GPT-6 Luna vs Mistral Medium 3.1
GPT-6 Luna leads Mistral Medium 3.1 on the Artificial Analysis Intelligence Index, 38.1 to 9.2, and GPT-6 Luna costs 4.0x less per output token ($0.50 vs $2.00 per million). Updated October 22, 2026; 50 benchmarks have a result for both.
GPT-6 Luna or Mistral Medium 3.1: which to pick
Only GPT-6 Luna has a coding score, 57.3 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding.
Pick Mistral Medium 3.1 for work measured by LMArena Text: software and IT or LMArena Text: hard prompts, where it leads.
- Shared results
- 50
- GPT-6 Luna only
- 53
- Mistral Medium 3.1 only
- 24
- Like-for-like categories
- 7 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 Luna has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-6 Luna
GPT-6 Luna leads, 38.1 against 9.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.1 has a AA Agentic Index result, 1.9.
Directional only
Math
AIME
No call
Only Mistral Medium 3.1 has a AIME result, 42.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.1 has a MMLU Pro result, 75.3%.
Directional only
Web design
Design Arena: websites
GPT-6 Luna
GPT-6 Luna leads, 1271 against 1132.
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 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna
GPT-6 Luna $0.0003, Mistral Medium 3.1 $0.0014 per turn.
List prices
At a glance
- Coding index
- GPT-6 Luna 57.3 from 5 benchmarks, Mistral Medium 3.1 not benchmarked.
- Widest category
- Intelligence, 28.9 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: GPT-6 Luna 1576.9 against Mistral Medium 3.1 210.2, 45.6 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Luna $0.50 against Mistral Medium 3.1 $2.00 per million output tokens, 4.0x apart.
- Context window
- GPT-6 Luna takes 1.05M tokens, Mistral Medium 3.1 131K.
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 3.1 | Ties |
|---|---|---|---|---|
| Coding | 3 | 1 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 2 | ||
| Math | 2 | 0 | ||
| Knowledge | 0 | 4 | ||
| Web design | 8 | 0 | ||
| General | 10 | 15 | ||
| All shared boards | 28 | 22 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index31
| Benchmark | GPT-6 Luna | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| LiveBench coding | 84.5% | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev | 1578 | Not measured | Only GPT-6 Luna measured |
| AA Coding Index | Not measured | 20.5 | Only Mistral Medium 3.1 measured |
| LiveCodeBench (Vals AI) | Not measured | 44.8% | Only Mistral Medium 3.1 measured |
| Terminal-Bench 4 (Vals AI) | 13.6% | Not measured | Only GPT-6 Luna measured |
| Vibe Code Bench (Vals AI) | 81.7% | Not measured | Only GPT-6 Luna measured |
| Code migration (Vals AI) | 42.5% | Not measured | Only GPT-6 Luna measured |
| 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% | Not measured | Only GPT-6 Luna measured |
| 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 40.6% | Only Mistral Medium 3.1 measured |
| SciCode | 54.6% | 32.4% | GPT-6 Luna |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 10.6% | Only Mistral Medium 3.1 measured |
| FrontierCode | 42.4% | Not measured | Only GPT-6 Luna measured |
| ALE-Bench | 1576.9 | 210.2 | GPT-6 Luna |
| LMArena Text: coding | 1437 | 1433 | GPT-6 Luna |
| LMArena Text: software and IT | 1423 | 1436 | Mistral Medium 3.1 |
| LMArena WebDev: React | 1573 | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev: HTML | 1595 | Not measured | Only GPT-6 Luna measured |
| 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 index10
| Benchmark | GPT-6 Luna | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.1 | 9.2 | GPT-6 Luna |
