Model comparison
GPT-3.5-turbo vs MiMo-V2-Omni
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 23.2 on the Noometry Index.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. GPT-3.5-turbo scores higher in 0 categories and MiMo-V2-Omni in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where MiMo-V2-Omni leads 61.4 to 25.3.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.50 / $1.50 for GPT-3.5-turbo.
- MiMo-V2-Omni accepts more context: 262K tokens versus 16K.
Side by side
| GPT-3.5-turbo | MiMo-V2-Omni | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 23.2 | 43.6 |
| Released | 2023-03-01 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 16K | 262K |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.50 | $0.14 |
| Output $ / M tokens | $1.50 | $0.28 |
| Results tracked | 44 | 18 |
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Category by category
Coding MiMo-V2-Omni leads
GPT-3.5-turbo: 23.9 (#331), MiMo-V2-Omni: 43.3 (#89)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1136 | 1466 |
| WeirdML | 3.5% | — |
| BigCodeBench Instruct | 39.1% | — |
| BigCodeBench Complete | 50.6% | — |
| HumanEval+ | 70.7% | — |
| MBPP+ | 69.7% | — |
Agentic & Tool Use Not comparable
GPT-3.5-turbo: —, MiMo-V2-Omni: —
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| METR Time Horizons | 21.5% | — |
Reasoning MiMo-V2-Omni leads
GPT-3.5-turbo: 13.8 (#332), MiMo-V2-Omni: 29.7 (#88)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1108 | 1445 |
| Chess Puzzles | 0% | — |
| Mystery Game Puzzles | 3% | — |
| DTBench | 48.5% | — |
| LMCA | 9.7% | — |
| Adversarial NLI | 58.1% | — |
| BIG-Bench Hard | 61.6% | — |
| CommonsenseQA 2.0 | 57% | — |
| Epoch Capabilities Index | 118.55 | — |
| ForecastBench | 50.4 | — |
| WinoGrande | 81.6% | — |
Math MiMo-V2-Omni leads
GPT-3.5-turbo: 6.3 (#327), MiMo-V2-Omni: 39.1 (#115)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1142 | 1430 |
| FrontierMath (Tiers 1-3) | 0% | — |
| OTIS Mock AIME 2024-2025 | 2.2% | — |
| MATH Level 5 | 15.9% | — |
| GSM8K | 57.8% | — |
Knowledge MiMo-V2-Omni leads
GPT-3.5-turbo: 10.0 (#303), MiMo-V2-Omni: 40.5 (#118)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1070 | 1449 |
| GPQA Diamond | 28% | — |
| ARC (AI2) Challenge | 87.4% | — |
| BoolQ | 87% | — |
| MMLU | 71.4% | — |
| OpenBookQA | 86% | — |
| TriviaQA | 85.8% | — |
Multimodal Not comparable
GPT-3.5-turbo: —, MiMo-V2-Omni: 38.6 (#63)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
Multilingual MiMo-V2-Omni leads
GPT-3.5-turbo: 31.5 (#258), MiMo-V2-Omni: 51.8 (#102)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1108 | 1404 |
| LMArena Chinese | 1075 | 1465 |
| LMArena French | 1118 | 1447 |
| LMArena German | 1090 | 1399 |
| LMArena Japanese | 1043 | 1317 |
| LMArena Korean | 1019 | 1355 |
| LMArena Russian | 1123 | 1412 |
| LMArena Spanish | 1121 | 1434 |
Instruction Following MiMo-V2-Omni leads
GPT-3.5-turbo: 57.9 (#262), MiMo-V2-Omni: 75.2 (#66)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1119 | 1428 |
Long Context MiMo-V2-Omni leads
GPT-3.5-turbo: 34.0 (#254), MiMo-V2-Omni: 44.1 (#76)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1121 | 1442 |
Writing & Preference MiMo-V2-Omni leads
GPT-3.5-turbo: 25.3 (#305), MiMo-V2-Omni: 61.4 (#87)
| Benchmark | GPT-3.5-turbo | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1125 | 1423 |
| LMArena Creative Writing | 1092 | 1392 |
| LMArena Multi-Turn | 1117 | 1445 |
| EQ-Bench Creative Writing | 451 | — |
Frequently asked questions
Is GPT-3.5-turbo better than MiMo-V2-Omni?
MiMo-V2-Omni is the stronger model overall, scoring 43.6 to 23.2 on the Noometry Index.
Which is cheaper, GPT-3.5-turbo 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-3.5-turbo lists at $0.50 and $1.50.
Is GPT-3.5-turbo or MiMo-V2-Omni better for coding?
MiMo-V2-Omni scores higher on coding benchmarks: 43.3 versus 23.9 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Omni does, with 262K tokens against 16K.
How many benchmarks do GPT-3.5-turbo and MiMo-V2-Omni share?
17 benchmarks have published results for both models. GPT-3.5-turbo has 44 scored results on Noometry and MiMo-V2-Omni has 18.