Model comparison
DeepSeek-R1-Distill-Qwen-1.5B vs Kimi K2.7 Code
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 26.1 on the Noometry Index.
Last verified . 3 shared benchmarks.
DeepSeekSummary
- They share 3 benchmarks with published results for both. DeepSeek-R1-Distill-Qwen-1.5B scores higher in 0 categories and Kimi K2.7 Code in 4 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Kimi K2.7 Code leads 53.5 to 16.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 21.4% for DeepSeek-R1-Distill-Qwen-1.5B and 95.6% for Kimi K2.7 Code.
Side by side
| DeepSeek-R1-Distill-Qwen-1.5B | Kimi K2.7 Code | |
|---|---|---|
| Provider | DeepSeek | Moonshot AI |
| Noometry Index | 26.1 | 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 | 5 | 19 |
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Category by category
Coding Kimi K2.7 Code leads
DeepSeek-R1-Distill-Qwen-1.5B: 21.8 (#336), Kimi K2.7 Code: 42.9 (#95)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Kimi K2.7 Code |
|---|---|---|
| DeepSWE | — | 30.5% |
| FrontierCode | — | 30.1% |
| LMArena WebDev | — | 1473 |
| SciCode | — | 47.5% |
| WeirdML | — | 54.1% |
| BigCodeBench Instruct | 7% | — |
| BigCodeBench Complete | 7.9% | — |
| ALE-Bench | — | 886.23 |
Agentic & Tool Use Not comparable
DeepSeek-R1-Distill-Qwen-1.5B: —, Kimi K2.7 Code: 24.0 (#122)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Kimi K2.7 Code |
|---|---|---|
| APEX-Agents | — | 37.6% |
| GBAEval | — | 0.9% |
| Vending-Bench 2 | — | 5,083 |
Reasoning Kimi K2.7 Code leads
DeepSeek-R1-Distill-Qwen-1.5B: 19.2 (#262), Kimi K2.7 Code: 39.0 (#61)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Kimi K2.7 Code |
|---|---|---|
| Chess Puzzles | 0% | 21% |
| SimpleBench | — | 57.9% |
| CritPt | — | 10% |
| Surface Evolver Bench | — | 48.8% |
| Epoch Capabilities Index | — | 149.97 |
Math Kimi K2.7 Code leads
DeepSeek-R1-Distill-Qwen-1.5B: 23.0 (#274), Kimi K2.7 Code: 52.9 (#48)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Kimi K2.7 Code |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 21.4% | 95.6% |
| FrontierMath (Tiers 1-3) | — | 54% |
| FrontierMath Tier 4 | — | 12.2% |
Knowledge Kimi K2.7 Code leads
DeepSeek-R1-Distill-Qwen-1.5B: 16.0 (#290), Kimi K2.7 Code: 53.5 (#57)
| Benchmark | DeepSeek-R1-Distill-Qwen-1.5B | Kimi K2.7 Code |
|---|---|---|
| GPQA Diamond | 33.6% | 87.9% |
| SimpleQA Verified | — | 36.5% |
Frequently asked questions
Is DeepSeek-R1-Distill-Qwen-1.5B better than Kimi K2.7 Code?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 26.1 on the Noometry Index.
Is DeepSeek-R1-Distill-Qwen-1.5B or Kimi K2.7 Code better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 21.8 in the Noometry coding category.
How many benchmarks do DeepSeek-R1-Distill-Qwen-1.5B and Kimi K2.7 Code share?
3 benchmarks have published results for both models. DeepSeek-R1-Distill-Qwen-1.5B has 5 scored results on Noometry and Kimi K2.7 Code has 19.