Model comparison
GPT-5.6 Luna vs MiMo-V2.5
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 43.4 on the Noometry Index. MiMo-V2.5 costs 2.6× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Summary
- They share 23 benchmarks with published results for both. GPT-5.6 Luna scores higher in 8 categories and MiMo-V2.5 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 36.8.
- The biggest single-benchmark swing is ProofBench: 60% for GPT-5.6 Luna and 16% for MiMo-V2.5.
- MiMo-V2.5 is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 1.05M.
- MiMo-V2.5 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Luna | MiMo-V2.5 | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 54.6 | 43.4 |
| Released | 2026-07-09 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.20 | $0.14 |
| Output $ / M tokens | $1.20 | $0.28 |
| 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-V2.5: 43.9 (#81)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| LMArena WebDev | 1519 | 1438 |
| SciCode | 53.6% | 43.1% |
| LMArena Coding | 1466 | 1469 |
| ALE-Bench | 1,667 | 513.95 |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| WeirdML | 60.9% | — |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 34.4 (#45), MiMo-V2.5: —
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| 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-V2.5: 28.6 (#101)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| CritPt | 20.6% | 3.7% |
| LMArena Hard Prompts | 1451 | 1450 |
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 49.1% | — |
| NYT Connections (extended) | 69.4% | — |
| ARC-AGI-1 | 88% | — |
| Chess Puzzles | 40% | — |
| 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-V2.5: 36.8 (#163)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| ProofBench | 60% | 16% |
| LMArena Math | 1458 | 1436 |
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
Knowledge GPT-5.6 Luna leads
GPT-5.6 Luna: 58.5 (#34), MiMo-V2.5: 40.8 (#115)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| LMArena Expert | 1478 | 1460 |
| GPQA Diamond | 91.6% | — |
| SimpleQA Verified | 41% | — |
Multimodal GPT-5.6 Luna leads
GPT-5.6 Luna: 42.7 (#28), MiMo-V2.5: 39.8 (#54)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| LMArena Vision | 1258 | 1247 |
| 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-V2.5: 51.9 (#99)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| LMArena Non-English | 1417 | 1404 |
| LMArena Chinese | 1470 | 1468 |
| LMArena French | 1456 | 1447 |
| LMArena German | 1454 | 1421 |
| LMArena Japanese | 1411 | 1306 |
| LMArena Korean | 1415 | 1363 |
| LMArena Russian | 1428 | 1395 |
| LMArena Spanish | 1448 | 1416 |
Instruction Following Too close to call
GPT-5.6 Luna: 75.6 (#57), MiMo-V2.5: 75.5 (#60)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| LMArena Instruction Following | 1437 | 1434 |
Long Context Too close to call
GPT-5.6 Luna: 43.9 (#82), MiMo-V2.5: 44.2 (#73)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| LMArena Longer Query | 1436 | 1445 |
Writing & Preference GPT-5.6 Luna leads
GPT-5.6 Luna: 68.0 (#29), MiMo-V2.5: 61.6 (#86)
| Benchmark | GPT-5.6 Luna | MiMo-V2.5 |
|---|---|---|
| LMArena Text | 1431 | 1428 |
| LMArena Creative Writing | 1396 | 1393 |
| LMArena Multi-Turn | 1434 | 1445 |
| EQ-Bench Creative Writing | 1829 | — |
| EQ-Bench 4 | 1156 | — |
Frequently asked questions
Is GPT-5.6 Luna better than MiMo-V2.5?
GPT-5.6 Luna is the stronger model overall, scoring 54.6 to 43.4 on the Noometry Index. MiMo-V2.5 costs 2.6× less per token, which makes it the better buy when GPT-5.6 Luna's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Luna or MiMo-V2.5?
MiMo-V2.5 is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is GPT-5.6 Luna or MiMo-V2.5 better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 54.5 versus 43.9 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-V2.5 share?
23 benchmarks have published results for both models. GPT-5.6 Luna has 52 scored results on Noometry and MiMo-V2.5 has 23.