Codestral 2508 vs GPT-6 Luna
Updated October 21, 2026. 7 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
vs boards won of 7 shared
Decision reading
Only GPT-6 Luna has a coding score, 57.3 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
Model ACodestral 2508Mistral AINot benchmarkedNo coding benchmarkNo cost per solved taskModel BGPT-6 LunaOpenAI57.3/1005 independent benchmarksNo cost per solved task
- Shared results
- 7
- Codestral 2508 only
- 0
- GPT-6 Luna only
- 95
- Like-for-like categories
- 2 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-6 Luna has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-6 Luna has a AA Intelligence Index result, 38.1.
Directional only
Web design
Design Arena: websites
GPT-6 Luna
GPT-6 Luna leads, 1272 against 1013.
Same board, both measured
General
Vals Index
No call
Only GPT-6 Luna has a Vals Index result, 52.3%.
Directional only
Long documents
Listed context window
GPT-6 Luna
GPT-6 Luna takes 1.05M tokens against 256K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna
Codestral 2508 $0.0008, GPT-6 Luna $0.0003 per turn.
List prices
At a glance
- Coding index
- Codestral 2508 not benchmarked, GPT-6 Luna 57.3 from 5 benchmarks.
- Widest category
- Coding, 48.0 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: Codestral 2508 137.8 against GPT-6 Luna 1576.9, 48.0 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against GPT-6 Luna $0.50 per million output tokens, 1.8x apart.
- Context window
- Codestral 2508 takes 256K tokens, GPT-6 Luna 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 Luna | 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 Luna | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 84.5% | Only GPT-6 Luna measured |
| LMArena WebDev | Not measured | 1578 | Only GPT-6 Luna measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 13.6% | Only GPT-6 Luna measured |
| Vibe Code Bench (Vals AI) | Not measured | 81.7% | Only GPT-6 Luna measured |
| Code migration (Vals AI) | Not measured | 42.5% | Only GPT-6 Luna measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-6 Luna measured |
| IOI (Vals AI) | Not measured | 55.6% | Only GPT-6 Luna measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 73.0% | Only GPT-6 Luna measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 37.5% | Only GPT-6 Luna measured |
| CyberBench (Vals AI) | Not measured | 76.3% | Only GPT-6 Luna measured |
| SciCode | Not measured | 54.6% | Only GPT-6 Luna measured |
| FrontierCode | Not measured | 42.4% | Only GPT-6 Luna measured |
| ALE-Bench | 137.8 | 1576.9 | GPT-6 Luna |
| LMArena Text: coding | Not measured | 1437 | Only GPT-6 Luna measured |
| LMArena Text: software and IT | Not measured | 1423 | Only GPT-6 Luna measured |
| LMArena WebDev: React | Not measured | 1573 | Only GPT-6 Luna measured |
| LMArena WebDev: HTML | Not measured | 1595 | Only GPT-6 Luna measured |
| LMArena WebDev: from an image | Not measured | 1571 | Only GPT-6 Luna measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | GPT-6 Luna | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 38.1 | Only GPT-6 Luna measured |
| Epoch Capabilities Index | Not measured | 156.8 | Only GPT-6 Luna measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | Codestral 2508 | GPT-6 Luna | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1191 | Only GPT-6 Luna measured |
| Design Arena: agents building full-stack apps | Not measured | 1158 | Only GPT-6 Luna measured |
| Design Arena: agents building mobile apps | Not measured | 1127 | Only GPT-6 Luna measured |
| LiveBench research | Not measured | 43.5% | Only GPT-6 Luna measured |
| LiveBench browser | Not measured | 72.0% | Only GPT-6 Luna measured |
| LiveBench long agentic | Not measured | 48.1% | Only GPT-6 Luna measured |
