Model comparison
Kimi K2.6 vs Qwen2.5-Coder-32B
Kimi K2.6 is the stronger model overall, scoring 47.7 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 K2.6's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. Kimi K2.6 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 coding, where Kimi K2.6 leads 50.7 to 22.6.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.95 / $4 for Kimi K2.6.
- Kimi K2.6 accepts more context: 262K tokens versus 33K.
Side by side
| Kimi K2.6 | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 47.7 | 33.4 |
| Released | 2026-04-20 | 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 | 51 | 31 |
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Category by category
Coding Kimi K2.6 leads
Kimi K2.6: 50.7 (#43), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1488 | 1276 |
| SWE-bench Verified | 76.7% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1509 | — |
| SciCode | 53.5% | — |
| WeirdML | 55.9% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 1,093 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
Kimi K2.6: 21.9 (#137), Qwen2.5-Coder-32B: —
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| OSWorld 2.0 | 4.6% | — |
| ExploitBench | 18.4% | — |
| GBAEval | 0.9% | — |
| GDP.pdf | 12% | — |
| Vending-Bench 2 | 6,205 | — |
Reasoning Kimi K2.6 leads
Kimi K2.6: 40.5 (#55), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1470 | 1251 |
| Epoch Capabilities Index | 151.05 | 119.49 |
| NYT Connections (extended) | 87.2% | — |
| CritPt | 8% | — |
| Chess Puzzles | 26% | — |
| EBR-Bench | 2.4% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 18% | — |
| DTBench | 90.9% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 37.3% | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math Kimi K2.6 leads
Kimi K2.6: 57.0 (#41), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1475 | 1251 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 25.6% | — |
| MathArena Final-Answer Competitions | 72.9% | — |
| OTIS Mock AIME 2024-2025 | 96.1% | — |
| ProofBench | 16% | — |
| LiveBench Math | — | 46.6% |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |
| GSM8K | — | 93% |
Knowledge Kimi K2.6 leads
Kimi K2.6: 54.0 (#54), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1491 | 1221 |
| GPQA Diamond | 90.8% | — |
| SimpleQA Verified | 34.9% | — |
| Vectara Hallucination Rate | 10.8% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
Kimi K2.6: 31.6 (#103), Qwen2.5-Coder-32B: —
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1283 | — |
| Blueprint-Bench 2 | 3.9% | — |
| Furniture Assembly | 21.7% | — |
| LMArena Document | 1451 | — |
Multilingual Kimi K2.6 leads
Kimi K2.6: 54.9 (#37), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1446 | 1205 |
| LMArena Chinese | 1521 | 1222 |
| LMArena Russian | 1446 | 1228 |
| LMArena French | 1471 | — |
| LMArena German | 1450 | — |
| LMArena Japanese | 1443 | — |
| LMArena Korean | 1427 | — |
| LMArena Spanish | 1464 | — |
Instruction Following Kimi K2.6 leads
Kimi K2.6: 76.3 (#43), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1451 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context Kimi K2.6 leads
Kimi K2.6: 44.9 (#52), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1468 | 1251 |
Writing & Preference Kimi K2.6 leads
Kimi K2.6: 68.5 (#26), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | Kimi K2.6 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1455 | 1230 |
| LMArena Creative Writing | 1434 | 1174 |
| LMArena Multi-Turn | 1453 | 1222 |
| EQ-Bench Creative Writing | 1725 | — |
| EQ-Bench 4 | 1202 | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is Kimi K2.6 better than Qwen2.5-Coder-32B?
Kimi K2.6 is the stronger model overall, scoring 47.7 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 K2.6's lead doesn't matter for your workload.
Which is cheaper, Kimi K2.6 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 K2.6 lists at $0.95 and $4.
Is Kimi K2.6 or Qwen2.5-Coder-32B better for coding?
Kimi K2.6 scores higher on coding benchmarks: 50.7 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.6 does, with 262K tokens against 33K.
How many benchmarks do Kimi K2.6 and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. Kimi K2.6 has 51 scored results on Noometry and Qwen2.5-Coder-32B has 31.