GPT-6 Luna vs Mistral Medium 3.5
Updated October 21, 2026. 64 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-6 Luna for coding: it scores 71.4 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5.
Pick Mistral Medium 3.5 when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 64
- GPT-6 Luna only
- 38
- Mistral Medium 3.5 only
- 16
- Like-for-like categories
- 8 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, four public coding benchmarks
GPT-6 Luna
GPT-6 Luna scores 71.4 against 9.2.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6 Luna
GPT-6 Luna leads, 38.1 against 14.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.5 has a AA Agentic Index result, 7.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Mistral Medium 3.5 has a GPQA Diamond result, 34.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.5 has a MMLU Pro result, 75.3%.
Directional only
Web design
Design Arena: websites
GPT-6 Luna
GPT-6 Luna leads, 1272 against 1142.
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna
GPT-6 Luna $0.0003, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Mistral Medium 3.5 has the number, $6.02.
Directional only
At a glance
- Coding index
- GPT-6 Luna 71.4 from 2 benchmarks, Mistral Medium 3.5 9.2 from 1 benchmark.
- Widest category
- Coding, 34.8 points apart on average over 10 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-6 Luna 81.7% against Mistral Medium 3.5 2.9%, 78.8 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Luna $0.50 against Mistral Medium 3.5 $7.50 per million output tokens, 15.0x apart.
- Context window
- GPT-6 Luna takes 1.05M tokens, Mistral Medium 3.5 262K.
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 | GPT-6 Luna | Bars | Mistral Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 8 | 2 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 4 | 0 | ||
| Reasoning | 3 | 3 | ||
| Math | 1 | 2 | ||
| Knowledge | 1 | 4 | ||
| Web design | 8 | 0 | ||
| General | 7 | 19 | ||
| All shared boards | 34 | 30 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index82
| Benchmark | GPT-6 Luna | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| LiveBench coding | 84.5% | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev | 1578 | 1264 | GPT-6 Luna |
| AA Coding Index | Not measured | 46.9 | Only Mistral Medium 3.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 66.4% | Only Mistral Medium 3.5 measured |
| Terminal-Bench 4 (Vals AI) | 13.6% | Not measured | Only GPT-6 Luna measured |
| Vibe Code Bench (Vals AI) | 81.7% | 2.9% | GPT-6 Luna |
| Code migration (Vals AI) | 42.5% | 5.1% | GPT-6 Luna |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GPT-6 Luna measured |
| IOI (Vals AI) | 55.6% | Not measured | Only GPT-6 Luna measured |
| Terminal-Bench 2.1 (Vals AI) | 73.0% | 39.0% | GPT-6 Luna |
| SWE-bench Multimodal (Vals AI) | 37.5% | Not measured | Only GPT-6 Luna measured |
| CyberBench (Vals AI) | 76.3% | Not measured | Only GPT-6 Luna measured |
| SciCode | 54.6% | 40.2% | GPT-6 Luna |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only Mistral Medium 3.5 measured |
| FrontierCode | 42.4% | Not measured | Only GPT-6 Luna measured |
| ALE-Bench | 1576.9 | 764.0 | GPT-6 Luna |
| LMArena Text: coding | 1437 | 1460 | Mistral Medium 3.5 |
| LMArena Text: software and IT | 1423 | 1458 | Mistral Medium 3.5 |
| LMArena WebDev: React | 1573 | 1254 | GPT-6 Luna |
| LMArena WebDev: HTML | 1595 | 1308 | GPT-6 Luna |
| LMArena WebDev: from an image | 1571 | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6 Luna | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.1 | 14.2 | GPT-6 Luna |
| Epoch Capabilities Index | 156.8 | 141.4 | GPT-6 Luna |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6 Luna | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 7.0 | Only Mistral Medium 3.5 measured |
| Design Arena: agents building web apps | 1189 | Not measured | Only GPT-6 Luna measured |
