Model comparison
GPT-4.1 vs MiMo-V2-Omni
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 35.9 on the Noometry Index.
Last verified . 18 shared benchmarks.
Summary
- They share 18 benchmarks with published results for both. GPT-4.1 scores higher in 0 categories and MiMo-V2-Omni in 9 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where MiMo-V2-Omni leads 29.7 to 11.7.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 accepts more context: 1.05M tokens versus 262K.
Side by side
| GPT-4.1 | MiMo-V2-Omni | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 35.9 | 43.6 |
| Released | 2025-04-14 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 262K |
| Max output | 33K | 131K |
| Input $ / M tokens | $2 | $0.14 |
| Output $ / M tokens | $8 | $0.28 |
| Results tracked | 52 | 18 |
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Category by category
Coding MiMo-V2-Omni leads
GPT-4.1: 34.4 (#238), MiMo-V2-Omni: 43.3 (#89)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1391 | 1466 |
| SWE-bench Verified | 48.5% | — |
| SWE-bench Verified (bash only) | 39.6% | — |
| Aider Polyglot | 52.4% | — |
| WeirdML | 39% | — |
| CadEval | 42% | — |
| ALE-Bench | 558.1 | — |
Agentic & Tool Use Not comparable
GPT-4.1: 34.7 (#43), MiMo-V2-Omni: —
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| Berkeley Function Calling Leaderboard | 54% | — |
Reasoning MiMo-V2-Omni leads
GPT-4.1: 11.7 (#339), MiMo-V2-Omni: 29.7 (#88)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1384 | 1445 |
| ARC-AGI-2 | 0.4% | — |
| SimpleBench | 27% | — |
| Kagi LLM Benchmark | 52.3% | — |
| ARC-AGI-1 | 5.5% | — |
| Chess Puzzles | 6% | — |
| EnigmaEval | 2.2% | — |
| DTBench | 68.3% | — |
| LMCA | 25.6% | — |
| Epoch Capabilities Index | 136.78 | — |
| ForecastBench | 61.5 | — |
Math MiMo-V2-Omni leads
GPT-4.1: 22.3 (#280), MiMo-V2-Omni: 39.1 (#115)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1370 | 1430 |
| FrontierMath (Tiers 1-3) | 6% | — |
| OTIS Mock AIME 2024-2025 | 38.3% | — |
| Omni-MATH | 47.1% | — |
| MATH Level 5 | 83% | — |
| FrontierMath (Feb 2025 set) | 5.5% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge MiMo-V2-Omni leads
GPT-4.1: 37.1 (#160), MiMo-V2-Omni: 40.5 (#118)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1364 | 1449 |
| GPQA Diamond | 66.9% | — |
| Humanity's Last Exam | 5.4% | — |
| SimpleQA Verified | 31.1% | — |
| MMLU-Pro | 81.1% | — |
| Vectara Hallucination Rate | 5.6% | — |
| GPQA (HELM) | 65.9% | — |
Multimodal Too close to call
GPT-4.1: 38.2 (#67), MiMo-V2-Omni: 38.6 (#63)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | 1211 | 1228 |
| GeoBench | 72% | — |
Multilingual MiMo-V2-Omni leads
GPT-4.1: 49.4 (#133), MiMo-V2-Omni: 51.8 (#102)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1370 | 1404 |
| LMArena Chinese | 1382 | 1465 |
| LMArena French | 1382 | 1447 |
| LMArena German | 1381 | 1399 |
| LMArena Japanese | 1319 | 1317 |
| LMArena Korean | 1339 | 1355 |
| LMArena Russian | 1377 | 1412 |
| LMArena Spanish | 1376 | 1434 |
Instruction Following MiMo-V2-Omni leads
GPT-4.1: 71.3 (#153), MiMo-V2-Omni: 75.2 (#66)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1367 | 1428 |
| IFEval | 83.8% | — |
Long Context MiMo-V2-Omni leads
GPT-4.1: 40.0 (#163), MiMo-V2-Omni: 44.1 (#76)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1385 | 1442 |
| Fiction.LiveBench | 63.9% | — |
Writing & Preference MiMo-V2-Omni leads
GPT-4.1: 57.6 (#125), MiMo-V2-Omni: 61.4 (#87)
| Benchmark | GPT-4.1 | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1383 | 1423 |
| LMArena Creative Writing | 1363 | 1392 |
| LMArena Multi-Turn | 1398 | 1445 |
| EQ-Bench Creative Writing | 1420 | — |
| WildBench | 85.4% | — |
Frequently asked questions
Is GPT-4.1 better than MiMo-V2-Omni?
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 35.9 on the Noometry Index.
Which is cheaper, GPT-4.1 or MiMo-V2-Omni?
MiMo-V2-Omni is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; GPT-4.1 lists at $2 and $8.
Is GPT-4.1 or MiMo-V2-Omni better for coding?
MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 262K.
How many benchmarks do GPT-4.1 and MiMo-V2-Omni share?
18 benchmarks have published results for both models. GPT-4.1 has 52 scored results on Noometry and MiMo-V2-Omni has 18.