GPT-6 Luna vs MiniMax M2.5
GPT-6 Luna leads MiniMax M2.5 on coding, 57.3 to 37.2 on the Vibe Coding Index, and GPT-6 Luna costs 2.4x less per output token ($0.50 vs $1.20 per million). Updated October 22, 2026; 47 benchmarks have a result for both.
GPT-6 Luna or MiniMax M2.5: which to pick
Pick GPT-6 Luna for coding: it scores 57.3 on the Vibe Coding Index against 37.2 for MiniMax M2.5.
Pick MiniMax M2.5 for work measured by LMArena Text: medicine and healthcare, where it leads.
- Shared results
- 47
- GPT-6 Luna only
- 57
- MiniMax M2.5 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
GPT-6 Luna
GPT-6 Luna scores 57.3 against 37.2.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6 Luna
GPT-6 Luna leads, 38.1 against 22.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only MiniMax M2.5 has a GPQA Diamond result, 82.1%.
Directional only
Math
AIME
No call
Only MiniMax M2.5 has a AIME result, 88.8%.
Directional only
Knowledge
MMLU Pro
No call
Only MiniMax M2.5 has a MMLU Pro result, 80.1%.
Directional only
Web design
Design Arena: websites
GPT-6 Luna
GPT-6 Luna leads, 1271 against 1222.
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 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna
GPT-6 Luna $0.0003, MiniMax M2.5 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2.5 has the number, $0.62.
Directional only
At a glance
- Coding index
- GPT-6 Luna 57.3 from 5 benchmarks, MiniMax M2.5 37.2 from 3 benchmarks.
- Widest category
- Coding, 29.4 points apart on average over 7 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GPT-6 Luna 81.7% against MiniMax M2.5 14.8%, 66.8 points apart on a 0 to 100 scale.
- Output price
- GPT-6 Luna $0.50 against MiniMax M2.5 $1.20 per million output tokens, 2.4x apart.
- Context window
- GPT-6 Luna takes 1.05M tokens, MiniMax M2.5 205K.
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 | MiniMax M2.5 | Ties |
|---|---|---|---|---|
| Coding | 7 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 3 | 1 | ||
| Web design | 7 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 46 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
| Benchmark | GPT-6 Luna | MiniMax M2.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 75.8% | Only MiniMax M2.5 measured |
| SWE-bench Multilingual | Not measured | 68.3% | Only MiniMax M2.5 measured |
| LiveBench coding | 84.5% | Not measured | Only GPT-6 Luna measured |
| LMArena WebDev | 1578 | 1386 | GPT-6 Luna |
| LiveCodeBench (Vals AI) | Not measured | 79.2% | Only MiniMax M2.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 74.2% | Only MiniMax M2.5 measured |
| Terminal-Bench 4 (Vals AI) | 13.6% | Not measured | Only GPT-6 Luna measured |
| Vibe Code Bench (Vals AI) | 81.7% | 14.8% | GPT-6 Luna |
| 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 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only MiniMax M2.5 measured |
| Terminal-Bench | Not measured | 42.7% | Only MiniMax M2.5 measured |
| FrontierCode | 42.4% | Not measured | Only GPT-6 Luna measured |
| ALE-Bench | 1576.9 | 618.2 | GPT-6 Luna |
| LMArena Text: coding | 1437 | 1380 | GPT-6 Luna |
| LMArena Text: software and IT | 1423 | 1383 | GPT-6 Luna |
| LMArena WebDev: React | 1573 | 1374 | GPT-6 Luna |
| LMArena WebDev: HTML | 1595 | 1414 | GPT-6 Luna |
| LMArena WebDev: from an image | 1571 | Not measured | Only GPT-6 Luna measured |
| SWE Atlas: Codebase QnA | Not measured | 10.3% | Only MiniMax M2.5 measured |
| SWE Atlas: Refactoring | Not measured | 19.5% | Only MiniMax M2.5 measured |
| SWE Atlas: Test Writing | Not measured | 18.6% | Only MiniMax M2.5 measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6 Luna | MiniMax M2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.1 | 22.8 | GPT-6 Luna |
| Epoch Capabilities Index | 156.8 | 146.6 | GPT-6 Luna |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GPT-6 Luna | MiniMax M2.5 | 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 | 1131 | 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 | 95.3% | Only MiniMax M2.5 measured |
| APEX-Agents | 44.3% | Not measured | Only GPT-6 Luna measured |
| SpireClimb | 0.0% | Not measured | Only GPT-6 Luna measured |
| Vending-Bench 2 | Not measured | -$23 | Only MiniMax M2.5 measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-6 Luna | MiniMax M2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.1% | Only MiniMax M2.5 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 | 84.8% | Only MiniMax M2.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 38.5% | 20.5% | GPT-6 Luna |
| GPQA Diamond (Epoch AI run) | 90.5% | Not measured | Only GPT-6 Luna measured |
| ARC-AGI | 86.7% | 63.7% | GPT-6 Luna |
| ARC-AGI-2 | 59.3% | 4.9% | GPT-6 Luna |
| 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 | 1372 | GPT-6 Luna |
| LMArena Text: expert | 1442 | 1378 | GPT-6 Luna |
| LMArena Text: hard prompts in English | 1410 | 1384 | GPT-6 Luna |
| LMArena Vision: diagrams | 1245 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: homework | 1261 | Not measured | Only GPT-6 Luna measured |