| Design Arena: agents building full-stack apps | 1158 | Not measured | Only GPT-6 Luna measured |
| Design Arena: agents building mobile apps | 1133 | Not measured | Only GPT-6 Luna measured |
| LiveBench research | 43.5% | Not measured | Only GPT-6 Luna measured |
| LiveBench browser | 72.0% | Not measured | Only GPT-6 Luna measured |
| LiveBench long agentic | 48.1% | Not measured | Only GPT-6 Luna measured |
| SREBench (Vals AI) | 2.7% | Not measured | Only GPT-6 Luna measured |
| Finance Agent (Vals AI) | 49.9% | 32.1% | GPT-6 Luna |
| Tax Agent Bench (Vals AI) | 19.9% | 4.5% | GPT-6 Luna |
| Legal Research Bench (Vals AI) | 30.3% | 9.1% | GPT-6 Luna |
| Harvey's Legal Agent Benchmark (Vals AI) | 4.2% | Not measured | Only GPT-6 Luna measured |
| Excel Modeling Benchmark (Vals AI) | 68.5% | 11.2% | GPT-6 Luna |
| BioMysteryBench (Vals AI) | 61.5% | Not measured | Only GPT-6 Luna measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only GPT-6 Luna measured |
| Vals Science Index | 28.4% | Not measured | Only GPT-6 Luna measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Mistral Medium 3.5 measured |
| APEX-Agents | 44.3% | Not measured | Only GPT-6 Luna measured |
| SpireClimb | 0.0% | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | GPT-6 Luna | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 34.9% | Only Mistral Medium 3.5 measured |
| LiveBench reasoning | 81.8% | Not measured | Only GPT-6 Luna measured |
| MysteryMechanism (Vals AI) | 19.4% | Not measured | Only GPT-6 Luna measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 74.8% | Only Mistral Medium 3.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 38.5% | 13.8% | GPT-6 Luna |
| GPQA Diamond (Epoch AI run) | 90.5% | Not measured | Only GPT-6 Luna measured |
| ARC-AGI | 86.7% | Not measured | Only GPT-6 Luna measured |
| ARC-AGI-2 | 59.3% | Not measured | Only GPT-6 Luna measured |
| Chess puzzles | 36.0% | Not measured | Only GPT-6 Luna measured |
| Mystery game puzzles | 7.0% | Not measured | Only GPT-6 Luna measured |
| LMArena Text: hard prompts | 1410 | 1436 | Mistral Medium 3.5 |
| LMArena Text: expert | 1442 | 1432 | GPT-6 Luna |
| LMArena Text: hard prompts in English | 1410 | 1445 | Mistral Medium 3.5 |
| LMArena Vision: diagrams | 1245 | 1254 | Mistral Medium 3.5 |
| LMArena Vision: homework | 1261 | 1230 | GPT-6 Luna |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GPT-6 Luna | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.9% | Not measured | Only GPT-6 Luna measured |
| FrontierMath, tiers 1 to 3 | 78.9% | Not measured | Only GPT-6 Luna measured |
| FrontierMath, tier 4 | 56.1% | Not measured | Only GPT-6 Luna measured |
| ProofBench | 64.0% | 9.0% | GPT-6 Luna |
| LMArena Text: math | 1409 | 1431 | Mistral Medium 3.5 |
| LMArena Text: mathematical work | 1427 | 1440 | Mistral Medium 3.5 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | GPT-6 Luna | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 75.3% | Only Mistral Medium 3.5 measured |
| CaseLaw (Vals AI) | Not measured | 44.2% | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | Not measured | 58.8% | Only Mistral Medium 3.5 measured |
| TaxEval (Vals AI) | Not measured | 68.0% | Only Mistral Medium 3.5 measured |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedCode (Vals AI) | 44.7% | 33.8% | GPT-6 Luna |
| SimpleQA Verified | 41.4% | Not measured | Only GPT-6 Luna measured |
| LMArena Text: science | 1386 | 1428 | Mistral Medium 3.5 |
| LMArena Text: medicine and healthcare | 1352 | 1409 | Mistral Medium 3.5 |
| LMArena Text: legal and government | 1400 | 1437 | Mistral Medium 3.5 |
| LMArena Text: business and finance | 1388 | 1426 | Mistral Medium 3.5 |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-6 Luna | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1272 | 1142 | GPT-6 Luna |
| Design Arena: UI components | 1297 | 1079 | GPT-6 Luna |
| Design Arena: game dev | 1259 | 1079 | GPT-6 Luna |
