Model comparison
GPT-5.5 vs Kimi K2.6
GPT-5.5 is the stronger model overall, scoring 63.4 to 47.7 on the Noometry Index. Kimi K2.6 costs 6.6× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Last verified . 51 shared benchmarks.
Summary
- They share 51 benchmarks with published results for both. GPT-5.5 scores higher in 10 categories and Kimi K2.6 in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.8 to 40.5.
- The biggest single-benchmark swing is GBAEval: 53.2% for GPT-5.5 and 0.9% for Kimi K2.6.
- Kimi K2.6 is cheaper at $0.95 / $4 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 262K.
- Kimi K2.6 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.5 | Kimi K2.6 | |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 63.4 | 47.7 |
| Released | 2026-04-23 | 2026-04-20 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 262K |
| Input $ / M tokens | $5 | $0.95 |
| Output $ / M tokens | $30 | $4 |
| Results tracked | 71 | 51 |
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Category by category
Coding GPT-5.5 leads
GPT-5.5: 58.2 (#17), Kimi K2.6: 50.7 (#43)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| SWE-bench Verified | 80.6% | 76.7% |
| LMArena WebDev | 1513 | 1509 |
| SciCode | 56.1% | 53.5% |
| WeirdML | 84.9% | 55.9% |
| LMArena Coding | 1494 | 1488 |
| ALE-Bench | 1,943 | 1,093 |
| DeepSWE | 67% | — |
| FrontierCode | 43% | — |
| GSO | 40.2% | — |
| MirrorCode | 10% | — |
Agentic & Tool Use GPT-5.5 leads
GPT-5.5: 50.7 (#6), Kimi K2.6: 21.9 (#137)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| OSWorld 2.0 | 13% | 4.6% |
| ExploitBench | 47.4% | 18.4% |
| GBAEval | 53.2% | 0.9% |
| GDP.pdf | 26% | 12% |
| Vending-Bench 2 | 7,524 | 6,205 |
| Terminal-Bench | 84.7% | — |
| APEX-Agents | 55.1% | — |
| Remote Labor Index | 6.3% | — |
| τ²-bench Banking | 44.6% | — |
| DeepResearch Bench | 54% | — |
| PostTrainBench | 27.2% | — |
| LMArena Search | 1242 | — |
Reasoning GPT-5.5 leads
GPT-5.5: 72.8 (#11), Kimi K2.6: 40.5 (#55)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| NYT Connections (extended) | 96.2% | 87.2% |
| CritPt | 27.1% | 8% |
| Chess Puzzles | 54% | 26% |
| EBR-Bench | 34.3% | 2.4% |
| LMArena Hard Prompts | 1489 | 1470 |
| Mystery Game Puzzles | 56% | 18% |
| DTBench | 96% | 90.9% |
| LMCA | 54.3% | 37.3% |
| Epoch Capabilities Index | 159.1 | 151.05 |
| ARC-AGI-2 | 85% | — |
| SimpleBench | 69% | — |
| Kagi LLM Benchmark | 88.8% | — |
| ARC-AGI-1 | 95% | — |
| Surface Evolver Bench | 88.1% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 60.6 | — |
Math GPT-5.5 leads
GPT-5.5: 81.7 (#11), Kimi K2.6: 57.0 (#41)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 85.3% | 57.2% |
| FrontierMath Tier 4 | 72.5% | 25.6% |
| MathArena Final-Answer Competitions | 94.3% | 72.9% |
| OTIS Mock AIME 2024-2025 | 100% | 96.1% |
| ProofBench | 50% | 16% |
| LMArena Math | 1486 | 1475 |
| FrontierMath (Feb 2025 set) | 51.7% | 39% |
| FrontierMath Tier 4 (v1) | 35.4% | 14.6% |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.5 leads
GPT-5.5: 64.4 (#17), Kimi K2.6: 54.0 (#54)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| GPQA Diamond | 94% | 90.8% |
| SimpleQA Verified | 63% | 34.9% |
| Vectara Hallucination Rate | 9.3% | 10.8% |
| LMArena Expert | 1508 | 1491 |
Multimodal GPT-5.5 leads
GPT-5.5: 46.9 (#12), Kimi K2.6: 31.6 (#103)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| LMArena Vision | 1297 | 1283 |
| Blueprint-Bench 2 | 36.2% | 3.9% |
| Furniture Assembly | 44.2% | 21.7% |
| LMArena Document | 1486 | 1451 |
Multilingual GPT-5.5 leads
GPT-5.5: 56.4 (#20), Kimi K2.6: 54.9 (#37)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| LMArena Non-English | 1467 | 1446 |
| LMArena Chinese | 1533 | 1521 |
| LMArena French | 1486 | 1471 |
| LMArena German | 1480 | 1450 |
| LMArena Japanese | 1498 | 1443 |
| LMArena Korean | 1460 | 1427 |
| LMArena Russian | 1473 | 1446 |
| LMArena Spanish | 1468 | 1464 |
Instruction Following GPT-5.5 leads
GPT-5.5: 77.5 (#18), Kimi K2.6: 76.3 (#43)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| LMArena Instruction Following | 1479 | 1451 |
Long Context GPT-5.5 leads
GPT-5.5: 48.3 (#12), Kimi K2.6: 44.9 (#52)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| LMArena Longer Query | 1484 | 1468 |
| CL-bench Life | 22.2% | — |
Writing & Preference GPT-5.5 leads
GPT-5.5: 72.7 (#13), Kimi K2.6: 68.5 (#26)
| Benchmark | GPT-5.5 | Kimi K2.6 |
|---|---|---|
| LMArena Text | 1472 | 1455 |
| LMArena Creative Writing | 1455 | 1434 |
| EQ-Bench Creative Writing | 1844 | 1725 |
| EQ-Bench 4 | 1315 | 1202 |
| LMArena Multi-Turn | 1476 | 1453 |
Frequently asked questions
Is GPT-5.5 better than Kimi K2.6?
GPT-5.5 is the stronger model overall, scoring 63.4 to 47.7 on the Noometry Index. Kimi K2.6 costs 6.6× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Which is cheaper, GPT-5.5 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-5.5 lists at $5 and $30.
Is GPT-5.5 or Kimi K2.6 better for coding?
GPT-5.5 scores higher on coding benchmarks: 58.2 versus 50.7 in the Noometry coding category.
Which has the bigger context window?
GPT-5.5 does, with 1.05M tokens against 262K.
How many benchmarks do GPT-5.5 and Kimi K2.6 share?
51 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Kimi K2.6 has 51.