Model comparison
GPT-4o mini vs MiMo-V2.5-Pro
MiMo-V2.5-Pro is the stronger model overall, scoring 45.2 to 25.5 on the Noometry Index. GPT-4o mini costs 2.1× less per token, which makes it the better buy when MiMo-V2.5-Pro's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GPT-4o mini scores higher in 0 categories and MiMo-V2.5-Pro in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where MiMo-V2.5-Pro leads 40.0 to 10.4.
- The biggest single-benchmark swing is DTBench: 54.4% for GPT-4o mini and 84.5% for MiMo-V2.5-Pro.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2.5-Pro.
- MiMo-V2.5-Pro accepts more context: 1.05M tokens versus 128K.
- MiMo-V2.5-Pro has downloadable open weights; the other is API-only.
Side by side
| GPT-4o mini | MiMo-V2.5-Pro | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 25.5 | 45.2 |
| Released | 2024-07-18 | 2026-04-22 |
| Weights | Proprietary | Open |
| Context window | 128K | 1.05M |
| Max output | 16K | 131K |
| Input $ / M tokens | $0.15 | $0.43 |
| Output $ / M tokens | $0.60 | $0.87 |
| Results tracked | 60 | 27 |
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Category by category
Coding MiMo-V2.5-Pro leads
GPT-4o mini: 22.0 (#335), MiMo-V2.5-Pro: 47.4 (#60)
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Coding | 1290 | 1503 |
| Aider Polyglot | 3.6% | — |
| LMArena WebDev | — | 1479 |
| SciCode | — | 50.2% |
| WeirdML | 11.8% | — |
| BigCodeBench Instruct | 46.1% | — |
| LiveBench Coding | 43.1% | — |
| BigCodeBench Complete | 57.4% | — |
| ALE-Bench | — | 899.8 |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use Not comparable
GPT-4o mini: 27.5 (#101), MiMo-V2.5-Pro: —
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| BALROG | 17.4% | — |
Reasoning MiMo-V2.5-Pro leads
GPT-4o mini: 8.7 (#347), MiMo-V2.5-Pro: 26.8 (#130)
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Hard Prompts | 1267 | 1488 |
| DTBench | 54.4% | 84.5% |
| LMCA | 10.4% | 29.5% |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| Kagi LLM Benchmark | 28.8% | — |
| NYT Connections (extended) | — | 34.4% |
| CritPt | — | 4% |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | 32.8% | — |
| Mystery Game Puzzles | 12% | — |
| LiveBench Data Analysis | 50% | — |
| Epoch Capabilities Index | 126.56 | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math MiMo-V2.5-Pro leads
GPT-4o mini: 10.4 (#314), MiMo-V2.5-Pro: 40.0 (#96)
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Math | 1267 | 1481 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| OTIS Mock AIME 2024-2025 | 6.9% | — |
| ProofBench | — | 22% |
| Omni-MATH | 28% | — |
| LiveBench Math | 36.3% | — |
| MATH Level 5 | 52.6% | — |
| GSM8K | 91.3% | — |
Knowledge MiMo-V2.5-Pro leads
GPT-4o mini: 17.7 (#284), MiMo-V2.5-Pro: 42.2 (#98)
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Expert | 1235 | 1503 |
| GPQA Diamond | 37.7% | — |
| SimpleQA Verified | 8.3% | — |
| MMLU-Pro | 60.3% | — |
| Confabulations | 37.2% | — |
| GPQA (HELM) | 36.8% | — |
| BoolQ | 88.7% | — |
| MMLU | 81.8% | — |
Multimodal Not comparable
GPT-4o mini: 25.9 (#122), MiMo-V2.5-Pro: —
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual MiMo-V2.5-Pro leads
GPT-4o mini: 42.0 (#199), MiMo-V2.5-Pro: 55.1 (#34)
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Non-English | 1266 | 1449 |
| LMArena Chinese | 1265 | 1507 |
| LMArena French | 1297 | 1488 |
| LMArena German | 1272 | 1458 |
| LMArena Japanese | 1216 | 1412 |
| LMArena Korean | 1195 | 1437 |
| LMArena Russian | 1275 | 1450 |
| LMArena Spanish | 1276 | 1471 |
Instruction Following MiMo-V2.5-Pro leads
GPT-4o mini: 61.9 (#239), MiMo-V2.5-Pro: 77.5 (#21)
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Instruction Following | 1258 | 1477 |
| LiveBench Instruction Following | 56.8% | — |
| IFEval | 78.2% | — |
Long Context MiMo-V2.5-Pro leads
GPT-4o mini: 39.1 (#186), MiMo-V2.5-Pro: 45.4 (#37)
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Longer Query | 1289 | 1483 |
Writing & Preference MiMo-V2.5-Pro leads
GPT-4o mini: 39.5 (#248), MiMo-V2.5-Pro: 65.3 (#49)
| Benchmark | GPT-4o mini | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Text | 1286 | 1465 |
| LMArena Creative Writing | 1268 | 1440 |
| EQ-Bench Creative Writing | 873 | 1493 |
| LMArena Multi-Turn | 1285 | 1477 |
| Short-Story Creative Writing | 67.2% | — |
| WildBench | 79.1% | — |
| EQ-Bench 4 | — | 1208 |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than MiMo-V2.5-Pro?
MiMo-V2.5-Pro is the stronger model overall, scoring 45.2 to 25.5 on the Noometry Index. GPT-4o mini costs 2.1× less per token, which makes it the better buy when MiMo-V2.5-Pro's lead doesn't matter for your workload.
Which is cheaper, GPT-4o mini or MiMo-V2.5-Pro?
GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; MiMo-V2.5-Pro lists at $0.43 and $0.87.
Is GPT-4o mini or MiMo-V2.5-Pro better for coding?
MiMo-V2.5-Pro scores higher on coding benchmarks: 47.4 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.5-Pro does, with 1.05M tokens against 128K.
How many benchmarks do GPT-4o mini and MiMo-V2.5-Pro share?
20 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and MiMo-V2.5-Pro has 27.