Model comparison
GPT-6 Luna vs MiMo-V2-Pro
GPT-6 Luna is the stronger model overall, scoring 53.3 to 43.0 on the Noometry Index.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GPT-6 Luna scores higher in 5 categories and MiMo-V2-Pro in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Luna leads 76.1 to 39.5.
- The biggest single-benchmark swing is NYT Connections (extended): 68.7% for GPT-6 Luna and 25.8% for MiMo-V2-Pro.
- GPT-6 Luna is cheaper at $0.10 / $0.50 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- GPT-6 Luna accepts more context: 1.05M tokens versus 1.05M.
Side by side
| GPT-6 Luna | MiMo-V2-Pro | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 53.3 | 43.0 |
| Released | 2026-09-22 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.10 | $0.43 |
| Output $ / M tokens | $0.50 | $0.87 |
| Results tracked | 42 | 23 |
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Category by category
Coding GPT-6 Luna leads
GPT-6 Luna: 55.5 (#25), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena WebDev | 1581 | 1433 |
| LMArena Coding | 1439 | 1476 |
| ALE-Bench | 1,577 | 785.17 |
| DeepSWE | 66.6% | — |
| FrontierCode | 42.4% | — |
| SciCode | 54.6% | — |
Agentic & Tool Use Not comparable
GPT-6 Luna: 33.3 (#54), MiMo-V2-Pro: —
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| APEX-Agents | 44.3% | — |
| GDP.pdf | 23% | — |
Reasoning GPT-6 Luna leads
GPT-6 Luna: 48.2 (#41), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| NYT Connections (extended) | 68.7% | 25.8% |
| LMArena Hard Prompts | 1411 | 1457 |
| ARC-AGI-2 | 59.3% | — |
| ARC-AGI-1 | 86.7% | — |
| CritPt | 19.4% | — |
| Chess Puzzles | 31% | — |
| Thematic Generalization | — | 45.9% |
| Mystery Game Puzzles | 7% | — |
| DTBench | 90.1% | — |
| LMCA | 44.5% | — |
| Epoch Capabilities Index | 156.28 | — |
Math GPT-6 Luna leads
GPT-6 Luna: 76.1 (#15), MiMo-V2-Pro: 39.5 (#102)
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1416 | 1447 |
| FrontierMath (Tiers 1-3) | 78.9% | — |
| FrontierMath Tier 4 | 56.1% | — |
| OTIS Mock AIME 2024-2025 | 98.9% | — |
| ProofBench | 64% | — |
Knowledge GPT-6 Luna leads
GPT-6 Luna: 57.0 (#41), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1444 | 1478 |
| GPQA Diamond | 90.5% | — |
| SimpleQA Verified | 41.4% | — |
Multimodal Not comparable
GPT-6 Luna: 42.4 (#30), MiMo-V2-Pro: —
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Vision | 1217 | — |
| Blueprint-Bench 2 | 31.2% | — |
| Furniture Assembly | 44.2% | — |
Multilingual MiMo-V2-Pro leads
GPT-6 Luna: 50.5 (#117), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1386 | 1416 |
| LMArena Chinese | 1433 | 1456 |
| LMArena French | 1420 | 1469 |
| LMArena German | 1369 | 1417 |
| LMArena Japanese | 1369 | 1366 |
| LMArena Korean | 1360 | 1400 |
| LMArena Russian | 1394 | 1427 |
| LMArena Spanish | 1393 | 1457 |
Instruction Following MiMo-V2-Pro leads
GPT-6 Luna: 74.3 (#99), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1409 | 1445 |
Long Context GPT-6 Luna leads
GPT-6 Luna: 43.0 (#111), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1409 | 1455 |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference MiMo-V2-Pro leads
GPT-6 Luna: 58.3 (#119), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | GPT-6 Luna | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1391 | 1436 |
| LMArena Creative Writing | 1363 | 1415 |
| LMArena Multi-Turn | 1396 | 1456 |
Frequently asked questions
Is GPT-6 Luna better than MiMo-V2-Pro?
GPT-6 Luna is the stronger model overall, scoring 53.3 to 43.0 on the Noometry Index.
Which is cheaper, GPT-6 Luna or MiMo-V2-Pro?
GPT-6 Luna is cheaper. It lists at $0.10 per million input tokens and $0.50 per million output tokens; MiMo-V2-Pro lists at $0.43 and $0.87.
Is GPT-6 Luna or MiMo-V2-Pro better for coding?
GPT-6 Luna scores higher on coding benchmarks: 55.5 versus 43.8 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Luna does, with 1.05M tokens against 1.05M.
How many benchmarks do GPT-6 Luna and MiMo-V2-Pro share?
20 benchmarks have published results for both models. GPT-6 Luna has 42 scored results on Noometry and MiMo-V2-Pro has 23.