| Design Arena: data viz | 1215 | 1120 | GPT-6 Luna |
| Design Arena: 3D | 1217 | 1061 | GPT-6 Luna |
| Design Arena: all code categories | 1271 | 1122 | GPT-6 Luna |
| Design Arena: SVG | 1253 | 949 | GPT-6 Luna |
| Design Arena: ASCII art | 1156 | 1062 | GPT-6 Luna |
| LiveBench design | 82.5% | Not measured | Only GPT-6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index719
| Benchmark | GPT-6 Luna | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Vals Index | 52.3% | Not measured | Only GPT-6 Luna measured |
| LiveBench data analysis | 73.4% | Not measured | Only GPT-6 Luna measured |
| Public Benefits Bench (Vals AI) | 57.6% | Not measured | Only GPT-6 Luna measured |
| SAGE (Vals AI) | 48.1% | 37.6% | GPT-6 Luna |
| MedScribe (Vals AI) | 83.7% | 67.7% | GPT-6 Luna |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | Not measured | 68.8% | Only Mistral Medium 3.5 measured |
| Long context reasoning (AA-LCR) | 83.3% | 69.3% | GPT-6 Luna |
| DTBench | 90.1% | 75.5% | GPT-6 Luna |
| LMCA | 44.5% | 26.1% | GPT-6 Luna |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-6 Luna measured |
| LMArena Text | 1389 | 1421 | Mistral Medium 3.5 |
| LMArena Text: instruction following | 1408 | 1415 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1363 | 1374 | Mistral Medium 3.5 |
| LMArena Vision | 1216 | 1222 | Mistral Medium 3.5 |
| LMArena Text: English | 1393 | 1433 | Mistral Medium 3.5 |
| LMArena Text: non-English | 1381 | 1404 | Mistral Medium 3.5 |
| LMArena Text: Chinese | 1427 | 1444 | Mistral Medium 3.5 |
| LMArena Text: German | 1376 | 1453 | Mistral Medium 3.5 |
| LMArena Text: Japanese | 1369 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: Korean | 1359 | 1384 | Mistral Medium 3.5 |
| LMArena Text: French | 1408 | 1448 | Mistral Medium 3.5 |
| LMArena Text: Spanish | 1391 | 1411 | Mistral Medium 3.5 |
| LMArena Text: Russian | 1392 | 1395 | Mistral Medium 3.5 |
| LMArena Text: Polish | 1383 | 1430 | Mistral Medium 3.5 |
| LMArena Text: multi-turn | 1392 | 1423 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1408 | 1415 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1384 | 1384 | Mistral Medium 3.5 |
| LMArena Text: entertainment and media | 1364 | 1382 | Mistral Medium 3.5 |
| LMArena Vision: English | 1204 | 1240 | Mistral Medium 3.5 |
| LMArena Vision: Chinese | 1266 | 1244 | GPT-6 Luna |
| LMArena Vision: reading text | 1225 | 1233 | Mistral Medium 3.5 |
| LMArena Vision: humor | Not measured | 1216 | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | 1205 | 1198 | GPT-6 Luna |
| 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 | GPT-6 Luna | Mistral Medium 3.5 | 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-6 Luna costs less
Repository review
50K input + 3K output tokens
GPT-6 Luna costs less
Agent loop
200K input + 10K output tokens
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 | OpenAI GPT-6 Luna | Mistral Medium 3.5 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | September 22, 2026 | April 30, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.10 | $1.50 |
| Output $/Mtok | $0.50 | $7.50 |
| Coding index | 71.4 | 9.2 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6 Luna better than Mistral Medium 3.5 for coding?
Pick GPT-6 Luna for coding: it scores 71.4 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5. Pick Mistral Medium 3.5 when what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-6 Luna or Mistral Medium 3.5?
GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.
Which has the bigger context window, GPT-6 Luna or Mistral Medium 3.5?
GPT-6 Luna, with 1.05M tokens against 262K.
How many benchmarks is the GPT-6 Luna vs Mistral Medium 3.5 comparison based on?
64 benchmarks have a result for both models as of October 21, 2026. GPT-6 Luna's coding index rests on 2 independent benchmarks and Mistral Medium 3.5's on 1. Where only one model has a result, the page says so instead of naming a winner.