GPT-5.6 Luna vs MiniMax M3
GPT-5.6 Luna leads MiniMax M3 on coding, 58.0 to 39.2 on the Vibe Coding Index, at about the same output price ($1.20 vs $1.20 per million). Updated October 22, 2026; 102 benchmarks have a result for both.
GPT-5.6 Luna or MiniMax M3: which to pick
Pick GPT-5.6 Luna for coding: it scores 58.0 on the Vibe Coding Index against 39.2 for MiniMax M3.
Pick MiniMax M3 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 102
- GPT-5.6 Luna only
- 18
- MiniMax M3 only
- 15
- Like-for-like categories
- 8 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
GPT-5.6 Luna
GPT-5.6 Luna scores 58.0 against 39.2.
6 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 37.3 against 29.2.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Luna
GPT-5.6 Luna leads, 42.1 against 29.5.
Same board, both measured
Reasoning
GPQA Diamond
MiniMax M3
MiniMax M3 leads, 92.7% against 91.7%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.6 Luna
GPT-5.6 Luna leads, 86.0% against 84.2%.
Same board, both measured
Web design
Design Arena: websites
MiniMax M3
MiniMax M3 leads, 1257 against 1255.
Same board, both measured
Long documents
Listed context window
GPT-5.6 Luna
GPT-5.6 Luna takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna $0.0008, MiniMax M3 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.6 Luna
GPT-5.6 Luna $0.04, MiniMax M3 $0.56.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.6 Luna 58.0 from 6 benchmarks, MiniMax M3 39.2 from 6 benchmarks.
- Widest category
- Math, 19.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- LiveBench browser: GPT-5.6 Luna 68.0% against MiniMax M3 8.0%, 60.0 points apart on a 0 to 100 scale.
- Output price
- Both cost $1.20 per million output tokens.
- Context window
- GPT-5.6 Luna takes 1.05M tokens, MiniMax M3 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 | GPT-5.6 Luna | Bars | MiniMax M3 | Ties |
|---|---|---|---|---|
| Coding | 17 | 2 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 9 | 1 | ||
| Reasoning | 12 | 2 | ||
| Math | 4 | 0 | ||
| Knowledge | 4 | 6 | ||
| Web design | 3 | 6 | ||
| General | 19 | 15 | ||
| All shared boards | 70 | 32 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index172
| Benchmark | GPT-5.6 Luna | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench coding | 73.6% | 70.9% | GPT-5.6 Luna |
| LMArena WebDev | 1518 | 1482 | GPT-5.6 Luna |
| AA Coding Index | 71.4 | 58.6 | GPT-5.6 Luna |
| LiveCodeBench (Vals AI) | Not measured | 82.2% | Only MiniMax M3 measured |
| SWE-bench Verified (Vals AI) | 93.0% | 75.0% | GPT-5.6 Luna |
| Terminal-Bench 4 (Vals AI) | 11.6% | 1.0% | GPT-5.6 Luna |
| Vibe Code Bench (Vals AI) | 77.1% | 47.6% | GPT-5.6 Luna |
| Code migration (Vals AI) | 44.5% | 19.9% | GPT-5.6 Luna |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GPT-5.6 Luna measured |
| IOI (Vals AI) | 61.8% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench 2.1 (Vals AI) | 79.0% | 53.6% | GPT-5.6 Luna |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.3% | Only MiniMax M3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 22.6% | 9.2% | GPT-5.6 Luna |
| SkillsBench (Vals AI) | 60.5% | 51.5% | GPT-5.6 Luna |
