Model comparison
Kimi K2 (Jul 2025) vs Qwen2.5-Coder-32B
Kimi K2 (Jul 2025) is the stronger model overall, scoring 41.2 to 33.4 on the Noometry Index.
Last verified . 15 shared benchmarks.
Alibaba (Qwen)Summary
- They share 15 benchmarks with published results for both. Kimi K2 (Jul 2025) scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Kimi K2 (Jul 2025) leads 62.3 to 41.6.
- The biggest single-benchmark swing is SWE-bench Verified (bash only): 63.4% for Kimi K2 (Jul 2025) and 9% for Qwen2.5-Coder-32B.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.57 / $2.30 for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 33K.
Side by side
| Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 41.2 | 33.4 |
| Released | 2025-07-12 | 2024-09-18 |
| Weights | Open | Open |
| Context window | 262K | 33K |
| Max output | 262K | 29K |
| Input $ / M tokens | $0.57 | $0.66 |
| Output $ / M tokens | $2.30 | $1 |
| Results tracked | 42 | 31 |
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Category by category
Coding Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 42.4 (#102), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| SWE-bench Verified (bash only) | 63.4% | 9% |
| Aider Polyglot | 59.1% | 16.4% |
| LMArena Coding | 1399 | 1276 |
| GSO | 4.9% | — |
| WeirdML | 42.8% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 597.5 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
Kimi K2 (Jul 2025): 32.4 (#64), Qwen2.5-Coder-32B: —
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| Terminal-Bench | 35.7% | — |
| Berkeley Function Calling Leaderboard | 59.1% | — |
| METR Time Horizons | 59.2% | — |
Reasoning Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 23.3 (#179), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1384 | 1251 |
| Epoch Capabilities Index | 146.01 | 119.49 |
| SimpleBench | 26.3% | — |
| Kagi LLM Benchmark | 64.4% | — |
| LiveBench Reasoning | — | 42.1% |
| LiveBench Data Analysis | — | 49.9% |
| ForecastBench | 60.2 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 42.7 (#83), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1397 | 1251 |
| Omni-MATH | 65.4% | — |
| LiveBench Math | — | 46.6% |
| FrontierMath (Feb 2025 set) | 21.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 93% |
Knowledge Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 37.3 (#157), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1365 | 1221 |
| MMLU-Pro | 81.9% | — |
| Confabulations | 20.4% | — |
| Vectara Hallucination Rate | 17.9% | — |
| GPQA (HELM) | 65.3% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multilingual Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 49.6 (#130), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1372 | 1205 |
| LMArena Chinese | 1415 | 1222 |
| LMArena Russian | 1385 | 1228 |
| LMArena French | 1379 | — |
| LMArena German | 1387 | — |
| LMArena Japanese | 1349 | — |
| LMArena Korean | 1325 | — |
| LMArena Spanish | 1386 | — |
Instruction Following Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 71.1 (#156), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1348 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
| IFEval | 85% | — |
Long Context Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 41.2 (#145), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1353 | 1251 |
| Fiction.LiveBench | 66.7% | — |
| CL-bench | 17.6% | — |
Writing & Preference Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 62.3 (#78), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1380 | 1230 |
| LMArena Creative Writing | 1350 | 1174 |
| LMArena Multi-Turn | 1371 | 1222 |
| Short-Story Creative Writing | 85.6% | — |
| EQ-Bench Creative Writing | 1666 | — |
| WildBench | 86.2% | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is Kimi K2 (Jul 2025) better than Qwen2.5-Coder-32B?
Kimi K2 (Jul 2025) is the stronger model overall, scoring 41.2 to 33.4 on the Noometry Index.
Which is cheaper, Kimi K2 (Jul 2025) 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 (Jul 2025) lists at $0.57 and $2.30.
Is Kimi K2 (Jul 2025) or Qwen2.5-Coder-32B better for coding?
Kimi K2 (Jul 2025) scores higher on coding benchmarks: 42.4 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
Kimi K2 (Jul 2025) does, with 262K tokens against 33K.
How many benchmarks do Kimi K2 (Jul 2025) and Qwen2.5-Coder-32B share?
15 benchmarks have published results for both models. Kimi K2 (Jul 2025) has 42 scored results on Noometry and Qwen2.5-Coder-32B has 31.