GPT-5.6 Luna vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads GPT-5.6 Luna on coding, 65.4 to 58.0 on the Vibe Coding Index, and MiMo-V2.6-Pro costs 1.4x less per output token ($0.87 vs $1.20 per million). Updated October 22, 2026; 69 benchmarks have a result for both.
GPT-5.6 Luna or MiMo-V2.6-Pro: which to pick
Pick MiMo-V2.6-Pro for coding: it scores 65.4 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 4 benchmarks against 6.
Pick GPT-5.6 Luna for work measured by Code migration (Vals AI) or IOI (Vals AI), where it leads.
- Shared results
- 69
- GPT-5.6 Luna only
- 51
- MiMo-V2.6-Pro only
- 4
- 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
MiMo-V2.6-Pro
MiMo-V2.6-Pro scores 65.4 against 58.0.
6 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 46.3 against 37.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Luna has a AA Agentic Index result, 42.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Luna has a GPQA Diamond result, 91.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Luna has a MMLU Pro result, 86.0%.
Directional only
Web design
Design Arena: websites
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 1307 against 1255.
Same board, both measured
General
Vals Index
No call
Only MiMo-V2.6-Pro has a Vals Index result, 57.8%.
Directional only
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna $0.0008, MiMo-V2.6-Pro $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Luna has the number, $0.04.
Directional only
At a glance
- Coding index
- GPT-5.6 Luna 58.0 from 6 benchmarks, MiMo-V2.6-Pro 65.4 from 4 benchmarks.
- Widest category
- Web design, 12.5 points apart on average over 7 shared benchmarks.
- Widest single gap
- Terminal-Bench 4 (Vals AI): GPT-5.6 Luna 11.6% against MiMo-V2.6-Pro 31.3%, 26.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5.6 Luna $1.20 against MiMo-V2.6-Pro $0.87 per million output tokens, 1.4x apart.
- Context window
- Both take 1.05M tokens.
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 | MiMo-V2.6-Pro | Ties |
|---|---|---|---|---|
| Coding | 5 | 11 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 9 | ||
| Reasoning | 2 | 6 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 5 | ||
| Web design | 0 | 7 | ||
| General | 1 | 18 | ||
| All shared boards | 9 | 60 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index511
| Benchmark | GPT-5.6 Luna | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench coding | 73.6% | 81.5% | MiMo-V2.6-Pro |
| LMArena WebDev | 1518 | 1625 | MiMo-V2.6-Pro |
| AA Coding Index | 71.4 | Not measured | Only GPT-5.6 Luna measured |
| SWE-bench Verified (Vals AI) | 93.0% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench 4 (Vals AI) | 11.6% | 31.3% | MiMo-V2.6-Pro |
| Vibe Code Bench (Vals AI) | 77.1% | 85.2% | MiMo-V2.6-Pro |
| Code migration (Vals AI) | 44.5% | 43.0% | GPT-5.6 Luna |
| ProgramBench (Vals AI) | 0.0% | 0.5% | MiMo-V2.6-Pro |
| IOI (Vals AI) | 61.8% | 39.3% | GPT-5.6 Luna |
| Terminal-Bench 2.1 (Vals AI) | 79.0% | 67.8% | GPT-5.6 Luna |
| SWE-bench Multimodal (Vals AI) | Not measured | 41.3% | Only MiMo-V2.6-Pro measured |
| Vibe Code Bench 1-100 (Vals AI) | 22.6% | Not measured | Only GPT-5.6 Luna measured |
| SkillsBench (Vals AI) | 60.5% | Not measured | Only GPT-5.6 Luna measured |
| CyberBench (Vals AI) | 73.6% | 72.9% | GPT-5.6 Luna |
| SciCode | 53.6% | 60.9% | MiMo-V2.6-Pro |
| FrontierCode | 39.8% | Not measured | Only GPT-5.6 Luna measured |
| WeirdML | 60.9% | Not measured | Only GPT-5.6 Luna measured |
| ALE-Bench | 1667.4 | 1158.1 | GPT-5.6 Luna |
| LMArena Text: coding | 1466 | 1533 | MiMo-V2.6-Pro |
| LMArena Text: software and IT | 1463 | 1528 | MiMo-V2.6-Pro |
| LMArena WebDev: React | 1508 | 1639 | MiMo-V2.6-Pro |
| LMArena WebDev: HTML | 1520 | 1616 | MiMo-V2.6-Pro |
| LMArena WebDev: from an image | 1517 | 1598 | MiMo-V2.6-Pro |
| SciCode | 6.2% | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.6 Luna | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 37.3 | 46.3 | MiMo-V2.6-Pro |