| CyberBench (Vals AI) | 73.6% | 65.0% | GPT-5.6 Luna |
| SciCode | 53.6% | 47.1% | GPT-5.6 Luna |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 42.4% | Only MiniMax M3 measured |
| FrontierCode | 39.8% | 14.7% | GPT-5.6 Luna |
| WeirdML | 60.9% | Not measured | Only GPT-5.6 Luna measured |
| ALE-Bench | 1667.4 | 640.0 | GPT-5.6 Luna |
| LMArena Text: coding | 1466 | 1470 | MiniMax M3 |
| LMArena Text: software and IT | 1463 | 1466 | MiniMax M3 |
| LMArena WebDev: React | 1508 | 1478 | GPT-5.6 Luna |
| LMArena WebDev: HTML | 1520 | 1455 | GPT-5.6 Luna |
| LMArena WebDev: from an image | 1517 | 1470 | GPT-5.6 Luna |
| SciCode | 6.2% | Not measured | Only GPT-5.6 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-5.6 Luna | MiniMax M3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 37.3 | 29.2 | GPT-5.6 Luna |
| Epoch Capabilities Index | 156.5 | 146.9 | GPT-5.6 Luna |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index91
| Benchmark | GPT-5.6 Luna | MiniMax M3 | Leader |
|---|---|---|---|
| AA Agentic Index | 42.1 | 29.5 | GPT-5.6 Luna |
| Design Arena: agents building web apps | Not measured | 1200 | Only MiniMax M3 measured |
| Design Arena: agents building full-stack apps | Not measured | 1197 | Only MiniMax M3 measured |
| Design Arena: agents building mobile apps | Not measured | 1169 | Only MiniMax M3 measured |
| Design Arena: agents building Android apps | Not measured | 1158 | Only MiniMax M3 measured |
| Design Arena: agents building games | Not measured | 1159 | Only MiniMax M3 measured |
| Design Arena: HTML slides | Not measured | 1120 | Only MiniMax M3 measured |
| Design Arena: PowerPoint slides | Not measured | 1178 | Only MiniMax M3 measured |
| Design Arena: spreadsheets | Not measured | 1042 | Only MiniMax M3 measured |
| LiveBench research | 51.0% | 68.0% | MiniMax M3 |
| LiveBench browser | 68.0% | 8.0% | GPT-5.6 Luna |
| LiveBench long agentic | 30.4% | 12.4% | GPT-5.6 Luna |
| SREBench (Vals AI) | 1.5% | Not measured | Only GPT-5.6 Luna measured |
| Finance Agent (Vals AI) | 55.0% | 48.3% | GPT-5.6 Luna |
| Tax Agent Bench (Vals AI) | 24.8% | 20.6% | GPT-5.6 Luna |
| Legal Research Bench (Vals AI) | 36.5% | 29.8% | GPT-5.6 Luna |
| Excel Modeling Benchmark (Vals AI) | 67.1% | 47.8% | GPT-5.6 Luna |
| BioMysteryBench (Vals AI) | 61.5% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only GPT-5.6 Luna measured |
| Vals Science Index | 25.3% | Not measured | Only GPT-5.6 Luna measured |
| τ²-Bench Telecom | Not measured | 88.9% | Only MiniMax M3 measured |
| APEX-Agents | 43.0% | 37.7% | GPT-5.6 Luna |
| OSWorld 2.0 | Not measured | 4.6% | Only MiniMax M3 measured |
| BALROG | 45.6% | Not measured | Only GPT-5.6 Luna measured |
| Vending-Bench 2 | $4,095 | $2,158 | GPT-5.6 Luna |
| Neither model has a result on the other 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index122
| Benchmark | GPT-5.6 Luna | MiniMax M3 | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | 92.7% | MiniMax M3 |
| LiveBench reasoning | 85.6% | 74.5% | GPT-5.6 Luna |
| MysteryMechanism (Vals AI) | 14.4% | Not measured | Only GPT-5.6 Luna measured |
| MMMU Pro (Vals AI) | 85.0% | 81.2% | GPT-5.6 Luna |