| Kagi LLM Benchmark | Not measured | 55.2% | Only MiniMax M2.5 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-6 Luna | MiniMax M2.5 | Leader |
|---|---|---|---|
| AIME | Not measured | 88.8% | Only MiniMax M2.5 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% | 4.0% | GPT-6 Luna |
| LMArena Text: math | 1409 | 1378 | GPT-6 Luna |
| LMArena Text: mathematical work | 1427 | 1378 | 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 index31
| Benchmark | GPT-6 Luna | MiniMax M2.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 80.1% | Only MiniMax M2.5 measured |
| LegalBench (Vals AI) | Not measured | 80.0% | Only MiniMax M2.5 measured |
| CaseLaw (Vals AI) | Not measured | 53.5% | Only MiniMax M2.5 measured |
| CorpFin (Vals AI) | Not measured | 59.6% | Only MiniMax M2.5 measured |
| TaxEval (Vals AI) | Not measured | 68.2% | Only MiniMax M2.5 measured |
| MedQA (Vals AI) | Not measured | 92.5% | Only MiniMax M2.5 measured |
| 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 | 1369 | GPT-6 Luna |
| LMArena Text: medicine and healthcare | 1352 | 1357 | MiniMax M2.5 |
| LMArena Text: legal and government | 1400 | 1366 | GPT-6 Luna |
| LMArena Text: business and finance | 1388 | 1356 | GPT-6 Luna |
| FACTS Benchmark Suite | 63.3% | Not measured | Only GPT-6 Luna measured |
| PRBench Legal | 42.9% | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GPT-6 Luna | MiniMax M2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1271 | 1222 | GPT-6 Luna |
| Design Arena: UI components | 1295 | 1170 | GPT-6 Luna |
| Design Arena: game dev | 1258 | 1176 | GPT-6 Luna |
| Design Arena: data viz | 1217 | 1172 | GPT-6 Luna |
| Design Arena: 3D | 1218 | 1170 | GPT-6 Luna |
| Design Arena: all code categories | 1270 | 1207 | GPT-6 Luna |
| Design Arena: SVG | 1253 | 1150 | GPT-6 Luna |
| 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 index180
| Benchmark | GPT-6 Luna | MiniMax M2.5 | 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% | Not measured | Only GPT-6 Luna measured |
| MedScribe (Vals AI) | 83.7% | Not measured | Only GPT-6 Luna measured |
| IFBench | Not measured | 71.6% | Only MiniMax M2.5 measured |
| Long context reasoning (AA-LCR) | 83.3% | 73.3% | GPT-6 Luna |
| DTBench | 90.1% | Not measured | Only GPT-6 Luna measured |
| LMCA | 44.5% | Not measured | Only GPT-6 Luna measured |
| CL-bench | Not measured | 11.4% | Only MiniMax M2.5 measured |
| Furniture assembly | 44.2% | Not measured | Only GPT-6 Luna measured |
| CL-bench Life | Not measured | 6.3% | Only MiniMax M2.5 measured |
| LMArena Text | 1389 | 1359 | GPT-6 Luna |
| LMArena Text: instruction following | 1408 | 1353 | GPT-6 Luna |
| LMArena Text: creative writing | 1363 | 1331 | GPT-6 Luna |
| LMArena Vision | 1216 | Not measured | Only GPT-6 Luna measured |
| LMArena Text: English | 1393 | 1380 | GPT-6 Luna |
| LMArena Text: non-English | 1381 | 1338 | GPT-6 Luna |
| LMArena Text: Chinese | 1427 | 1393 | GPT-6 Luna |
| LMArena Text: German | 1376 | 1361 | GPT-6 Luna |
| LMArena Text: Japanese | 1369 | 1171 | GPT-6 Luna |
| LMArena Text: Korean | 1359 | 1233 | GPT-6 Luna |
| LMArena Text: French | 1408 | 1363 | GPT-6 Luna |
| LMArena Text: Spanish | 1391 | 1353 | GPT-6 Luna |
| LMArena Text: Russian | 1392 | 1359 | GPT-6 Luna |
| LMArena Text: Polish | 1383 | 1334 | GPT-6 Luna |
| LMArena Text: multi-turn | 1392 | 1364 | GPT-6 Luna |
| LMArena Text: longer queries | 1408 | 1366 | GPT-6 Luna |
| LMArena Text: writing and language | 1384 | 1345 | GPT-6 Luna |
| LMArena Text: entertainment and media | 1364 | 1329 | GPT-6 Luna |
| LMArena Vision: English | 1204 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: Chinese | 1266 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: reading text | 1225 | Not measured | Only GPT-6 Luna measured |
| LMArena Vision: creative writing | 1205 | Not measured | Only GPT-6 Luna measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6 Luna | MiniMax M2.5 | 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 | MiniMax M2.5 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | September 22, 2026 | February 12, 2026 |
| Context window | 1.05M | 205K |
| Input $/Mtok | $0.10 | $0.30 |
| Output $/Mtok | $0.50 | $1.20 |
| Coding index | 57.3 | 37.2 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | Not computable | $0.62 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6 Luna better than MiniMax M2.5 for coding?
Pick GPT-6 Luna for coding: it scores 57.3 on the Vibe Coding Index against 37.2 for MiniMax M2.5. Pick MiniMax M2.5 for work measured by LMArena Text: medicine and healthcare, where it leads.
Which is cheaper, GPT-6 Luna or MiniMax M2.5?
GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens; MiniMax M2.5 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.5 only.
Which has the bigger context window, GPT-6 Luna or MiniMax M2.5?
GPT-6 Luna, with 1.05M tokens against 205K.
How many benchmarks is the GPT-6 Luna vs MiniMax M2.5 comparison based on?
47 benchmarks have a result for both models as of October 22, 2026. GPT-6 Luna's coding index rests on 5 independent benchmarks and MiniMax M2.5's on 3. Where only one model has a result, the page says so instead of naming a winner.