| Epoch Capabilities Index | 156.5 | Not measured | Only GPT-5.6 Luna measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index19
| Benchmark | GPT-5.6 Luna | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 42.1 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: agents building web apps | Not measured | 1290 | Only MiMo-V2.6-Pro measured |
| LiveBench research | 51.0% | 76.7% | MiMo-V2.6-Pro |
| LiveBench browser | 68.0% | 88.0% | MiMo-V2.6-Pro |
| LiveBench long agentic | 30.4% | 47.5% | MiMo-V2.6-Pro |
| SREBench (Vals AI) | 1.5% | 3.0% | MiMo-V2.6-Pro |
| Finance Agent (Vals AI) | 55.0% | 57.3% | MiMo-V2.6-Pro |
| Tax Agent Bench (Vals AI) | 24.8% | 32.4% | MiMo-V2.6-Pro |
| Legal Research Bench (Vals AI) | 36.5% | 47.1% | MiMo-V2.6-Pro |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 13.8% | Only MiMo-V2.6-Pro measured |
| Excel Modeling Benchmark (Vals AI) | 67.1% | 62.9% | GPT-5.6 Luna |
| BioMysteryBench (Vals AI) | 61.5% | Not measured | Only GPT-5.6 Luna measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | 2.9% | MiMo-V2.6-Pro |
| Vals Science Index | 25.3% | Not measured | Only GPT-5.6 Luna measured |
| APEX-Agents | 43.0% | 54.4% | MiMo-V2.6-Pro |
| BALROG | 45.6% | Not measured | Only GPT-5.6 Luna measured |
| Vending-Bench 2 | $4,095 | Not measured | Only GPT-5.6 Luna measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index26
| Benchmark | GPT-5.6 Luna | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 91.7% | Not measured | Only GPT-5.6 Luna measured |
| LiveBench reasoning | 85.6% | 79.6% | GPT-5.6 Luna |
| MysteryMechanism (Vals AI) | 14.4% | 15.3% | MiMo-V2.6-Pro |
| MMMU Pro (Vals AI) | 85.0% | Not measured | Only GPT-5.6 Luna measured |
| GPQA Diamond (Artificial Analysis run) | 91.1% | Not measured | Only GPT-5.6 Luna measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.5% | 49.4% | MiMo-V2.6-Pro |
| GPQA Diamond (Epoch AI run) | 91.6% | Not measured | Only GPT-5.6 Luna measured |
| 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% | Not measured | Only GPT-5.6 Luna measured |
| Chess puzzles | 43.0% | Not measured | Only GPT-5.6 Luna measured |
| Mystery game puzzles | 21.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: hard prompts | 1451 | 1511 | MiMo-V2.6-Pro |
| LMArena Text: expert | 1478 | 1541 | MiMo-V2.6-Pro |
| LMArena Text: hard prompts in English | 1460 | 1520 | MiMo-V2.6-Pro |
| LMArena Vision: diagrams | 1273 | 1281 | MiMo-V2.6-Pro |
| LMArena Vision: homework | 1282 | 1264 | 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 index03
| Benchmark | GPT-5.6 Luna | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | Not measured | Only GPT-5.6 Luna measured |
| 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% | 70.0% | MiMo-V2.6-Pro |
| LMArena Text: math | 1459 | 1500 | MiMo-V2.6-Pro |
| LMArena Text: mathematical work | 1470 | 1511 | MiMo-V2.6-Pro |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-5.6 Luna | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | Not measured | Only GPT-5.6 Luna measured |
| LegalBench (Vals AI) | 84.0% | Not measured | Only GPT-5.6 Luna measured |
| CorpFin (Vals AI) | 64.2% | Not measured | Only GPT-5.6 Luna measured |
| TaxEval (Vals AI) | 76.2% | Not measured | Only GPT-5.6 Luna measured |
| MortgageTax (Vals AI) | 67.3% | Not measured | Only GPT-5.6 Luna measured |
| MedCode (Vals AI) | 42.4% | 45.0% | MiMo-V2.6-Pro |
| SimpleQA Verified | 41.0% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: science | 1433 | 1506 | MiMo-V2.6-Pro |
| LMArena Text: medicine and healthcare | 1399 | 1497 | MiMo-V2.6-Pro |
| LMArena Text: legal and government | 1446 | 1485 | MiMo-V2.6-Pro |
| LMArena Text: business and finance | 1439 | 1468 | MiMo-V2.6-Pro |
| 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 index07
| Benchmark | GPT-5.6 Luna | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1255 | 1307 | MiMo-V2.6-Pro |
| Design Arena: UI components | 1248 | 1337 | MiMo-V2.6-Pro |
| Design Arena: game dev | 1195 | 1313 | MiMo-V2.6-Pro |
| Design Arena: data viz | 1212 | 1337 | MiMo-V2.6-Pro |
| Design Arena: 3D | 1121 | 1329 | MiMo-V2.6-Pro |