| GPQA Diamond (Artificial Analysis run) | 91.1% | 92.9% | MiniMax M3 |
| Humanity's Last Exam (Artificial Analysis run) | 39.5% | 39.0% | GPT-5.6 Luna |
| GPQA Diamond (Epoch AI run) | 91.6% | 90.9% | GPT-5.6 Luna |
| ARC-AGI | 88.0% | Not measured | Only GPT-5.6 Luna measured |
| ARC-AGI-2 | 59.5% | Not measured | Only GPT-5.6 Luna measured |
| SimpleBench | 46.8% | 45.8% | GPT-5.6 Luna |
| Chess puzzles | 43.0% | 13.0% | GPT-5.6 Luna |
| Mystery game puzzles | 21.0% | 8.0% | GPT-5.6 Luna |
| LMArena Text: hard prompts | 1451 | 1447 | GPT-5.6 Luna |
| LMArena Text: expert | 1478 | 1461 | GPT-5.6 Luna |
| LMArena Text: hard prompts in English | 1460 | 1455 | GPT-5.6 Luna |
| LMArena Vision: diagrams | 1273 | 1266 | GPT-5.6 Luna |
| LMArena Vision: homework | 1282 | 1268 | GPT-5.6 Luna |
| Kagi LLM Benchmark | 49.1% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-5.6 Luna | MiniMax M3 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | 71.1% | GPT-5.6 Luna |
| FrontierMath, tiers 1 to 3 | 82.1% | Not measured | Only GPT-5.6 Luna measured |
| FrontierMath, tier 4 | 61.0% | Not measured | Only GPT-5.6 Luna measured |
| ProofBench | 60.0% | 18.0% | GPT-5.6 Luna |
| LMArena Text: math | 1459 | 1429 | GPT-5.6 Luna |
| LMArena Text: mathematical work | 1470 | 1448 | GPT-5.6 Luna |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
| Benchmark | GPT-5.6 Luna | MiniMax M3 | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | 84.2% | GPT-5.6 Luna |
| LegalBench (Vals AI) | 84.0% | 85.4% | MiniMax M3 |
| CorpFin (Vals AI) | 64.2% | 68.1% | MiniMax M3 |
| TaxEval (Vals AI) | 76.2% | 72.7% | GPT-5.6 Luna |
| MortgageTax (Vals AI) | 67.3% | 68.4% | MiniMax M3 |
| MedCode (Vals AI) | 42.4% | 46.3% | MiniMax M3 |
| SimpleQA Verified | 41.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: science | 1433 | 1452 | MiniMax M3 |
| LMArena Text: medicine and healthcare | 1399 | 1441 | MiniMax M3 |
| LMArena Text: legal and government | 1446 | 1437 | GPT-5.6 Luna |
| LMArena Text: business and finance | 1439 | 1434 | GPT-5.6 Luna |
| FACTS Benchmark Suite | 35.0% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index36
| Benchmark | GPT-5.6 Luna | MiniMax M3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1255 | 1257 | MiniMax M3 |
| Design Arena: UI components | 1248 | 1243 | GPT-5.6 Luna |
| Design Arena: game dev | 1195 | 1210 | MiniMax M3 |
| Design Arena: data viz | 1212 | 1236 | MiniMax M3 |
| Design Arena: 3D | 1121 | 1208 | MiniMax M3 |
| Design Arena: all code categories | 1230 | 1246 | MiniMax M3 |
| Design Arena: SVG | 1193 | 1160 | GPT-5.6 Luna |
| Design Arena: ASCII art | 1142 | 1161 | MiniMax M3 |
| LiveBench design | 76.0% | 66.2% | GPT-5.6 Luna |
GeneralLike-for-likePublic boards, shown as published, kept out of the index1915
| Benchmark | GPT-5.6 Luna | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench data analysis | 78.0% | 76.2% | GPT-5.6 Luna |
| Public Benefits Bench (Vals AI) | 61.2% | 64.1% | MiniMax M3 |
| SAGE (Vals AI) | 44.2% | 50.6% | MiniMax M3 |
| MedScribe (Vals AI) | 84.4% | 87.3% | MiniMax M3 |
| Vals Multimodal Index | 69.1% | 60.0% | GPT-5.6 Luna |
| IFBench | Not measured | 82.9% | Only MiniMax M3 measured |
