Model comparison
Kimi K2.7 Code vs Qwen2.5 32B Instruct
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 30.1 on the Noometry Index.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. Kimi K2.7 Code scores higher in 4 categories and Qwen2.5 32B Instruct in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.7 Code leads 52.9 to 16.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 95.6% for Kimi K2.7 Code and 7.4% for Qwen2.5 32B Instruct.
- Qwen2.5 32B Instruct is cheaper at $0.70 / $2.80 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
| Kimi K2.7 Code | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 43.3 | 30.1 |
| Released | 2026-06-12 | 2024-09 |
| Weights | Open | Open |
| Context window | 262K | 131K |
| Max output | 262K | 8K |
| Input $ / M tokens | $0.95 | $0.70 |
| Output $ / M tokens | $4 | $2.80 |
| Results tracked | 19 | 7 |
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Category by category
Coding Kimi K2.7 Code leads
Kimi K2.7 Code: 42.9 (#95), Qwen2.5 32B Instruct: 38.7 (#169)
| Benchmark | Kimi K2.7 Code | Qwen2.5 32B Instruct |
|---|---|---|
| DeepSWE | 30.5% | — |
| FrontierCode | 30.1% | — |
| LMArena WebDev | 1473 | — |
| SciCode | 47.5% | — |
| WeirdML | 54.1% | — |
| BigCodeBench Instruct | — | 45% |
| BigCodeBench Complete | — | 52.3% |
| ALE-Bench | 886.23 | — |
Agentic & Tool Use Not comparable
Kimi K2.7 Code: 24.0 (#122), Qwen2.5 32B Instruct: —
| Benchmark | Kimi K2.7 Code | Qwen2.5 32B Instruct |
|---|---|---|
| APEX-Agents | 37.6% | — |
| GBAEval | 0.9% | — |
| Vending-Bench 2 | 5,083 | — |
Reasoning Kimi K2.7 Code leads
Kimi K2.7 Code: 39.0 (#61), Qwen2.5 32B Instruct: 19.2 (#266)
| Benchmark | Kimi K2.7 Code | Qwen2.5 32B Instruct |
|---|---|---|
| Chess Puzzles | 21% | 0% |
| Epoch Capabilities Index | 149.97 | 128.52 |
| SimpleBench | 57.9% | — |
| CritPt | 10% | — |
| Surface Evolver Bench | 48.8% | — |
Math Kimi K2.7 Code leads
Kimi K2.7 Code: 52.9 (#48), Qwen2.5 32B Instruct: 16.2 (#296)
| Benchmark | Kimi K2.7 Code | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 95.6% | 7.4% |
| FrontierMath (Tiers 1-3) | 54% | — |
| FrontierMath Tier 4 | 12.2% | — |
| MATH Level 5 | — | 56.1% |
Knowledge Kimi K2.7 Code leads
Kimi K2.7 Code: 53.5 (#57), Qwen2.5 32B Instruct: 24.9 (#266)
| Benchmark | Kimi K2.7 Code | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | 87.9% | 46.1% |
| SimpleQA Verified | 36.5% | — |
Frequently asked questions
Is Kimi K2.7 Code better than Qwen2.5 32B Instruct?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 30.1 on the Noometry Index.
Which is cheaper, Kimi K2.7 Code or Qwen2.5 32B Instruct?
Qwen2.5 32B Instruct is cheaper. It lists at $0.70 per million input tokens and $2.80 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is Kimi K2.7 Code or Qwen2.5 32B Instruct better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 38.7 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 Kimi K2.7 Code and Qwen2.5 32B Instruct share?
4 benchmarks have published results for both models. Kimi K2.7 Code has 19 scored results on Noometry and Qwen2.5 32B Instruct has 7.