public Benefits Bench (Vals AI) | Not measured | 59.3% | Only GPT-6.1 Sol measured |
| SAGE (Vals AI) | Not measured | 46.5% | Only GPT-6.1 Sol measured |
| MedScribe (Vals AI) | Not measured | 86.5% | Only GPT-6.1 Sol measured |
| Long context reasoning (AA-LCR) | Not measured | 84.0% | Only GPT-6.1 Sol measured |
| DTBench | Not measured | 96.5% | Only GPT-6.1 Sol measured |
| LMCA | Not measured | 62.8% | Only GPT-6.1 Sol measured |
| Furniture assembly | Not measured | 80.0% | Only GPT-6.1 Sol measured |
| LMArena Text | Not measured | 1443 | Only GPT-6.1 Sol measured |
| LMArena Text: instruction following | Not measured | 1466 | Only GPT-6.1 Sol measured |
| LMArena Text: creative writing | Not measured | 1425 | Only GPT-6.1 Sol measured |
| LMArena Vision | Not measured | 1288 | Only GPT-6.1 Sol measured |
| LMArena Text: English | Not measured | 1450 | Only GPT-6.1 Sol measured |
| LMArena Text: non-English | Not measured | 1433 | Only GPT-6.1 Sol measured |
| LMArena Text: Chinese | Not measured | 1487 | Only GPT-6.1 Sol measured |
| LMArena Text: French | Not measured | 1460 | Only GPT-6.1 Sol measured |
| LMArena Text: Spanish | Not measured | 1439 | Only GPT-6.1 Sol measured |
| LMArena Text: Russian | Not measured | 1457 | Only GPT-6.1 Sol measured |
| LMArena Text: multi-turn | Not measured | 1442 | Only GPT-6.1 Sol measured |
| LMArena Text: longer queries | Not measured | 1458 | Only GPT-6.1 Sol measured |
| LMArena Text: writing and language | Not measured | 1444 | Only GPT-6.1 Sol measured |
| LMArena Text: entertainment and media | Not measured | 1426 | Only GPT-6.1 Sol measured |
| LMArena Vision: English | Not measured | 1274 | Only GPT-6.1 Sol measured |
| LMArena Vision: reading text | Not measured | 1289 | Only GPT-6.1 Sol measured |
| LMArena Vision: creative writing | Not measured | 1301 | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GPT-6.1 Sol | 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-6.1 Sol |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | September 29, 2026 |
| Context window | 256K | 1.05M |
| Input $/Mtok | $0.30 | $2.00 |
| Output $/Mtok | $0.90 | $10.00 |
| Coding index | Not benchmarked | 82.4 |
| Independent measurements behind it | 0 | 5 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than GPT-6.1 Sol for coding?
Only GPT-6.1 Sol has a coding score, 82.4 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding. Pick Codestral 2508 for output tokens 11.1x cheaper ($0.90 vs $10.00 per million).
Which is cheaper, Codestral 2508 or GPT-6.1 Sol?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-6.1 Sol costs $2.00 and $10.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or GPT-6.1 Sol?
GPT-6.1 Sol, with 1.05M tokens against 256K.
How many benchmarks is the Codestral 2508 vs GPT-6.1 Sol comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 independent benchmarks and GPT-6.1 Sol's on 5. Where only one model has a result, the page says so instead of naming a winner.