Model comparison
Kimi K2.6 vs Qwen2.5 32B Instruct
Kimi K2.6 is the stronger model overall, scoring 47.5 to 29.7 on the Noometry Index.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. Kimi K2.6 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 K2.6 leads 56.8 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 96.1% for Kimi K2.6 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 K2.6.
- Kimi K2.6 accepts more context: 262K tokens versus 131K.
Side by side
| Kimi K2.6 | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 47.5 | 29.7 |
| Released | 2026-04-20 | 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 | 51 | 7 |
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Category by category
Coding Kimi K2.6 leads
Kimi K2.6: 50.5 (#41), Qwen2.5 32B Instruct: 38.1 (#165)
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| SWE-bench Verified | 76.7% | — |
| LMArena WebDev | 1509 | — |
| SciCode | 53.5% | — |
| WeirdML | 55.9% | — |
| BigCodeBench Instruct | — | 45% |
| LMArena Coding | 1488 | — |
| BigCodeBench Complete | — | 52.3% |
| ALE-Bench | 1,093 | — |
Agentic & Tool Use Not comparable
Kimi K2.6: 21.8 (#128), Qwen2.5 32B Instruct: —
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| 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: 39.6 (#51), Qwen2.5 32B Instruct: 18.5 (#262)
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| Chess Puzzles | 26% | 0% |
| Epoch Capabilities Index | 151.03 | 128.52 |
| NYT Connections (extended) | 87.2% | — |
| CritPt | 8% | — |
| EBR-Bench | 2.4% | — |
| LMArena Hard Prompts | 1470 | — |
| Mystery Game Puzzles | 18% | — |
| DTBench | 90.9% | — |
| LMCA | 37.3% | — |
Math Kimi K2.6 leads
Kimi K2.6: 56.8 (#34), Qwen2.5 32B Instruct: 15.3 (#289)
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 96.1% | 7.4% |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 25.6% | — |
| MathArena Final-Answer Competitions | 72.9% | — |
| ProofBench | 16% | — |
| LMArena Math | 1475 | — |
| MATH Level 5 | — | 56.1% |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge Kimi K2.6 leads
Kimi K2.6: 53.9 (#48), Qwen2.5 32B Instruct: 24.9 (#259)
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | 90.8% | 46.1% |
| SimpleQA Verified | 34.9% | — |
| Vectara Hallucination Rate | 10.8% | — |
| LMArena Expert | 1491 | — |
Multimodal Not comparable
Kimi K2.6: 31.8 (#95), Qwen2.5 32B Instruct: —
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Vision | 1282 | — |
| Blueprint-Bench 2 | 3.9% | — |
| Furniture Assembly | 21.7% | — |
| LMArena Document | 1451 | — |
Multilingual Not comparable
Kimi K2.6: 55.1 (#35), Qwen2.5 32B Instruct: —
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Non-English | 1446 | — |
| LMArena Chinese | 1521 | — |
| LMArena French | 1471 | — |
| LMArena German | 1449 | — |
| LMArena Japanese | 1442 | — |
| LMArena Korean | 1428 | — |
| LMArena Russian | 1446 | — |
| LMArena Spanish | 1463 | — |
Instruction Following Not comparable
Kimi K2.6: 76.5 (#40), Qwen2.5 32B Instruct: —
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Instruction Following | 1451 | — |
Long Context Not comparable
Kimi K2.6: 44.9 (#50), Qwen2.5 32B Instruct: —
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Longer Query | 1468 | — |
Writing & Preference Not comparable
Kimi K2.6: 68.7 (#25), Qwen2.5 32B Instruct: —
| Benchmark | Kimi K2.6 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Text | 1455 | — |
| LMArena Creative Writing | 1434 | — |
| EQ-Bench Creative Writing | 1725 | — |
| EQ-Bench 4 | 1202 | — |
| LMArena Multi-Turn | 1453 | — |
Frequently asked questions
Is Kimi K2.6 better than Qwen2.5 32B Instruct?
Kimi K2.6 is the stronger model overall, scoring 47.5 to 29.7 on the Noometry Index.
Which is cheaper, Kimi K2.6 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 K2.6 lists at $0.95 and $4.
Is Kimi K2.6 or Qwen2.5 32B Instruct better for coding?
Kimi K2.6 scores higher on coding benchmarks: 50.5 versus 38.1 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.6 does, with 262K tokens against 131K.
How many benchmarks do Kimi K2.6 and Qwen2.5 32B Instruct share?
4 benchmarks have published results for both models. Kimi K2.6 has 51 scored results on Noometry and Qwen2.5 32B Instruct has 7.