Model comparison
GPT-5.2 vs MiMo-V2-Pro
GPT-5.2 is the stronger model overall, scoring 54.1 to 43.0 on the Noometry Index. MiMo-V2-Pro costs 8.9× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GPT-5.2 scores higher in 7 categories and MiMo-V2-Pro in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.2 leads 50.2 to 22.1.
- The biggest single-benchmark swing is NYT Connections (extended): 83.6% for GPT-5.2 and 25.8% for MiMo-V2-Pro.
- MiMo-V2-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 400K.
Side by side
| GPT-5.2 | MiMo-V2-Pro | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 54.1 | 43.0 |
| Released | 2025-12-11 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $1.75 | $0.43 |
| Output $ / M tokens | $14 | $0.87 |
| Results tracked | 67 | 23 |
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Category by category
Coding GPT-5.2 leads
GPT-5.2: 51.6 (#37), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena WebDev | 1416 | 1433 |
| LMArena Coding | 1447 | 1476 |
| ALE-Bench | 1,294 | 785.17 |
| SWE-bench Verified | 73.8% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 66.7% | — |
| GSO | 27.4% | — |
| WeirdML | 72.2% | — |
| AlgoTune | 2.05 | — |
Agentic & Tool Use Not comparable
GPT-5.2: 40.2 (#24), MiMo-V2-Pro: —
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| Terminal-Bench | 64.9% | — |
| Berkeley Function Calling Leaderboard | 55.9% | — |
| GDPval | 49.7% | — |
| Remote Labor Index | 2.5% | — |
| τ²-bench Airline | 83% | — |
| τ²-bench Banking | 32.2% | — |
| τ²-bench Retail | 81.6% | — |
| τ²-bench Telecom | 89.7% | — |
| DeepResearch Bench | 41.1% | — |
| LMArena Search | 1207 | — |
| METR Time Horizons | 75.3% | — |
| Vending-Bench 2 | 3,591 | — |
Reasoning GPT-5.2 leads
GPT-5.2: 50.2 (#35), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| NYT Connections (extended) | 83.6% | 25.8% |
| LMArena Hard Prompts | 1445 | 1457 |
| ARC-AGI-2 | 52.9% | — |
| SimpleBench | 45.8% | — |
| Kagi LLM Benchmark | 73.3% | — |
| ARC-AGI-1 | 86.2% | — |
| Chess Puzzles | 49% | — |
| EnigmaEval | 10.4% | — |
| Thematic Generalization | — | 45.9% |
| EBR-Bench | 23% | — |
| Mystery Game Puzzles | 23% | — |
| DTBench | 90.9% | — |
| LMCA | 43.9% | — |
| Epoch Capabilities Index | 153.45 | — |
| ForecastBench | 60.1 | — |
Math GPT-5.2 leads
GPT-5.2: 60.0 (#38), MiMo-V2-Pro: 39.5 (#102)
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1440 | 1447 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 72% | — |
| OTIS Mock AIME 2024-2025 | 96.1% | — |
| ProofBench | 15% | — |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 18.8% | — |
Knowledge GPT-5.2 leads
GPT-5.2: 59.3 (#32), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1445 | 1478 |
| GPQA Diamond | 91.4% | — |
| Humanity's Last Exam | 27.8% | — |
| SimpleQA Verified | 37.1% | — |
| Vectara Hallucination Rate | 8.4% | — |
Multimodal Not comparable
GPT-5.2: 51.3 (#7), MiMo-V2-Pro: —
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Vision | 1268 | — |
| VPCT | 84% | — |
| Furniture Assembly | 38.3% | — |
| LMArena Document | 1405 | — |
Multilingual Too close to call
GPT-5.2: 53.4 (#67), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1425 | 1416 |
| LMArena Chinese | 1460 | 1456 |
| LMArena French | 1455 | 1469 |
| LMArena German | 1448 | 1417 |
| LMArena Japanese | 1420 | 1366 |
| LMArena Korean | 1392 | 1400 |
| LMArena Russian | 1440 | 1427 |
| LMArena Spanish | 1433 | 1457 |
Instruction Following MiMo-V2-Pro leads
GPT-5.2: 74.7 (#89), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1417 | 1445 |
Long Context GPT-5.2 leads
GPT-5.2: 44.0 (#78), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| CL-bench | 18.2% | 15.7% |
| LMArena Longer Query | 1428 | 1455 |
| CL-bench Life | — | 6.9% |
Writing & Preference GPT-5.2 leads
GPT-5.2: 66.8 (#32), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | GPT-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1439 | 1436 |
| LMArena Creative Writing | 1401 | 1415 |
| LMArena Multi-Turn | 1458 | 1456 |
| EQ-Bench Creative Writing | 1703 | — |
Frequently asked questions
Is GPT-5.2 better than MiMo-V2-Pro?
GPT-5.2 is the stronger model overall, scoring 54.1 to 43.0 on the Noometry Index. MiMo-V2-Pro costs 8.9× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 or MiMo-V2-Pro?
MiMo-V2-Pro is cheaper. It lists at $0.43 per million input tokens and $0.87 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GPT-5.2 or MiMo-V2-Pro better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 43.8 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 400K.
How many benchmarks do GPT-5.2 and MiMo-V2-Pro share?
21 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and MiMo-V2-Pro has 23.