Model comparison
GPT-5.6 Sol vs Kimi K2.6
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 47.7 on the Noometry Index. Kimi K2.6 costs 4.7× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 46 shared benchmarks.
Summary
- They share 46 benchmarks with published results for both. GPT-5.6 Sol scores higher in 10 categories and Kimi K2.6 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.8 to 40.5.
- The biggest single-benchmark swing is ProofBench: 83% for GPT-5.6 Sol and 16% for Kimi K2.6.
- Kimi K2.6 is cheaper at $0.95 / $4 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 262K.
- Kimi K2.6 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Sol | Kimi K2.6 | |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 65.0 | 47.7 |
| Released | 2026-07-09 | 2026-04-20 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 262K |
| Input $ / M tokens | $4 | $0.95 |
| Output $ / M tokens | $20 | $4 |
| Results tracked | 65 | 51 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), Kimi K2.6: 50.7 (#43)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| LMArena WebDev | 1618 | 1509 |
| SciCode | 57.1% | 53.5% |
| WeirdML | 89.4% | 55.9% |
| LMArena Coding | 1498 | 1488 |
| ALE-Bench | 2,177 | 1,093 |
| SWE-bench Verified | — | 76.7% |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| FrontierSWE | 32.2% | — |
| GSO | 76.5% | — |
| MirrorCode | 20% | — |
Agentic & Tool Use GPT-5.6 Sol leads
GPT-5.6 Sol: 50.3 (#7), Kimi K2.6: 21.9 (#137)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| OSWorld 2.0 | 27.3% | 4.6% |
| GBAEval | 52.6% | 0.9% |
| GDP.pdf | 30.7% | 12% |
| Vending-Bench 2 | 9,619 | 6,205 |
| APEX-Agents | 51.4% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| ExploitBench | — | 18.4% |
| LMArena Search | 1257 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.8 (#8), Kimi K2.6: 40.5 (#55)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| NYT Connections (extended) | 93.8% | 87.2% |
| CritPt | 32.3% | 8% |
| Chess Puzzles | 64% | 26% |
| EBR-Bench | 44.8% | 2.4% |
| LMArena Hard Prompts | 1484 | 1470 |
| Mystery Game Puzzles | 58% | 18% |
| DTBench | 96% | 90.9% |
| LMCA | 59.2% | 37.3% |
| Epoch Capabilities Index | 161.66 | 151.05 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| ARC-AGI-1 | 97.5% | — |
| EnigmaEval | 37.1% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), Kimi K2.6: 57.0 (#41)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 89.1% | 57.2% |
| FrontierMath Tier 4 | 82.9% | 25.6% |
| OTIS Mock AIME 2024-2025 | 100% | 96.1% |
| ProofBench | 83% | 16% |
| LMArena Math | 1474 | 1475 |
| MathArena Final-Answer Competitions | — | 72.9% |
| FrontierMath (Feb 2025 set) | — | 39% |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | — | 14.6% |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), Kimi K2.6: 54.0 (#54)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| GPQA Diamond | 93.5% | 90.8% |
| SimpleQA Verified | 69.7% | 34.9% |
| Vectara Hallucination Rate | 12.4% | 10.8% |
| LMArena Expert | 1516 | 1491 |
Multimodal GPT-5.6 Sol leads
GPT-5.6 Sol: 48.6 (#9), Kimi K2.6: 31.6 (#103)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| LMArena Vision | 1281 | 1283 |
| Blueprint-Bench 2 | 33.6% | 3.9% |
| Furniture Assembly | 56.7% | 21.7% |
| LMArena Document | 1483 | 1451 |
Multilingual Too close to call
GPT-5.6 Sol: 55.3 (#32), Kimi K2.6: 54.9 (#37)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| LMArena Non-English | 1452 | 1446 |
| LMArena Chinese | 1527 | 1521 |
| LMArena French | 1477 | 1471 |
| LMArena German | 1476 | 1450 |
| LMArena Japanese | 1471 | 1443 |
| LMArena Korean | 1442 | 1427 |
| LMArena Russian | 1468 | 1446 |
| LMArena Spanish | 1441 | 1464 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), Kimi K2.6: 76.3 (#43)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| LMArena Instruction Following | 1482 | 1451 |
Long Context Too close to call
GPT-5.6 Sol: 45.4 (#42), Kimi K2.6: 44.9 (#52)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| LMArena Longer Query | 1480 | 1468 |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), Kimi K2.6: 68.5 (#26)
| Benchmark | GPT-5.6 Sol | Kimi K2.6 |
|---|---|---|
| LMArena Text | 1457 | 1455 |
| LMArena Creative Writing | 1448 | 1434 |
| EQ-Bench Creative Writing | 1972 | 1725 |
| EQ-Bench 4 | 1250 | 1202 |
| LMArena Multi-Turn | 1460 | 1453 |
Frequently asked questions
Is GPT-5.6 Sol better than Kimi K2.6?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 47.7 on the Noometry Index. Kimi K2.6 costs 4.7× 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 Kimi K2.6?
Kimi K2.6 is cheaper. It lists at $0.95 per million input tokens and $4 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or Kimi K2.6 better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 50.7 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 262K.
How many benchmarks do GPT-5.6 Sol and Kimi K2.6 share?
46 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and Kimi K2.6 has 51.