Model comparison
gpt-oss-120b vs Kimi K2.6
Kimi K2.6 is the stronger model overall, scoring 47.5 to 36.0 on the Noometry Index. gpt-oss-120b costs 24× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
Summary
- They share 31 benchmarks with published results for both. gpt-oss-120b scores higher in 0 categories and Kimi K2.6 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Kimi K2.6 leads 68.7 to 46.7.
- The biggest single-benchmark swing is SciCode: 36% for gpt-oss-120b and 53.5% for Kimi K2.6.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.95 / $4 for Kimi K2.6.
- Kimi K2.6 accepts more context: 262K tokens versus 131K.
Side by side
| gpt-oss-120b | Kimi K2.6 | |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 36.0 | 47.5 |
| Released | 2025-08-05 | 2026-04-20 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 41K | 262K |
| Input $ / M tokens | $0.037 | $0.95 |
| Output $ / M tokens | $0.17 | $4 |
| Results tracked | 48 | 51 |
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Category by category
Coding Kimi K2.6 leads
gpt-oss-120b: 33.2 (#251), Kimi K2.6: 50.5 (#41)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| SciCode | 36% | 53.5% |
| WeirdML | 48.2% | 55.9% |
| LMArena Coding | 1380 | 1488 |
| ALE-Bench | 575.62 | 1,093 |
| SWE-bench Verified | — | 76.7% |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1509 |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Kimi K2.6 leads
gpt-oss-120b: 12.1 (#145), Kimi K2.6: 21.8 (#128)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| Vending-Bench 2 | -21.53 | 6,205 |
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| OSWorld 2.0 | — | 4.6% |
| ExploitBench | — | 18.4% |
| GBAEval | — | 0.9% |
| GDP.pdf | — | 12% |
| METR Time Horizons | 56.6% | — |
Reasoning Kimi K2.6 leads
gpt-oss-120b: 19.5 (#236), Kimi K2.6: 39.6 (#51)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| CritPt | 1.1% | 8% |
| Chess Puzzles | 20% | 26% |
| LMArena Hard Prompts | 1364 | 1470 |
| Mystery Game Puzzles | 2% | 18% |
| DTBench | 76.3% | 90.9% |
| LMCA | 22.1% | 37.3% |
| Epoch Capabilities Index | 139.94 | 151.03 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| NYT Connections (extended) | — | 87.2% |
| EBR-Bench | — | 2.4% |
| Surface Evolver Bench | 25% | — |
Math Kimi K2.6 leads
gpt-oss-120b: 50.6 (#43), Kimi K2.6: 56.8 (#34)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 96.1% |
| LMArena Math | 1388 | 1475 |
| FrontierMath (Tiers 1-3) | — | 57.2% |
| FrontierMath Tier 4 | — | 25.6% |
| MathArena Final-Answer Competitions | — | 72.9% |
| ProofBench | — | 16% |
| Omni-MATH | 68.8% | — |
| FrontierMath (Feb 2025 set) | — | 39% |
| FrontierMath Tier 4 (v1) | — | 14.6% |
Knowledge Kimi K2.6 leads
gpt-oss-120b: 42.3 (#89), Kimi K2.6: 53.9 (#48)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| GPQA Diamond | 75.8% | 90.8% |
| Vectara Hallucination Rate | 14.2% | 10.8% |
| LMArena Expert | 1356 | 1491 |
| SimpleQA Verified | — | 34.9% |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| GPQA (HELM) | 68.4% | — |
Multimodal Not comparable
gpt-oss-120b: —, Kimi K2.6: 31.8 (#95)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| LMArena Vision | — | 1282 |
| Blueprint-Bench 2 | — | 3.9% |
| Furniture Assembly | — | 21.7% |
| LMArena Document | — | 1451 |
Multilingual Kimi K2.6 leads
gpt-oss-120b: 48.2 (#142), Kimi K2.6: 55.1 (#35)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| LMArena Non-English | 1350 | 1446 |
| LMArena Chinese | 1384 | 1521 |
| LMArena French | 1370 | 1471 |
| LMArena German | 1355 | 1449 |
| LMArena Japanese | 1332 | 1442 |
| LMArena Korean | 1283 | 1428 |
| LMArena Russian | 1343 | 1446 |
| LMArena Spanish | 1388 | 1463 |
Instruction Following Kimi K2.6 leads
gpt-oss-120b: 69.5 (#168), Kimi K2.6: 76.5 (#40)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| LMArena Instruction Following | 1318 | 1451 |
| IFEval | 83.6% | — |
Long Context Kimi K2.6 leads
gpt-oss-120b: 31.4 (#273), Kimi K2.6: 44.9 (#50)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| LMArena Longer Query | 1319 | 1468 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Kimi K2.6 leads
gpt-oss-120b: 46.7 (#213), Kimi K2.6: 68.7 (#25)
| Benchmark | gpt-oss-120b | Kimi K2.6 |
|---|---|---|
| LMArena Text | 1366 | 1455 |
| LMArena Creative Writing | 1274 | 1434 |
| EQ-Bench Creative Writing | 961 | 1725 |
| LMArena Multi-Turn | 1340 | 1453 |
| Short-Story Creative Writing | 77.1% | — |
| WildBench | 84.5% | — |
| EQ-Bench 4 | — | 1202 |
Frequently asked questions
Is gpt-oss-120b better than Kimi K2.6?
Kimi K2.6 is the stronger model overall, scoring 47.5 to 36.0 on the Noometry Index. gpt-oss-120b costs 24× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-120b or Kimi K2.6?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Kimi K2.6 lists at $0.95 and $4.
Is gpt-oss-120b or Kimi K2.6 better for coding?
Kimi K2.6 scores higher on coding benchmarks: 50.5 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.6 does, with 262K tokens against 131K.
How many benchmarks do gpt-oss-120b and Kimi K2.6 share?
31 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Kimi K2.6 has 51.