Model comparison
gpt-oss-20b vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 32.5 on the Noometry Index. gpt-oss-20b costs 48× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Last verified . 8 shared benchmarks.
Summary
- They share 8 benchmarks with published results for both. gpt-oss-20b scores higher in 0 categories and Kimi K2.7 Code in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 39.0 to 19.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 65.3% for gpt-oss-20b and 95.6% for Kimi K2.7 Code.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 131K.
Side by side
| gpt-oss-20b | Kimi K2.7 Code | |
|---|---|---|
| Provider | OpenAI | Moonshot AI |
| Noometry Index | 32.5 | 43.3 |
| Released | 2025-08-05 | 2026-06-12 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 16K | 262K |
| Input $ / M tokens | $0.018 | $0.95 |
| Output $ / M tokens | $0.09 | $4 |
| Results tracked | 34 | 19 |
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Category by category
Coding Kimi K2.7 Code leads
gpt-oss-20b: 37.6 (#192), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| SciCode | 34.4% | 47.5% |
| WeirdML | 40.9% | 54.1% |
| ALE-Bench | 566.05 | 886.23 |
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| LMArena WebDev | — | 1473 |
| LMArena Coding | 1306 | — |
Agentic & Tool Use Kimi K2.7 Code leads
gpt-oss-20b: 9.3 (#154), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| Terminal-Bench | 3.4% | — |
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
gpt-oss-20b: 19.3 (#261), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| CritPt | 1.4% | 10% |
| Chess Puzzles | 4% | 21% |
| Epoch Capabilities Index | 137.82 | 149.97 |
| SimpleBench | — | 57.9% |
| Kagi LLM Benchmark | 53.2% | — |
| LMArena Hard Prompts | 1274 | — |
| DTBench | 68% | — |
| LMCA | 14.5% | — |
| Surface Evolver Bench | — | 48.8% |
Math Kimi K2.7 Code leads
gpt-oss-20b: 39.4 (#103), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 65.3% | 95.6% |
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| Omni-MATH | 56.5% | — |
| LMArena Math | 1317 | — |
Knowledge Kimi K2.7 Code leads
gpt-oss-20b: 34.6 (#195), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 60.8% | 87.9% |
| SimpleQA Verified | — | 36.5% |
| MMLU-Pro | 74% | — |
| GPQA (HELM) | 59.4% | — |
| LMArena Expert | 1258 | — |
Multilingual Not comparable
gpt-oss-20b: 42.2 (#197), Kimi K2.7 Code: —
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| LMArena Non-English | 1268 | — |
| LMArena Chinese | 1314 | — |
| LMArena German | 1255 | — |
| LMArena Japanese | 1244 | — |
| LMArena Korean | 1236 | — |
| LMArena Russian | 1278 | — |
| LMArena Spanish | 1267 | — |
Instruction Following Not comparable
gpt-oss-20b: 61.8 (#240), Kimi K2.7 Code: —
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| IFEval | 73.2% | — |
| LMArena Instruction Following | 1236 | — |
Long Context Not comparable
gpt-oss-20b: 37.9 (#209), Kimi K2.7 Code: —
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| LMArena Longer Query | 1250 | — |
Writing & Preference Not comparable
gpt-oss-20b: 35.5 (#265), Kimi K2.7 Code: —
| Benchmark | gpt-oss-20b | Kimi K2.7 Code |
|---|---|---|
| LMArena Text | 1287 | — |
| LMArena Creative Writing | 1201 | — |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |
| LMArena Multi-Turn | 1268 | — |
Frequently asked questions
Is gpt-oss-20b better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 32.5 on the Noometry Index. gpt-oss-20b costs 48× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-20b or Kimi K2.7 Code?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is gpt-oss-20b or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 37.6 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.7 Code does, with 262K tokens against 131K.
How many benchmarks do gpt-oss-20b and Kimi K2.7 Code share?
8 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and Kimi K2.7 Code has 19.