GPT-6 Luna vs Mistral Small 3.1 24B
GPT-6 Luna and Mistral Small 3.1 24B cost about the same per output token ($0.50 vs $0.555 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
vs boards won of 35 shared
GPT-6 Luna or Mistral Small 3.1 24B: which to pick
Only GPT-6 Luna has a coding score, 57.3 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
Model AGPT-6 LunaOpenAI57.3/1005 independent benchmarksNo cost per solved taskModel BMistral Small 3.1 24BMistral AINot benchmarkedNo coding benchmarkNo cost per solved task
- Shared results
- 35
- GPT-6 Luna only
- 68
- Mistral Small 3.1 24B only
- 3
- Like-for-like categories
- 5 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
No call
Only GPT-6 Luna has a AA Intelligence Index result, 38.1.
Directional only
Web design
Design Arena: websites
No call
Only GPT-6 Luna has a Design Arena: websites result, 1271.
Directional only
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 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna
GPT-6 Luna $0.0003, Mistral Small 3.1 24B $0.0006 per turn.
List prices
At a glance
- Coding index
- GPT-6 Luna 57.3 from 5 benchmarks, Mistral Small 3.1 24B not benchmarked.
- Widest category
- Math, 25.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: expert: GPT-6 Luna 1442 against Mistral Small 3.1 24B 1257, 30.8 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Luna $0.50 against Mistral Small 3.1 24B $0.555 per million output tokens, 1.1x apart.
- Context window
- GPT-6 Luna takes 1.05M tokens, Mistral Small 3.1 24B 128K.
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 Small 3.1 24B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 22 | 0 | ||
| All shared boards | 35 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| LiveBench coding | 84.5% | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev | 1578 | Not measured | Only GPT-6 Luna 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 |
| SciCode | 54.6% | Not measured | Only GPT-6 Luna measured |
| FrontierCode | 42.4% | Not measured | Only GPT-6 Luna measured |
| ALE-Bench | 1576.9 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: coding | 1437 | 1309 | GPT-6 Luna |
| LMArena Text: software and IT | 1423 | 1301 | GPT-6 Luna |
| 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 21 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.1 | Not measured | Only GPT-6 Luna measured |
| Epoch Capabilities Index | 156.8 | Not measured | Only GPT-6 Luna measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index17 boards, none shared
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| 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 |
| 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 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| LiveBench reasoning | 81.8% | Not measured | Only GPT-6 Luna measured |
| MysteryMechanism (Vals AI) | 19.4% | Not measured | Only GPT-6 Luna measured |
| Humanity's Last Exam (Artificial Analysis run) | 38.5% | Not measured | Only GPT-6 Luna measured |
| 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 | 1278 | GPT-6 Luna |
| LMArena Text: expert | 1442 | 1257 | GPT-6 Luna |
| LMArena Text: hard prompts in English | 1410 | 1290 | GPT-6 Luna |
| LMArena Vision: diagrams | 1245 | 1119 | GPT-6 Luna |
| LMArena Vision: homework | 1261 | 1158 | GPT-6 Luna |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | 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% | Not measured | Only GPT-6 Luna measured |
| LMArena Text: math | 1409 | 1261 | GPT-6 Luna |
| LMArena Text: mathematical work | 1427 | 1271 | GPT-6 Luna |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| MedCode (Vals AI) | 44.7% | Not measured | Only GPT-6 Luna measured |
| SimpleQA Verified | 41.4% | Not measured | Only GPT-6 Luna measured |
| LMArena Text: science | 1386 | 1265 | GPT-6 Luna |
| LMArena Text: medicine and healthcare | 1352 | 1267 | GPT-6 Luna |
| LMArena Text: legal and government | 1400 | 1271 | GPT-6 Luna |
| LMArena Text: business and finance | 1388 | 1257 | GPT-6 Luna |
| FACTS Benchmark Suite | 63.3% | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 1271 | Not measured | Only GPT-6 Luna measured |
