Model comparison
DeepSeek-R1-Distill-Qwen-32B vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 35.5 on the Noometry Index.
Last verified . 4 shared benchmarks.
DeepSeekSummary
- They share 4 benchmarks with published results for both. DeepSeek-R1-Distill-Qwen-32B scores higher in 1 category and Kimi K2.7 Code in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 39.0 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 55.6% for DeepSeek-R1-Distill-Qwen-32B and 95.6% for Kimi K2.7 Code.
Side by side
| DeepSeek-R1-Distill-Qwen-32B | Kimi K2.7 Code | |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Noometry Index | 35.5 | 43.3 |
| Released | 2025-01-20 | 2026-06-12 |
| Weights | Open | Open |
| Context window | — | 262K |
| Max output | — | 262K |
| Input $ / M tokens | — | $0.95 |
| Output $ / M tokens | — | $4 |
| Results tracked | 14 | 19 |
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Category by category
Coding Kimi K2.7 Code leads
DeepSeek-R1-Distill-Qwen-32B: 36.1 (#212), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Kimi K2.7 Code |
|---|---|---|
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| WeirdML | — | 54.1% |
| BigCodeBench Instruct | 43.9% | — |
| LiveBench Coding | 33.7% | — |
| BigCodeBench Complete | 54.9% | — |
| ALE-Bench | — | 886.23 |
Agentic & Tool Use DeepSeek-R1-Distill-Qwen-32B leads
DeepSeek-R1-Distill-Qwen-32B: 28.1 (#94), Kimi K2.7 Code: 24.0 (#122)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| BALROG | 19.5% | — |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
DeepSeek-R1-Distill-Qwen-32B: 18.2 (#284), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Kimi K2.7 Code |
|---|---|---|
| Chess Puzzles | 1% | 21% |
| Epoch Capabilities Index | 137.44 | 149.97 |
| SimpleBench | — | 57.9% |
| CritPt | — | 10% |
| LiveBench Reasoning | 52.3% | — |
| LiveBench Data Analysis | 45.4% | — |
| Surface Evolver Bench | — | 48.8% |
| LiveBench | 45.5% | — |
Math Kimi K2.7 Code leads
DeepSeek-R1-Distill-Qwen-32B: 34.5 (#194), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Kimi K2.7 Code |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 55.6% | 95.6% |
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
| LiveBench Math | 59.4% | — |
Knowledge Kimi K2.7 Code leads
DeepSeek-R1-Distill-Qwen-32B: 35.7 (#182), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 64.1% | 87.9% |
| SimpleQA Verified | — | 36.5% |
Instruction Following Not comparable
DeepSeek-R1-Distill-Qwen-32B: 61.6 (#243), Kimi K2.7 Code: —
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Kimi K2.7 Code |
|---|---|---|
| LiveBench Instruction Following | 55.7% | — |
Writing & Preference Not comparable
DeepSeek-R1-Distill-Qwen-32B: 49.6 (#188), Kimi K2.7 Code: —
| Benchmark | DeepSeek-R1-Distill-Qwen-32B | Kimi K2.7 Code |
|---|---|---|
| LiveBench Language | 26.8% | — |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-32B better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 35.5 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-32B or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 36.1 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-32B and Kimi K2.7 Code share?
4 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-32B has 14 scored results on Noometry and Kimi K2.7 Code has 19.