Model comparison
GPT-5.6 Luna vs MiMo-V2-Pro
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 43.0 on the Noometry Index.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GPT-5.6 Luna scores higher in 7 categories and MiMo-V2-Pro in 1 category; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Luna leads 77.7 to 39.5.
- The biggest single-benchmark swing is NYT Connections (extended): 69.4% for GPT-5.6 Luna and 25.8% for MiMo-V2-Pro.
- GPT-5.6 Luna is cheaper at $0.20 / $1.20 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 1.05M.
Side by side
| GPT-5.6 Luna | MiMo-V2-Pro | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 54.6 | 43.0 |
| Released | 2026-07-09 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.20 | $0.43 |
| Output $ / M tokens | $1.20 | $0.87 |
| Results tracked | 52 | 23 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 54.5 (#28), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena WebDev | 1519 | 1433 |
| LMArena Coding | 1466 | 1476 |
| ALE-Bench | 1,667 | 785.17 |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| SciCode | 53.6% | — |
| WeirdML | 60.9% | — |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 34.4 (#45), MiMo-V2-Pro: —
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| APEX-Agents | 43% | — |
| BALROG | 45.6% | — |
| GDP.pdf | 22.7% | — |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.6 (#43), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| NYT Connections (extended) | 69.4% | 25.8% |
| LMArena Hard Prompts | 1451 | 1457 |
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 49.1% | — |
| ARC-AGI-1 | 88% | — |
| CritPt | 20.6% | — |
| Chess Puzzles | 40% | — |
| Thematic Generalization | — | 45.9% |
| Mystery Game Puzzles | 21% | — |
| DTBench | 89.1% | — |
| LMCA | 48.5% | — |
| Surface Evolver Bench | 61.9% | — |
| Epoch Capabilities Index | 156.39 | — |
Math GPT-5.6 Luna leads
GPT-5.6 Luna: 77.7 (#14), MiMo-V2-Pro: 39.5 (#102)
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1458 | 1447 |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 60% | — |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.5 (#34), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1478 | 1478 |
| GPQA Diamond | 91.6% | — |
| SimpleQA Verified | 41% | — |
Multimodal Not comparable
GPT-5.6 Luna: 42.7 (#28), MiMo-V2-Pro: —
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Vision | 1258 | — |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual Too close to call
GPT-5.6 Luna: 52.8 (#78), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1417 | 1416 |
| LMArena Chinese | 1470 | 1456 |
| LMArena French | 1456 | 1469 |
| LMArena German | 1454 | 1417 |
| LMArena Japanese | 1411 | 1366 |
| LMArena Korean | 1415 | 1400 |
| LMArena Russian | 1428 | 1427 |
| LMArena Spanish | 1448 | 1457 |
Instruction Following Too close to call
GPT-5.6 Luna: 75.6 (#57), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1437 | 1445 |
Long Context GPT-5.6 Luna leads
GPT-5.6 Luna: 43.9 (#82), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1436 | 1455 |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.0 (#29), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | GPT-5.6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1431 | 1436 |
| LMArena Creative Writing | 1396 | 1415 |
| LMArena Multi-Turn | 1434 | 1456 |
| EQ-Bench Creative Writing | 1829 | — |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than MiMo-V2-Pro?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 43.0 on the Noometry Index.
Which is cheaper, GPT-5.6 Luna or MiMo-V2-Pro?
GPT-5.6 Luna is cheaper. It lists at $0.20 per million input tokens and $1.20 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.
Is GPT-5.6 Luna or MiMo-V2-Pro better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 43.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 1.05M.
How many benchmarks do GPT-5.6 Luna and MiMo-V2-Pro share?
20 benchmarks have published results for both models. GPT-5.6 Luna has 52 scored results on Noometry and MiMo-V2-Pro has 23.