Model comparison
GPT-5.2 vs MiMo-V2.6-Flash
GPT-5.2 is the stronger model overall, scoring 54.1 to 48.5 on the Noometry Index. MiMo-V2.6-Flash costs 27× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. GPT-5.2 scores higher in 5 categories and MiMo-V2.6-Flash in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.2 leads 59.3 to 42.2.
- The biggest single-benchmark swing is ProofBench: 15% for GPT-5.2 and 63% for MiMo-V2.6-Flash.
- MiMo-V2.6-Flash is cheaper at $0.14 / $0.28 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- MiMo-V2.6-Flash accepts more context: 1.05M tokens versus 400K.
- MiMo-V2.6-Flash has downloadable open weights; the other is API-only.
Side by side
| GPT-5.2 | MiMo-V2.6-Flash | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 54.1 | 48.5 |
| Released | 2025-12-11 | 2026-09-21 |
| Weights | Proprietary | Open |
| Context window | 400K | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $1.75 | $0.14 |
| Output $ / M tokens | $14 | $0.28 |
| Results tracked | 67 | 19 |
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Category by category
Coding MiMo-V2.6-Flash leads
GPT-5.2: 51.6 (#37), MiMo-V2.6-Flash: 53.4 (#30)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena WebDev | 1416 | 1637 |
| LMArena Coding | 1447 | 1504 |
| SWE-bench Verified | 73.8% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 66.7% | — |
| SciCode | — | 51.3% |
| GSO | 27.4% | — |
| WeirdML | 72.2% | — |
| ALE-Bench | 1,294 | — |
| AlgoTune | 2.05 | — |
Agentic & Tool Use Not comparable
GPT-5.2: 40.2 (#24), MiMo-V2.6-Flash: —
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| 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-V2.6-Flash: 36.5 (#66)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Hard Prompts | 1445 | 1482 |
| ARC-AGI-2 | 52.9% | — |
| SimpleBench | 45.8% | — |
| Kagi LLM Benchmark | 73.3% | — |
| NYT Connections (extended) | 83.6% | — |
| ARC-AGI-1 | 86.2% | — |
| CritPt | — | 12% |
| Chess Puzzles | 49% | — |
| EnigmaEval | 10.4% | — |
| 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-V2.6-Flash: 51.9 (#52)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| ProofBench | 15% | 63% |
| LMArena Math | 1440 | 1468 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 72% | — |
| OTIS Mock AIME 2024-2025 | 96.1% | — |
| FrontierMath (Feb 2025 set) | 40.7% | — |
| FrontierMath Tier 4 (v1) | 18.8% | — |
Knowledge GPT-5.2 leads
GPT-5.2: 59.3 (#32), MiMo-V2.6-Flash: 42.2 (#99)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Expert | 1445 | 1501 |
| GPQA Diamond | 91.4% | — |
| Humanity's Last Exam | 27.8% | — |
| SimpleQA Verified | 37.1% | — |
| Vectara Hallucination Rate | 8.4% | — |
Multimodal GPT-5.2 leads
GPT-5.2: 51.3 (#7), MiMo-V2.6-Flash: 40.5 (#47)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Vision | 1268 | 1259 |
| VPCT | 84% | — |
| Furniture Assembly | 38.3% | — |
| LMArena Document | 1405 | — |
Multilingual Too close to call
GPT-5.2: 53.4 (#67), MiMo-V2.6-Flash: 54.0 (#51)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Non-English | 1425 | 1434 |
| LMArena Chinese | 1460 | 1511 |
| LMArena French | 1455 | 1475 |
| LMArena Russian | 1440 | 1409 |
| LMArena Spanish | 1433 | 1456 |
| LMArena German | 1448 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1392 | — |
Instruction Following MiMo-V2.6-Flash leads
GPT-5.2: 74.7 (#89), MiMo-V2.6-Flash: 76.8 (#35)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Instruction Following | 1417 | 1463 |
Long Context Too close to call
GPT-5.2: 44.0 (#78), MiMo-V2.6-Flash: 44.8 (#57)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Longer Query | 1428 | 1463 |
| CL-bench | 18.2% | — |
Writing & Preference GPT-5.2 leads
GPT-5.2: 66.8 (#32), MiMo-V2.6-Flash: 63.1 (#67)
| Benchmark | GPT-5.2 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Text | 1439 | 1455 |
| LMArena Creative Writing | 1401 | 1400 |
| LMArena Multi-Turn | 1458 | 1451 |
| EQ-Bench Creative Writing | 1703 | — |
Frequently asked questions
Is GPT-5.2 better than MiMo-V2.6-Flash?
GPT-5.2 is the stronger model overall, scoring 54.1 to 48.5 on the Noometry Index. MiMo-V2.6-Flash costs 27× 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-V2.6-Flash?
MiMo-V2.6-Flash is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GPT-5.2 or MiMo-V2.6-Flash better for coding?
MiMo-V2.6-Flash scores higher on coding benchmarks: 53.4 versus 51.6 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.6-Flash does, with 1.05M tokens against 400K.
How many benchmarks do GPT-5.2 and MiMo-V2.6-Flash share?
17 benchmarks have published results for both models. GPT-5.2 has 67 scored results on Noometry and MiMo-V2.6-Flash has 19.