| SREBench (Vals AI) | Not measured | 2.7% | Only GPT-6 Luna measured |
| Finance Agent (Vals AI) | Not measured | 49.9% | Only GPT-6 Luna measured |
| Tax Agent Bench (Vals AI) | Not measured | 19.9% | Only GPT-6 Luna measured |
| Legal Research Bench (Vals AI) | Not measured | 30.3% | Only GPT-6 Luna measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 4.2% | Only GPT-6 Luna measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 68.5% | Only GPT-6 Luna measured |
| BioMysteryBench (Vals AI) | Not measured | 61.5% | Only GPT-6 Luna measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 4.3% | Only GPT-6 Luna measured |
| Vals Science Index | Not measured | 28.4% | Only GPT-6 Luna measured |
| APEX-Agents | Not measured | 44.3% | Only GPT-6 Luna measured |
| SpireClimb | Not measured | 0.0% | Only GPT-6 Luna measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | Codestral 2508 | GPT-6 Luna | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 81.8% | Only GPT-6 Luna measured |
| MysteryMechanism (Vals AI) | Not measured | 19.4% | Only GPT-6 Luna measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 38.5% | Only GPT-6 Luna measured |
| GPQA Diamond (Epoch AI run) | Not measured | 90.5% | Only GPT-6 Luna measured |
| ARC-AGI | Not measured | 86.7% | Only GPT-6 Luna measured |
| ARC-AGI-2 | Not measured | 59.3% | Only GPT-6 Luna measured |
| Chess puzzles | Not measured | 36.0% | Only GPT-6 Luna measured |
| Mystery game puzzles | Not measured | 7.0% | Only GPT-6 Luna measured |
| LMArena Text: hard prompts | Not measured | 1410 | Only GPT-6 Luna measured |
| LMArena Text: expert | Not measured | 1442 | Only GPT-6 Luna measured |
| LMArena Text: hard prompts in English | Not measured | 1410 | Only GPT-6 Luna measured |
| LMArena Vision: diagrams | Not measured | 1245 | Only GPT-6 Luna measured |
| LMArena Vision: homework | Not measured | 1261 | Only GPT-6 Luna measured |
| Neither model has a result on the other 8 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Codestral 2508 | GPT-6 Luna | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 98.9% | Only GPT-6 Luna measured |
| FrontierMath, tiers 1 to 3 | Not measured | 78.9% | Only GPT-6 Luna measured |
| FrontierMath, tier 4 | Not measured | 56.1% | Only GPT-6 Luna measured |
| ProofBench | Not measured | 64.0% | Only GPT-6 Luna measured |
| LMArena Text: math | Not measured | 1409 | Only GPT-6 Luna measured |
| LMArena Text: mathematical work | Not measured | 1427 | Only GPT-6 Luna 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 Luna | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 44.7% | Only GPT-6 Luna measured |
| SimpleQA Verified | Not measured | 41.4% | Only GPT-6 Luna measured |
| LMArena Text: science | Not measured | 1386 | Only GPT-6 Luna measured |
| LMArena Text: medicine and healthcare | Not measured | 1352 | Only GPT-6 Luna measured |
| LMArena Text: legal and government | Not measured | 1400 | Only GPT-6 Luna measured |
| LMArena Text: business and finance | Not measured | 1388 | Only GPT-6 Luna 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 Luna | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 1272 | GPT-6 Luna |
| Design Arena: UI components | 1017 | 1298 | GPT-6 Luna |
| Design Arena: game dev | 968 | 1261 | GPT-6 Luna |
| Design Arena: data viz | 1017 | 1215 | GPT-6 Luna |
| Design Arena: 3D | 1020 | 1217 | GPT-6 Luna |
| Design Arena: all code categories | 1007 | 1272 | GPT-6 Luna |
| Design Arena: SVG | Not measured | 1250 | Only GPT-6 Luna measured |
| Design Arena: ASCII art | Not measured | 1155 | Only GPT-6 Luna measured |
| LiveBench design | Not measured | 82.5% | Only GPT-6 Luna measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index31 boards, none shared
| Benchmark | Codestral 2508 | GPT-6 Luna | Leader |
