Codestral 2508 vs GPT-5.6 Terra
Codestral 2508 costs 13.3x less per output token than GPT-5.6 Terra ($0.90 vs $12.00 per million). Updated October 21, 2026; 8 benchmarks have a result for both.
vs boards won of 8 shared
Codestral 2508 or GPT-5.6 Terra: which to pick
Only GPT-5.6 Terra has a coding score, 63.9 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding.
Pick Codestral 2508 for output tokens 13.3x cheaper ($0.90 vs $12.00 per million).
Model ACodestral 2508Mistral AINot benchmarkedNo coding benchmarkNo cost per solved taskModel BGPT-5.6 TerraOpenAI63.9/1006 independent benchmarks$0.42 per solved task
- Shared results
- 8
- Codestral 2508 only
- 0
- GPT-5.6 Terra only
- 119
- Like-for-like categories
- 3 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-5.6 Terra has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-5.6 Terra has a AA Intelligence Index result, 42.1.
Directional only
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Terra has a AA Agentic Index result, 43.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Terra has a GPQA Diamond result, 90.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Terra has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
GPT-5.6 Terra
GPT-5.6 Terra leads, 1268 against 1013.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Terra
GPT-5.6 Terra takes 1.05M tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Codestral 2508
Codestral 2508 $0.0008, GPT-5.6 Terra $0.0080 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Terra has the number, $0.42.
Directional only
At a glance
- Coding index
- Codestral 2508 not benchmarked, GPT-5.6 Terra 63.9 from 6 benchmarks.
- Widest category
- Coding, 60.5 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Codestral 2508 137.8 against GPT-5.6 Terra 1951.4, 60.5 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GPT-5.6 Terra $12.00 per million output tokens, 13.3x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-5.6 Terra 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-5.6 Terra | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Web design | 0 | 6 | ||
| All shared boards | 0 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Codestral 2508 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.2% | Only GPT-5.6 Terra measured |
| LMArena WebDev | Not measured | 1522 | Only GPT-5.6 Terra measured |
| AA Coding Index | Not measured | 76.7 | Only GPT-5.6 Terra measured |
| LiveCodeBench (Vals AI) | Not measured | 85.9% | Only GPT-5.6 Terra measured |
| SWE-bench Verified (Vals AI) | Not measured | 95.4% | Only GPT-5.6 Terra measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 22.7% | Only GPT-5.6 Terra measured |
| Vibe Code Bench (Vals AI) | Not measured | 74.6% | Only GPT-5.6 Terra measured |
| Code migration (Vals AI) | Not measured | 47.8% | Only GPT-5.6 Terra measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-5.6 Terra measured |
| IOI (Vals AI) | Not measured | 87.6% | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 77.5% | Only GPT-5.6 Terra measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.8% | Only GPT-5.6 Terra measured |
| SkillsBench (Vals AI) | Not measured | 58.9% | Only GPT-5.6 Terra measured |
| CyberBench (Vals AI) | Not measured | 72.1% | Only GPT-5.6 Terra measured |
| SciCode | Not measured | 55.0% | Only GPT-5.6 Terra measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 62.9% | Only GPT-5.6 Terra measured |
| FrontierCode | Not measured | 41.3% | Only GPT-5.6 Terra measured |
| WeirdML | Not measured | 78.3% | Only GPT-5.6 Terra measured |
| ALE-Bench | 137.8 | 1951.4 | GPT-5.6 Terra |
| LMArena Text: coding | Not measured | 1485 | Only GPT-5.6 Terra measured |
| LMArena Text: software and IT | Not measured | 1482 | Only GPT-5.6 Terra measured |
| LMArena WebDev: React | Not measured | 1513 | Only GPT-5.6 Terra measured |
| LMArena WebDev: HTML | Not measured | 1526 | Only GPT-5.6 Terra measured |
| LMArena WebDev: from an image | Not measured | 1541 | Only GPT-5.6 Terra measured |
| SciCode | Not measured | 10.8% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 42.1 | Only GPT-5.6 Terra measured |
| Epoch Capabilities Index | Not measured | 160.1 | Only GPT-5.6 Terra measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 43.2 | Only GPT-5.6 Terra measured |
| Design Arena: agents building web apps | Not measured | 1120 | Only GPT-5.6 Terra measured |
| Design Arena: agents building full-stack apps | Not measured | 1061 | Only GPT-5.6 Terra measured |
| Design Arena: agents building mobile apps | Not measured | 1111 | Only GPT-5.6 Terra measured |
