Model comparison
GPT-5 vs MiMo-V2-Omni
GPT-5 is the stronger model overall, scoring 50.9 to 43.6 on the Noometry Index. MiMo-V2-Omni costs 20× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
Summary
- They share 18 benchmarks with published results for both. GPT-5 scores higher in 7 categories and MiMo-V2-Omni in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 44.1.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-5 accepts more context: 400K tokens versus 262K.
Side by side
| GPT-5 | MiMo-V2-Omni | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 50.9 | 43.6 |
| Released | 2025-08-07 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 262K |
| Max output | 128K | 131K |
| Input $ / M tokens | $1.25 | $0.14 |
| Output $ / M tokens | $10 | $0.28 |
| Results tracked | 69 | 18 |
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Category by category
Coding GPT-5 leads
GPT-5: 50.3 (#47), MiMo-V2-Omni: 43.3 (#89)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1436 | 1466 |
| SWE-bench Verified | 73.6% | — |
| SWE-bench Verified (bash only) | 65% | — |
| Aider Polyglot | 88% | — |
| LMArena WebDev | 1418 | — |
| SciCode | 42.9% | — |
| GSO | 6.9% | — |
| WeirdML | 60.7% | — |
| ALE-Bench | 1,162 | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use Not comparable
GPT-5: 33.1 (#56), MiMo-V2-Omni: —
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| Terminal-Bench | 49.6% | — |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |
Reasoning GPT-5 leads
GPT-5: 38.3 (#64), MiMo-V2-Omni: 29.7 (#88)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1416 | 1445 |
| ARC-AGI-2 | 9.9% | — |
| SimpleBench | 56.7% | — |
| Kagi LLM Benchmark | 72.7% | — |
| ARC-AGI-1 | 65.7% | — |
| CritPt | 12.6% | — |
| Chess Puzzles | 37% | — |
| EnigmaEval | 10.5% | — |
| EBR-Bench | 12.7% | — |
| Mystery Game Puzzles | 23% | — |
| DTBench | 90.7% | — |
| LMCA | 40% | — |
| Epoch Capabilities Index | 150 | — |
| ForecastBench | 61.4 | — |
Math GPT-5 leads
GPT-5: 55.0 (#44), MiMo-V2-Omni: 39.1 (#115)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1407 | 1430 |
| FrontierMath (Tiers 1-3) | 55.4% | — |
| FrontierMath Tier 4 | 22% | — |
| OTIS Mock AIME 2024-2025 | 91.4% | — |
| ProofBench | 18% | — |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5 leads
GPT-5: 56.6 (#43), MiMo-V2-Omni: 40.5 (#118)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1419 | 1449 |
| GPQA Diamond | 86.2% | — |
| Humanity's Last Exam | 25.3% | — |
| SimpleQA Verified | 50.1% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| Vectara Hallucination Rate | 14.7% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal GPT-5 leads
GPT-5: 46.8 (#13), MiMo-V2-Omni: 38.6 (#63)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | 1232 | 1228 |
| GeoBench | 81% | — |
| VPCT | 66% | — |
Multilingual Too close to call
GPT-5: 51.4 (#110), MiMo-V2-Omni: 51.8 (#102)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1397 | 1404 |
| LMArena Chinese | 1422 | 1465 |
| LMArena French | 1410 | 1447 |
| LMArena German | 1416 | 1399 |
| LMArena Japanese | 1409 | 1317 |
| LMArena Korean | 1360 | 1355 |
| LMArena Russian | 1406 | 1412 |
| LMArena Spanish | 1399 | 1434 |
Instruction Following MiMo-V2-Omni leads
GPT-5: 73.8 (#113), MiMo-V2-Omni: 75.2 (#66)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1388 | 1428 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), MiMo-V2-Omni: 44.1 (#76)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1399 | 1442 |
| Fiction.LiveBench | 97.2% | — |
Writing & Preference GPT-5 leads
GPT-5: 63.4 (#65), MiMo-V2-Omni: 61.4 (#87)
| Benchmark | GPT-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1406 | 1423 |
| LMArena Creative Writing | 1365 | 1392 |
| LMArena Multi-Turn | 1426 | 1445 |
| Short-Story Creative Writing | 86% | — |
| EQ-Bench Creative Writing | 1627 | — |
| WildBench | 85.7% | — |
Frequently asked questions
Is GPT-5 better than MiMo-V2-Omni?
GPT-5 is the stronger model overall, scoring 50.9 to 43.6 on the Noometry Index. MiMo-V2-Omni costs 20× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GPT-5 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-5 lists at $1.25 and $10.
Is GPT-5 or MiMo-V2-Omni better for coding?
GPT-5 scores higher on coding benchmarks: 50.3 versus 43.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5 does, with 400K tokens against 262K.
How many benchmarks do GPT-5 and MiMo-V2-Omni share?
18 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and MiMo-V2-Omni has 18.