Model comparison
GPT-5.6 Sol vs MiMo-V2-Pro
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 42.4 on the Noometry Index. MiMo-V2-Pro costs 15× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GPT-5.6 Sol scores higher in 8 categories and MiMo-V2-Pro in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.7 to 21.3.
- The biggest single-benchmark swing is NYT Connections (extended): 93.8% for GPT-5.6 Sol and 25.8% for MiMo-V2-Pro.
- MiMo-V2-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 1.05M.
Side by side
| GPT-5.6 Sol | MiMo-V2-Pro | |
|---|---|---|
| Provider | OpenAI | Xiaomi |
| Noometry Index | 64.4 | 42.4 |
| Released | 2026-07-09 | 2026-03-18 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1.05M |
| Max output | 128K | 131K |
| Input $ / M tokens | $4 | $0.43 |
| Output $ / M tokens | $20 | $0.87 |
| Results tracked | 64 | 23 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 61.1 (#7), MiMo-V2-Pro: 43.4 (#76)
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| LMArena WebDev | 1619 | 1433 |
| LMArena Coding | 1493 | 1476 |
| ALE-Bench | 2,177 | 785.17 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| WeirdML | 89.4% | — |
| MirrorCode | 20% | — |
Agentic & Tool Use Not comparable
GPT-5.6 Sol: 50.2 (#7), MiMo-V2-Pro: —
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.7 (#6), MiMo-V2-Pro: 21.3 (#200)
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| NYT Connections (extended) | 93.8% | 25.8% |
| LMArena Hard Prompts | 1481 | 1457 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| Thematic Generalization | — | 45.9% |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| DTBench | 96% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 161.99 | — |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.5 (#7), MiMo-V2-Pro: 36.7 (#104)
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1472 | 1447 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 83% | — |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#16), MiMo-V2-Pro: 40.8 (#106)
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1517 | 1479 |
| GPQA Diamond | 93.5% | — |
| SimpleQA Verified | 69.7% | — |
| Vectara Hallucination Rate | 12.4% | — |
Multimodal Not comparable
GPT-5.6 Sol: 48.9 (#8), MiMo-V2-Pro: —
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.4 (#32), MiMo-V2-Pro: 52.9 (#77)
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1450 | 1416 |
| LMArena Chinese | 1523 | 1455 |
| LMArena French | 1475 | 1470 |
| LMArena German | 1476 | 1417 |
| LMArena Japanese | 1475 | 1366 |
| LMArena Korean | 1434 | 1401 |
| LMArena Russian | 1466 | 1427 |
| LMArena Spanish | 1449 | 1458 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.8 (#16), MiMo-V2-Pro: 76.2 (#46)
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1480 | 1444 |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.3 (#42), MiMo-V2-Pro: 41.5 (#133)
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1479 | 1455 |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.5 (#12), MiMo-V2-Pro: 62.9 (#67)
| Benchmark | GPT-5.6 Sol | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1456 | 1436 |
| LMArena Creative Writing | 1450 | 1415 |
| LMArena Multi-Turn | 1456 | 1456 |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than MiMo-V2-Pro?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 42.4 on the Noometry Index. MiMo-V2-Pro costs 15× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Sol 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-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or MiMo-V2-Pro better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 43.4 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 1.05M.
How many benchmarks do GPT-5.6 Sol and MiMo-V2-Pro share?
20 benchmarks have published results for both models. GPT-5.6 Sol has 64 scored results on Noometry and MiMo-V2-Pro has 23.