| Design Arena: UI components | 1295 | Not measured | Only GPT-6 Luna measured |
| Design Arena: game dev | 1257 | Not measured | Only GPT-6 Luna measured |
| Design Arena: data viz | 1216 | Not measured | Only GPT-6 Luna measured |
| Design Arena: 3D | 1218 | Not measured | Only GPT-6 Luna measured |
| Design Arena: all code categories | 1270 | Not measured | Only GPT-6 Luna measured |
| Design Arena: SVG | 1252 | Not measured | Only GPT-6 Luna measured |
| Design Arena: ASCII art | 1153 | Not measured | Only GPT-6 Luna measured |
| LiveBench design | 82.5% | Not measured | Only GPT-6 Luna measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index220
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | 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 |
| Long context reasoning (AA-LCR) | 83.3% | Not measured | Only GPT-6 Luna measured |
| DTBench | 90.1% | Not measured | Only GPT-6 Luna measured |
| LMCA | 44.5% | Not measured | Only GPT-6 Luna measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-6 Luna measured |
| LMArena Text | 1389 | 1277 | GPT-6 Luna |
| LMArena Text: instruction following | 1408 | 1263 | GPT-6 Luna |
| LMArena Text: creative writing | 1363 | 1252 | GPT-6 Luna |
| LMArena Vision | 1216 | 1136 | GPT-6 Luna |
| LMArena Text: English | 1393 | 1295 | GPT-6 Luna |
| LMArena Text: non-English | 1381 | 1255 | GPT-6 Luna |
| LMArena Text: Chinese | 1427 | 1253 | GPT-6 Luna |
| LMArena Text: German | 1376 | 1266 | GPT-6 Luna |
| LMArena Text: Japanese | 1369 | 1208 | GPT-6 Luna |
| LMArena Text: Korean | 1359 | 1207 | GPT-6 Luna |
| LMArena Text: French | 1408 | 1273 | GPT-6 Luna |
| LMArena Text: Spanish | 1391 | 1284 | GPT-6 Luna |
| LMArena Text: Russian | 1392 | 1263 | GPT-6 Luna |
| LMArena Text: Polish | 1383 | 1242 | GPT-6 Luna |
| LMArena Text: multi-turn | 1392 | 1270 | GPT-6 Luna |
| LMArena Text: longer queries | 1408 | 1299 | GPT-6 Luna |
| LMArena Text: writing and language | 1384 | 1268 | GPT-6 Luna |
| LMArena Text: entertainment and media | 1364 | 1250 | GPT-6 Luna |
| LMArena Vision: English | 1204 | 1157 | GPT-6 Luna |
| LMArena Vision: Chinese | 1266 | 1155 | GPT-6 Luna |
| LMArena Vision: reading text | 1225 | 1132 | GPT-6 Luna |
| LMArena Vision: humor | Not measured | 1142 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: captioning | Not measured | 1153 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: recognizing things | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: creative writing | 1205 | 1123 | GPT-6 Luna |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6 Luna | Mistral Small 3.1 24B | 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$0.0003Fits in one request
Mistral Small 3.1 24B$0.0006Fits in one request
GPT-6 Luna costs less
Repository review
50K input + 3K output tokens
GPT-6 Luna$0.0065Fits in one request
Mistral Small 3.1 24B$0.019Fits in one request
GPT-6 Luna costs less
Agent loop
200K input + 10K output tokens
GPT-6 Luna$0.025Fits in one request
Mistral Small 3.1 24B$0.076Exceeds the context window
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 Small 3.1 24B |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | Open weights |
| Released | September 22, 2026 | March 17, 2025 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $0.10 | $0.351 |
| Output $/Mtok | $0.50 | $0.555 |
| 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 Small 3.1 24B for coding?
Only GPT-6 Luna has a coding score, 57.3 on the Vibe Coding Index; Mistral Small 3.1 24B has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-6 Luna or Mistral Small 3.1 24B?
GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens; Mistral Small 3.1 24B costs $0.351 and $0.555. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6 Luna or Mistral Small 3.1 24B?
GPT-6 Luna, with 1.05M tokens against 128K.
How many benchmarks is the GPT-6 Luna vs Mistral Small 3.1 24B comparison based on?
35 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 Small 3.1 24B's on 0. Where only one model has a result, the page says so instead of naming a winner.