|---|---|---|---|
| Vals Index | Not measured | 52.3% | Only GPT-6 Luna measured |
| LiveBench data analysis | Not measured | 73.4% | Only GPT-6 Luna measured |
| Public Benefits Bench (Vals AI) | Not measured | 57.6% | Only GPT-6 Luna measured |
| SAGE (Vals AI) | Not measured | 48.1% | Only GPT-6 Luna measured |
| MedScribe (Vals AI) | Not measured | 83.7% | Only GPT-6 Luna measured |
| Long context reasoning (AA-LCR) | Not measured | 83.3% | Only GPT-6 Luna measured |
| DTBench | Not measured | 90.1% | Only GPT-6 Luna measured |
| LMCA | Not measured | 44.5% | Only GPT-6 Luna measured |
| Furniture assembly | Not measured | 44.2% | Only GPT-6 Luna measured |
| LMArena Text | Not measured | 1389 | Only GPT-6 Luna measured |
| LMArena Text: instruction following | Not measured | 1408 | Only GPT-6 Luna measured |
| LMArena Text: creative writing | Not measured | 1363 | Only GPT-6 Luna measured |
| LMArena Vision | Not measured | 1216 | Only GPT-6 Luna measured |
| LMArena Text: English | Not measured | 1393 | Only GPT-6 Luna measured |
| LMArena Text: non-English | Not measured | 1381 | Only GPT-6 Luna measured |
| LMArena Text: Chinese | Not measured | 1427 | Only GPT-6 Luna measured |
| LMArena Text: German | Not measured | 1376 | Only GPT-6 Luna measured |
| LMArena Text: Japanese | Not measured | 1369 | Only GPT-6 Luna measured |
| LMArena Text: Korean | Not measured | 1359 | Only GPT-6 Luna measured |
| LMArena Text: French | Not measured | 1408 | Only GPT-6 Luna measured |
| LMArena Text: Spanish | Not measured | 1391 | Only GPT-6 Luna measured |
| LMArena Text: Russian | Not measured | 1392 | Only GPT-6 Luna measured |
| LMArena Text: Polish | Not measured | 1383 | Only GPT-6 Luna measured |
| LMArena Text: multi-turn | Not measured | 1392 | Only GPT-6 Luna measured |
| LMArena Text: longer queries | Not measured | 1408 | Only GPT-6 Luna measured |
| LMArena Text: writing and language | Not measured | 1384 | Only GPT-6 Luna measured |
| LMArena Text: entertainment and media | Not measured | 1364 | Only GPT-6 Luna measured |
| LMArena Vision: English | Not measured | 1204 | Only GPT-6 Luna measured |
| LMArena Vision: Chinese | Not measured | 1266 | Only GPT-6 Luna measured |
| LMArena Vision: reading text | Not measured | 1225 | Only GPT-6 Luna measured |
| LMArena Vision: creative writing | Not measured | 1205 | Only GPT-6 Luna measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | GPT-6 Luna | 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-6 Luna$0.0003Fits in one request
GPT-6 Luna costs less
Repository review
50K input + 3K output tokens
Codestral 2508$0.018Fits in one request
GPT-6 Luna$0.0065Fits in one request
GPT-6 Luna costs less
Agent loop
200K input + 10K output tokens
Codestral 2508$0.069Fits in one request
GPT-6 Luna$0.025Fits in one request
GPT-6 Luna 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 Luna |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | August 1, 2025 | September 22, 2026 |
| Context window | 256K | 1.05M |
| Input $/Mtok | $0.30 | $0.10 |
| Output $/Mtok | $0.90 | $0.50 |
| Coding index | Not benchmarked | 57.3 |
| 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 Luna for coding?
Only GPT-6 Luna has a coding score, 57.3 on the Vibe Coding Index; Codestral 2508 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Codestral 2508 or GPT-6 Luna?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; GPT-6 Luna costs $0.10 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Codestral 2508 or GPT-6 Luna?
GPT-6 Luna, with 1.05M tokens against 256K.
How many benchmarks is the Codestral 2508 vs GPT-6 Luna 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 Luna's on 5. Where only one model has a result, the page says so instead of naming a winner.