| Long context reasoning (AA-LCR) | 83.7% | 83.0% | GPT-5.6 Luna |
| DTBench | 89.1% | 78.9% | GPT-5.6 Luna |
| LMCA | 48.5% | 33.7% | GPT-5.6 Luna |
| Surface Evolver Bench | 61.9% | 55.0% | GPT-5.6 Luna |
| Furniture assembly | 42.5% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text | 1431 | 1433 | MiniMax M3 |
| LMArena Text: instruction following | 1437 | 1433 | GPT-5.6 Luna |
| LMArena Text: creative writing | 1396 | 1404 | MiniMax M3 |
| LMArena Vision | 1257 | 1251 | GPT-5.6 Luna |
| LMArena Text: English | 1441 | 1443 | MiniMax M3 |
| LMArena Text: non-English | 1417 | 1420 | MiniMax M3 |
| LMArena Text: Chinese | 1470 | 1466 | GPT-5.6 Luna |
| LMArena Text: German | 1455 | 1428 | GPT-5.6 Luna |
| LMArena Text: Japanese | 1415 | 1380 | GPT-5.6 Luna |
| LMArena Text: Korean | 1415 | 1373 | GPT-5.6 Luna |
| LMArena Text: French | 1458 | 1444 | GPT-5.6 Luna |
| LMArena Text: Spanish | 1446 | 1433 | GPT-5.6 Luna |
| LMArena Text: Russian | 1427 | 1426 | GPT-5.6 Luna |
| LMArena Text: Polish | 1405 | 1423 | MiniMax M3 |
| LMArena Text: multi-turn | 1434 | 1443 | MiniMax M3 |
| LMArena Text: longer queries | 1436 | 1446 | MiniMax M3 |
| LMArena Text: writing and language | 1419 | 1419 | MiniMax M3 |
| LMArena Text: entertainment and media | 1402 | 1403 | MiniMax M3 |
| LMArena Vision: English | 1263 | 1263 | MiniMax M3 |
| LMArena Vision: Chinese | 1294 | 1311 | MiniMax M3 |
| LMArena Vision: reading text | 1264 | 1259 | GPT-5.6 Luna |
| LMArena Vision: humor | 1190 | 1235 | MiniMax M3 |
| LMArena Vision: recognizing things | Not measured | 1197 | Only MiniMax M3 measured |
| LMArena Vision: creative writing | 1257 | 1234 | GPT-5.6 Luna |
| LMArena Document | 1457 | 1435 | GPT-5.6 Luna |
| EQ-Bench 4 | 1156 | 1150 | GPT-5.6 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-5.6 Luna | MiniMax M3 | 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-5.6 Luna costs less
Repository review
50K input + 3K output tokens
GPT-5.6 Luna costs less
Agent loop
200K input + 10K output tokens
GPT-5.6 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-5.6 Luna | MiniMax M3 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | May 31, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.20 | $0.30 |
| Output $/Mtok | $1.20 | $1.20 |
| Coding index | 58.0 | 39.2 |
| Independent measurements behind it | 6 | 6 |
| Cost per solved task | $0.04 | $0.56 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than MiniMax M3 for coding?
Pick GPT-5.6 Luna for coding: it scores 58.0 on the Vibe Coding Index against 39.2 for MiniMax M3. Pick MiniMax M3 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-5.6 Luna or MiniMax M3?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; MiniMax M3 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GPT-5.6 Luna costs $0.04 and MiniMax M3 $0.56.
Which has the bigger context window, GPT-5.6 Luna or MiniMax M3?
GPT-5.6 Luna, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-5.6 Luna vs MiniMax M3 comparison based on?
102 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Luna's coding index rests on 6 independent benchmarks and MiniMax M3's on 6. Where only one model has a result, the page says so instead of naming a winner.