Model comparison
GPT-5 Nano vs MiMo-V2-Omni
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 33.5 on the Noometry Index.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. GPT-5 Nano scores higher in 0 categories and MiMo-V2-Omni in 9 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiMo-V2-Omni leads 61.4 to 39.1.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.14 / $0.28 for MiMo-V2-Omni.
- GPT-5 Nano accepts more context: 400K tokens versus 262K.
Side by side
| GPT-5 Nano | MiMo-V2-Omni | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 33.5 | 43.6 |
| Released | 2025-08-07 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 262K |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.05 | $0.14 |
| Output $ / M tokens | $0.40 | $0.28 |
| Results tracked | 49 | 18 |
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Category by category
Coding MiMo-V2-Omni leads
GPT-5 Nano: 33.6 (#254), MiMo-V2-Omni: 43.3 (#89)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1351 | 1466 |
| SWE-bench Verified (bash only) | 34.8% | — |
| WeirdML | 38.1% | — |
| ALE-Bench | 718.67 | — |
Agentic & Tool Use Not comparable
GPT-5 Nano: 25.8 (#106), MiMo-V2-Omni: —
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| Berkeley Function Calling Leaderboard | 51.5% | — |
Reasoning MiMo-V2-Omni leads
GPT-5 Nano: 16.3 (#306), MiMo-V2-Omni: 29.7 (#88)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1328 | 1445 |
| ARC-AGI-2 | 2.6% | — |
| Kagi LLM Benchmark | 62.2% | — |
| ARC-AGI-1 | 20.7% | — |
| Chess Puzzles | 27% | — |
| Mystery Game Puzzles | 9% | — |
| DTBench | 62.7% | — |
| LMCA | 7.9% | — |
| Epoch Capabilities Index | 139.38 | — |
| ForecastBench | 59.1 | — |
Math MiMo-V2-Omni leads
GPT-5 Nano: 29.4 (#241), MiMo-V2-Omni: 39.1 (#115)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1317 | 1430 |
| FrontierMath (Tiers 1-3) | 20% | — |
| FrontierMath Tier 4 | 2.4% | — |
| OTIS Mock AIME 2024-2025 | 81.1% | — |
| ProofBench | 12% | — |
| Omni-MATH | 54.6% | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge MiMo-V2-Omni leads
GPT-5 Nano: 35.9 (#178), MiMo-V2-Omni: 40.5 (#118)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1321 | 1449 |
| GPQA Diamond | 69.4% | — |
| SimpleQA Verified | 11.7% | — |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
Multimodal MiMo-V2-Omni leads
GPT-5 Nano: 31.3 (#108), MiMo-V2-Omni: 38.6 (#63)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | 1159 | 1228 |
| VPCT | 37.2% | — |
Multilingual MiMo-V2-Omni leads
GPT-5 Nano: 45.3 (#172), MiMo-V2-Omni: 51.8 (#102)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1313 | 1404 |
| LMArena Chinese | 1356 | 1465 |
| LMArena German | 1327 | 1399 |
| LMArena Japanese | 1226 | 1317 |
| LMArena Korean | 1269 | 1355 |
| LMArena Russian | 1296 | 1412 |
| LMArena Spanish | 1360 | 1434 |
| LMArena French | — | 1447 |
Instruction Following Too close to call
GPT-5 Nano: 75.0 (#79), MiMo-V2-Omni: 75.2 (#66)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1306 | 1428 |
| IFEval | 93.2% | — |
Long Context MiMo-V2-Omni leads
GPT-5 Nano: 31.3 (#281), MiMo-V2-Omni: 44.1 (#76)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1312 | 1442 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference MiMo-V2-Omni leads
GPT-5 Nano: 39.1 (#249), MiMo-V2-Omni: 61.4 (#87)
| Benchmark | GPT-5 Nano | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1320 | 1423 |
| LMArena Creative Writing | 1249 | 1392 |
| LMArena Multi-Turn | 1311 | 1445 |
| EQ-Bench Creative Writing | 705 | — |
| WildBench | 80.6% | — |
Frequently asked questions
Is GPT-5 Nano better than MiMo-V2-Omni?
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 33.5 on the Noometry Index.
Which is cheaper, GPT-5 Nano or MiMo-V2-Omni?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; MiMo-V2-Omni lists at $0.14 and $0.28.
Is GPT-5 Nano or MiMo-V2-Omni better for coding?
MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 33.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 262K.
How many benchmarks do GPT-5 Nano and MiMo-V2-Omni share?
17 benchmarks have published results for both models. GPT-5 Nano has 49 scored results on Noometry and MiMo-V2-Omni has 18.