| Design Arena: agents building Android apps | Not measured | 1061 | Only GPT-5.6 Terra measured |
| Design Arena: agents building games | Not measured | 1083 | Only GPT-5.6 Terra measured |
| Design Arena: HTML slides | Not measured | 1108 | Only GPT-5.6 Terra measured |
| Design Arena: PowerPoint slides | Not measured | 1131 | Only GPT-5.6 Terra measured |
| LiveBench research | Not measured | 60.4% | Only GPT-5.6 Terra measured |
| LiveBench browser | Not measured | 84.0% | Only GPT-5.6 Terra measured |
| LiveBench long agentic | Not measured | 48.4% | Only GPT-5.6 Terra measured |
| SREBench (Vals AI) | Not measured | 9.5% | Only GPT-5.6 Terra measured |
| Finance Agent (Vals AI) | Not measured | 54.4% | Only GPT-5.6 Terra measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.0% | Only GPT-5.6 Terra measured |
| Legal Research Bench (Vals AI) | Not measured | 41.4% | Only GPT-5.6 Terra measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 66.2% | Only GPT-5.6 Terra measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 10.0% | Only GPT-5.6 Terra measured |
| τ²-Bench Telecom | Not measured | 86.3% | Only GPT-5.6 Terra measured |
| APEX-Agents | Not measured | 58.2% | Only GPT-5.6 Terra measured |
| BALROG | Not measured | 53.2% | Only GPT-5.6 Terra measured |
| Vending-Bench 2 | Not measured | $7,343 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Codestral 2508 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 90.9% | Only GPT-5.6 Terra measured |
| LiveBench reasoning | Not measured | 90.6% | Only GPT-5.6 Terra measured |
| MMMU Pro (Vals AI) | Not measured | 86.5% | Only GPT-5.6 Terra measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 92.5% | Only GPT-5.6 Terra measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 42.9% | Only GPT-5.6 Terra measured |
| GPQA Diamond (Epoch AI run) | Not measured | 93.3% | Only GPT-5.6 Terra measured |
| ARC-AGI | Not measured | 96.5% | Only GPT-5.6 Terra measured |
| ARC-AGI-2 | Not measured | 83.9% | Only GPT-5.6 Terra measured |
| SimpleBench | Not measured | 48.9% | Only GPT-5.6 Terra measured |
| Chess puzzles | Not measured | 47.0% | Only GPT-5.6 Terra measured |
| Mystery game puzzles | Not measured | 35.0% | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts | Not measured | 1469 | Only GPT-5.6 Terra measured |
| LMArena Text: expert | Not measured | 1496 | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts in English | Not measured | 1465 | Only GPT-5.6 Terra measured |
| LMArena Vision: diagrams | Not measured | 1295 | Only GPT-5.6 Terra measured |
| LMArena Vision: homework | Not measured | 1312 | Only GPT-5.6 Terra measured |
| Kagi LLM Benchmark | 32.5% | 51.3% | GPT-5.6 Terra |
| Neither model has a result on the other 5 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 99.7% | Only GPT-5.6 Terra measured |
| FrontierMath, tiers 1 to 3 | Not measured | 86.0% | Only GPT-5.6 Terra measured |
| FrontierMath, tier 4 | Not measured | 70.7% | Only GPT-5.6 Terra measured |
| ProofBench | Not measured | 74.0% | Only GPT-5.6 Terra measured |
| LMArena Text: math | Not measured | 1465 | Only GPT-5.6 Terra measured |
| LMArena Text: mathematical work | Not measured | 1478 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.7% | Only GPT-5.6 Terra measured |
| LegalBench (Vals AI) | Not measured | 85.1% | Only GPT-5.6 Terra measured |
| CorpFin (Vals AI) | Not measured | 65.3% | Only GPT-5.6 Terra measured |
| TaxEval (Vals AI) | Not measured | 76.2% | Only GPT-5.6 Terra measured |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Terra measured |
| MedCode (Vals AI) | Not measured | 43.4% | Only GPT-5.6 Terra measured |
| SimpleQA Verified | Not measured | 43.2% | Only GPT-5.6 Terra measured |
| LMArena Text: science | Not measured | 1452 | Only GPT-5.6 Terra measured |
| LMArena Text: medicine and healthcare | Not measured | 1430 | Only GPT-5.6 Terra measured |
| LMArena Text: legal and government | Not measured | 1461 | Only GPT-5.6 Terra measured |
| LMArena Text: business and finance | Not measured | 1448 | Only GPT-5.6 Terra measured |
| FACTS Benchmark Suite | Not measured | 52.0% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Codestral 2508 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 1268 | GPT-5.6 Terra |
| Design Arena: UI components | 1017 | 1259 | GPT-5.6 Terra |
| Design Arena: game dev | 968 | 1230 | GPT-5.6 Terra |
| Design Arena: data viz | 1017 | 1240 | GPT-5.6 Terra |
| Design Arena: 3D | 1020 | 1135 | GPT-5.6 Terra |
| Design Arena: all code categories | 1007 | 1241 | GPT-5.6 Terra |
| Design Arena: SVG | Not measured | 1220 | Only GPT-5.6 Terra measured |
