Model comparison
Kimi K2.7 Code vs Qwen2.5-Coder-32B
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 2.3× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Last verified . 1 shared benchmarks.
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. Kimi K2.7 Code scores higher in 4 categories and Qwen2.5-Coder-32B in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in coding, where Kimi K2.7 Code leads 42.9 to 22.6.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 33K.
Side by side
| Kimi K2.7 Code | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 43.3 | 33.4 |
| Released | 2026-06-12 | 2024-09-18 |
| Weights | Open | Open |
| Context window | 262K | 33K |
| Max output | 262K | 29K |
| Input $ / M tokens | $0.95 | $0.66 |
| Output $ / M tokens | $4 | $1 |
| Results tracked | 19 | 31 |
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Category by category
Coding Kimi K2.7 Code leads
Kimi K2.7 Code: 42.9 (#95), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| DeepSWE | 30.5% | — |
| FrontierCode | 30.1% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1473 | — |
| SciCode | 47.5% | — |
| WeirdML | 54.1% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| LMArena Coding | — | 1276 |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 886.23 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
Kimi K2.7 Code: 24.0 (#122), Qwen2.5-Coder-32B: —
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| 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-Coder-32B: 21.2 (#225)
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| Epoch Capabilities Index | 149.97 | 119.49 |
| SimpleBench | 57.9% | — |
| CritPt | 10% | — |
| Chess Puzzles | 21% | — |
| LiveBench Reasoning | — | 42.1% |
| LMArena Hard Prompts | — | 1251 |
| LiveBench Data Analysis | — | 49.9% |
| Surface Evolver Bench | 48.8% | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Kimi K2.7 Code leads
Kimi K2.7 Code: 52.9 (#48), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 54% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 95.6% | — |
| LiveBench Math | — | 46.6% |
| LMArena Math | — | 1251 |
| GSM8K | — | 93% |
Knowledge Kimi K2.7 Code leads
Kimi K2.7 Code: 53.5 (#57), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| GPQA Diamond | 87.9% | — |
| SimpleQA Verified | 36.5% | — |
| LMArena Expert | — | 1221 |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multilingual Not comparable
Kimi K2.7 Code: —, Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | — | 1205 |
| LMArena Chinese | — | 1222 |
| LMArena Russian | — | 1228 |
Instruction Following Not comparable
Kimi K2.7 Code: —, Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| LiveBench Instruction Following | — | 58.7% |
| LMArena Instruction Following | — | 1223 |
Long Context Not comparable
Kimi K2.7 Code: —, Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | — | 1251 |
Writing & Preference Not comparable
Kimi K2.7 Code: —, Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | Kimi K2.7 Code | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | — | 1230 |
| LMArena Creative Writing | — | 1174 |
| LMArena Multi-Turn | — | 1222 |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is Kimi K2.7 Code better than Qwen2.5-Coder-32B?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 2.3× 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, Kimi K2.7 Code or Qwen2.5-Coder-32B?
Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is Kimi K2.7 Code or Qwen2.5-Coder-32B better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.7 Code does, with 262K tokens against 33K.
How many benchmarks do Kimi K2.7 Code and Qwen2.5-Coder-32B share?
1 benchmark has published results for both models. Kimi K2.7 Code has 19 scored results on Noometry and Qwen2.5-Coder-32B has 31.