| Design Arena: all code categories | 1230 | 1316 | MiMo-V2.6-Pro |
| Design Arena: SVG | 1193 | Not measured | Only GPT-5.6 Luna measured |
| Design Arena: ASCII art | 1142 | Not measured | Only GPT-5.6 Luna measured |
| LiveBench design | 76.0% | 78.5% | MiMo-V2.6-Pro |
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
| Benchmark | GPT-5.6 Luna | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Vals Index | Not measured | 57.8% | Only MiMo-V2.6-Pro measured |
| LiveBench data analysis | 78.0% | 72.3% | GPT-5.6 Luna |
| Public Benefits Bench (Vals AI) | 61.2% | 68.9% | MiMo-V2.6-Pro |
| SAGE (Vals AI) | 44.2% | 45.0% | MiMo-V2.6-Pro |
| MedScribe (Vals AI) | 84.4% | 88.3% | MiMo-V2.6-Pro |
| Vals Multimodal Index | 69.1% | Not measured | Only GPT-5.6 Luna measured |
| Long context reasoning (AA-LCR) | 83.7% | 86.3% | MiMo-V2.6-Pro |
| DTBench | 89.1% | Not measured | Only GPT-5.6 Luna measured |
| LMCA | 48.5% | Not measured | Only GPT-5.6 Luna measured |
| Surface Evolver Bench | 61.9% | Not measured | Only GPT-5.6 Luna measured |
| Furniture assembly | 42.5% | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text | 1431 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: instruction following | 1437 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: creative writing | 1396 | 1466 | MiMo-V2.6-Pro |
| LMArena Vision | 1257 | 1263 | MiMo-V2.6-Pro |
| LMArena Text: English | 1441 | 1504 | MiMo-V2.6-Pro |
| LMArena Text: non-English | 1417 | 1474 | MiMo-V2.6-Pro |
| LMArena Text: Chinese | 1470 | 1529 | MiMo-V2.6-Pro |
| LMArena Text: German | 1455 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Japanese | 1415 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Korean | 1415 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: French | 1458 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Spanish | 1446 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: Russian | 1427 | 1479 | MiMo-V2.6-Pro |
| LMArena Text: Polish | 1405 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Text: multi-turn | 1434 | 1466 | MiMo-V2.6-Pro |
| LMArena Text: longer queries | 1436 | 1501 | MiMo-V2.6-Pro |
| LMArena Text: writing and language | 1419 | 1470 | MiMo-V2.6-Pro |
| LMArena Text: entertainment and media | 1402 | 1465 | MiMo-V2.6-Pro |
| LMArena Vision: English | 1263 | 1268 | MiMo-V2.6-Pro |
| LMArena Vision: Chinese | 1294 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: reading text | 1264 | 1270 | MiMo-V2.6-Pro |
| LMArena Vision: humor | 1190 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Vision: creative writing | 1257 | Not measured | Only GPT-5.6 Luna measured |
| LMArena Document | 1457 | Not measured | Only GPT-5.6 Luna measured |
| EQ-Bench 4 | 1156 | Not measured | Only GPT-5.6 Luna measured |
| 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-5.6 Luna | MiMo-V2.6-Pro | 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 | Xiaomi MiMo-V2.6-Pro |
|---|---|---|
| Maker | OpenAI | Xiaomi |
| Weights | No public weights listed | Open weights |
| Released | July 9, 2026 | September 21, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.20 | $0.435 |
| Output $/Mtok | $1.20 | $0.87 |
| Coding index | 58.0 | 65.4 |
| Independent measurements behind it | 6 | 4 |
| Cost per solved task | $0.04 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.6 Luna better than MiMo-V2.6-Pro for coding?
Pick MiMo-V2.6-Pro for coding: it scores 65.4 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 4 benchmarks against 6. Pick GPT-5.6 Luna for work measured by Code migration (Vals AI) or IOI (Vals AI), where it leads.
Which is cheaper, GPT-5.6 Luna or MiMo-V2.6-Pro?
GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; MiMo-V2.6-Pro costs $0.435 and $0.87. Cost per solved task is known for GPT-5.6 Luna only.
Which has the bigger context window, GPT-5.6 Luna or MiMo-V2.6-Pro?
Neither: both take 1.05M tokens.
How many benchmarks is the GPT-5.6 Luna vs MiMo-V2.6-Pro comparison based on?
69 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 MiMo-V2.6-Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.