| Design Arena: ASCII art | Not measured | 1177 | Only GPT-5.6 Terra measured |
| LiveBench design | Not measured | 76.0% | Only GPT-5.6 Terra measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index35 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 79.3% | Only GPT-5.6 Terra measured |
| Public Benefits Bench (Vals AI) | Not measured | 62.4% | Only GPT-5.6 Terra measured |
| SAGE (Vals AI) | Not measured | 47.0% | Only GPT-5.6 Terra measured |
| MedScribe (Vals AI) | Not measured | 82.9% | Only GPT-5.6 Terra measured |
| Vals Multimodal Index | Not measured | 65.1% | Only GPT-5.6 Terra measured |
| IFBench | Not measured | 71.2% | Only GPT-5.6 Terra measured |
| Long context reasoning (AA-LCR) | Not measured | 83.0% | Only GPT-5.6 Terra measured |
| DTBench | Not measured | 93.3% | Only GPT-5.6 Terra measured |
| LMCA | Not measured | 54.9% | Only GPT-5.6 Terra measured |
| Surface Evolver Bench | Not measured | 83.8% | Only GPT-5.6 Terra measured |
| Furniture assembly | Not measured | 54.2% | Only GPT-5.6 Terra measured |
| LMArena Text | Not measured | 1448 | Only GPT-5.6 Terra measured |
| LMArena Text: instruction following | Not measured | 1456 | Only GPT-5.6 Terra measured |
| LMArena Text: creative writing | Not measured | 1413 | Only GPT-5.6 Terra measured |
| LMArena Vision | Not measured | 1271 | Only GPT-5.6 Terra measured |
| LMArena Text: English | Not measured | 1449 | Only GPT-5.6 Terra measured |
| LMArena Text: non-English | Not measured | 1441 | Only GPT-5.6 Terra measured |
| LMArena Text: Chinese | Not measured | 1514 | Only GPT-5.6 Terra measured |
| LMArena Text: German | Not measured | 1460 | Only GPT-5.6 Terra measured |
| LMArena Text: Japanese | Not measured | 1454 | Only GPT-5.6 Terra measured |
| LMArena Text: Korean | Not measured | 1427 | Only GPT-5.6 Terra measured |
| LMArena Text: French | Not measured | 1466 | Only GPT-5.6 Terra measured |
| LMArena Text: Spanish | Not measured | 1451 | Only GPT-5.6 Terra measured |
| LMArena Text: Russian | Not measured | 1452 | Only GPT-5.6 Terra measured |
| LMArena Text: Polish | Not measured | 1446 | Only GPT-5.6 Terra measured |
| LMArena Text: multi-turn | Not measured | 1453 | Only GPT-5.6 Terra measured |
| LMArena Text: longer queries | Not measured | 1454 | Only GPT-5.6 Terra measured |
| LMArena Text: writing and language | Not measured | 1431 | Only GPT-5.6 Terra measured |
| LMArena Text: entertainment and media | Not measured | 1418 | Only GPT-5.6 Terra measured |
| LMArena Vision: English | Not measured | 1272 | Only GPT-5.6 Terra measured |
| LMArena Vision: Chinese | Not measured | 1337 | Only GPT-5.6 Terra measured |
| LMArena Vision: reading text | Not measured | 1278 | Only GPT-5.6 Terra measured |
| LMArena Vision: humor | Not measured | 1236 | Only GPT-5.6 Terra measured |
| LMArena Vision: creative writing | Not measured | 1248 | Only GPT-5.6 Terra measured |
| LMArena Document | Not measured | 1472 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GPT-5.6 Terra | 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$0.0008Fits in one request
GPT-5.6 Terra$0.0080Fits in one request
Codestral 2508 costs less
Repository review
50K input + 3K output tokens
Codestral 2508$0.018Fits in one request
GPT-5.6 Terra$0.136Fits in one request
Codestral 2508 costs less
Agent loop
200K input + 10K output tokens
Codestral 2508$0.069Fits in one request
GPT-5.6 Terra$0.520Fits in one request
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-5.6 Terra |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | July 9, 2026 |
| Context window | 256K | 1.05M |
| Input $/Mtok | $0.30 | $2.00 |
| Output $/Mtok | $0.90 | $12.00 |
| Coding index | Not benchmarked | 63.9 |
| Independent measurements behind it | 0 | 6 |
| Cost per solved task | Not computable | $0.42 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than GPT-5.6 Terra for coding?
Only GPT-5.6 Terra has a coding score, 63.9 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding. Pick Codestral 2508 for output tokens 13.3x cheaper ($0.90 vs $12.00 per million).
Which is cheaper, Codestral 2508 or GPT-5.6 Terra?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-5.6 Terra costs $2.00 and $12.00. Cost per solved task is known for GPT-5.6 Terra only.
Which has the bigger context window, Codestral 2508 or GPT-5.6 Terra?
GPT-5.6 Terra, with 1.05M tokens against 256K.
How many benchmarks is the Codestral 2508 vs GPT-5.6 Terra comparison based on?
8 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 independent benchmarks and GPT-5.6 Terra's on 6. Where only one model has a result, the page says so instead of naming a winner.