| Epoch Capabilities Index | 156.8 | Not measured | Only GPT-6 Luna measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GPT-6 Luna | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.9 | Only Mistral Medium 3.1 measured |
| Design Arena: agents building web apps | 1191 | 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 | 1134 | 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% | Not measured | Only GPT-6 Luna measured |
| Tax Agent Bench (Vals AI) | 19.9% | Not measured | Only GPT-6 Luna measured |
| Legal Research Bench (Vals AI) | 30.3% | Not measured | Only GPT-6 Luna measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 4.2% | Not measured | Only GPT-6 Luna measured |
| Excel Modeling Benchmark (Vals AI) | 68.5% | Not measured | Only GPT-6 Luna measured |
| 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 | 40.6% | Only Mistral Medium 3.1 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 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | GPT-6 Luna | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| LiveBench reasoning | 81.8% | Not measured | Only GPT-6 Luna measured |
| MysteryMechanism (Vals AI) | 19.4% | Not measured | Only GPT-6 Luna measured |
| MMMU Pro (Vals AI) | Not measured | 63.0% | Only Mistral Medium 3.1 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 58.8% | Only Mistral Medium 3.1 measured |
| Humanity's Last Exam (Artificial Analysis run) | 38.5% | 4.7% | 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 | 1426 | Mistral Medium 3.1 |
| LMArena Text: expert | 1442 | 1408 | GPT-6 Luna |
| LMArena Text: hard prompts in English | 1410 | 1435 | Mistral Medium 3.1 |
| LMArena Vision: diagrams | 1245 | 1175 | GPT-6 Luna |
| LMArena Vision: homework | 1261 | 1165 | GPT-6 Luna |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6 Luna | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| AIME | Not measured | 42.3% | Only Mistral Medium 3.1 measured |
| MATH 500 (Vals AI) | Not measured | 87.0% | Only Mistral Medium 3.1 measured |
| MGSM (Vals AI) | Not measured | 90.9% | Only Mistral Medium 3.1 measured |
| AA Math Index | Not measured | 38.3 | Only Mistral Medium 3.1 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 38.3% | Only Mistral Medium 3.1 measured |
| 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% | Not measured | Only GPT-6 Luna measured |
| LMArena Text: math | 1409 | 1408 | GPT-6 Luna |
| LMArena Text: mathematical work | 1427 | 1412 | GPT-6 Luna |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-6 Luna | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 75.3% | Only Mistral Medium 3.1 measured |
| LegalBench (Vals AI) | Not measured | 62.0% | Only Mistral Medium 3.1 measured |
| CorpFin (Vals AI) | Not measured | 50.7% | Only Mistral Medium 3.1 measured |
| TaxEval (Vals AI) | Not measured | 70.3% | Only Mistral Medium 3.1 measured |
| MortgageTax (Vals AI) | Not measured | 36.5% | Only Mistral Medium 3.1 measured |
| MedQA (Vals AI) | Not measured | 78.2% | Only Mistral Medium 3.1 measured |
| MedCode (Vals AI) | 44.7% | Not measured | Only GPT-6 Luna measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 68.3% | Only Mistral Medium 3.1 measured |
| SimpleQA Verified | 41.4% | Not measured | Only GPT-6 Luna measured |
| LMArena Text: science | 1386 | 1444 | Mistral Medium 3.1 |
| LMArena Text: medicine and healthcare | 1352 | 1449 | Mistral Medium 3.1 |
| LMArena Text: legal and government | 1400 | 1439 | Mistral Medium 3.1 |
| LMArena Text: business and finance | 1388 | 1418 | Mistral Medium 3.1 |
| FACTS Benchmark Suite | 63.3% | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GPT-6 Luna | Mistral Medium 3.1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1271 | 1132 | GPT-6 Luna |
| Design Arena: UI components | 1295 | 1097 | GPT-6 Luna |
| Design Arena: game dev | 1257 | 1073 | GPT-6 Luna |
| Design Arena: data viz | 1216 | 1144 | GPT-6 Luna |
| Design Arena: 3D | 1218 | 1081 | GPT-6 Luna |
| Design Arena: all code categories | 1270 | 1120 | GPT-6 Luna |
| Design Arena: SVG | 1252 | 992 | GPT-6 Luna |
| Design Arena: ASCII art | 1153 | 997 | 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 index1015
| Benchmark | GPT-6 Luna | Mistral Medium 3.1 | 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% | Not measured | Only GPT-6 Luna measured |
| MedScribe (Vals AI) | 83.7% | Not measured | Only GPT-6 Luna measured |
| IFBench | Not measured | 39.8% | Only Mistral Medium 3.1 measured |
| Long context reasoning (AA-LCR) | 83.3% | 42.7% | GPT-6 Luna |
| DTBench | 90.1% | 67.5% | GPT-6 Luna |
| LMCA | 44.5% | 19.1% | GPT-6 Luna |
| Furniture assembly | 44.2% | Not measured | Only GPT-6 Luna measured |
| LMArena Text | 1389 | 1424 | Mistral Medium 3.1 |
| LMArena Text: instruction following | 1408 | 1398 | GPT-6 Luna |
| LMArena Text: creative writing | 1363 | 1391 | Mistral Medium 3.1 |
| LMArena Vision | 1216 | 1172 | GPT-6 Luna |
| LMArena Text: English | 1393 | 1438 | Mistral Medium 3.1 |
| LMArena Text: non-English | 1381 | 1408 | Mistral Medium 3.1 |
| LMArena Text: Chinese | 1427 | 1447 | Mistral Medium 3.1 |
| LMArena Text: German | 1376 | 1432 | Mistral Medium 3.1 |
| LMArena Text: Japanese | 1369 | 1379 | Mistral Medium 3.1 |
| LMArena Text: Korean | 1359 | 1380 | Mistral Medium 3.1 |
| LMArena Text: French | 1408 | 1460 | Mistral Medium 3.1 |
| LMArena Text: Spanish | 1391 | 1433 | Mistral Medium 3.1 |
| LMArena Text: Russian | 1392 | 1411 | Mistral Medium 3.1 |
| LMArena Text: Polish | 1383 | 1430 | Mistral Medium 3.1 |
| LMArena Text: multi-turn | 1392 | 1418 | Mistral Medium 3.1 |
| LMArena Text: longer queries | 1408 | 1406 | GPT-6 Luna |
| LMArena Text: writing and language | 1384 | 1395 | Mistral Medium 3.1 |
| LMArena Text: entertainment and media | 1364 | 1390 | Mistral Medium 3.1 |
| LMArena Vision: English | 1204 | 1179 | GPT-6 Luna |
| LMArena Vision: Chinese | 1266 | 1190 | GPT-6 Luna |
| LMArena Vision: reading text | 1225 | 1175 | GPT-6 Luna |
| LMArena Vision: humor | Not measured | 1168 | Only Mistral Medium 3.1 measured |
| LMArena Vision: captioning | Not measured | 1145 | Only Mistral Medium 3.1 measured |
| LMArena Vision: recognizing things | Not measured | 1147 | Only Mistral Medium 3.1 measured |
| LMArena Vision: creative writing | 1205 | 1200 | 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 3.1 | 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 3.1 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | September 22, 2026 | August 13, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.10 | $0.40 |
| Output $/Mtok | $0.50 | $2.00 |
| Coding index | 57.3 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6 Luna better than Mistral Medium 3.1 for coding?
Only GPT-6 Luna has a coding score, 57.3 on the Vibe Coding Index; Mistral Medium 3.1 has none, so the two cannot be ranked on coding. Pick Mistral Medium 3.1 for work measured by LMArena Text: software and IT or LMArena Text: hard prompts, where it leads.
Which is cheaper, GPT-6 Luna or Mistral Medium 3.1?
GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens; Mistral Medium 3.1 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6 Luna or Mistral Medium 3.1?
GPT-6 Luna, with 1.05M tokens against 131K.
How many benchmarks is the GPT-6 Luna vs Mistral Medium 3.1 comparison based on?
50 benchmarks have a result for both models as of October 22, 2026. GPT-6 Luna's coding index rests on 5 independent benchmarks and Mistral Medium 3.1's on 0. Where only one model has a result, the page says so instead of naming a winner.