Model comparison
Kimi K2.6 vs Qwen3-Coder 480B-A35B Instruct
Kimi K2.6 is the stronger model overall, scoring 47.7 to 38.1 on the Noometry Index.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. Kimi K2.6 scores higher in 8 categories and Qwen3-Coder 480B-A35B Instruct in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.6 leads 57.0 to 37.6.
- The biggest single-benchmark swing is WeirdML: 55.9% for Kimi K2.6 and 41.2% for Qwen3-Coder 480B-A35B Instruct.
- Kimi K2.6 is cheaper at $0.95 / $4 per million input/output tokens, against $1.50 / $7.50 for Qwen3-Coder 480B-A35B Instruct.
Side by side
| Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 47.7 | 38.1 |
| Released | 2026-04-20 | 2025-04 |
| Weights | Open | Open |
| Context window | 262K | 262K |
| Max output | 262K | 66K |
| Input $ / M tokens | $0.95 | $1.50 |
| Output $ / M tokens | $4 | $7.50 |
| Results tracked | 51 | 25 |
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Category by category
Coding Kimi K2.6 leads
Kimi K2.6: 50.7 (#43), Qwen3-Coder 480B-A35B Instruct: 35.5 (#223)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena WebDev | 1509 | 1275 |
| WeirdML | 55.9% | 41.2% |
| LMArena Coding | 1488 | 1412 |
| ALE-Bench | 1,093 | 461.45 |
| SWE-bench Verified | 76.7% | — |
| SWE-bench Verified (bash only) | — | 55.4% |
| SciCode | 53.5% | — |
| GSO | — | 4.9% |
| AlgoTune | — | 1.44 |
Agentic & Tool Use Qwen3-Coder 480B-A35B Instruct leads
Kimi K2.6: 21.9 (#137), Qwen3-Coder 480B-A35B Instruct: 23.9 (#123)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| Terminal-Bench | — | 27.2% |
| 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), Qwen3-Coder 480B-A35B Instruct: 25.5 (#149)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1470 | 1372 |
| Kagi LLM Benchmark | — | 49.5% |
| NYT Connections (extended) | 87.2% | — |
| CritPt | 8% | — |
| Chess Puzzles | 26% | — |
| EBR-Bench | 2.4% | — |
| Mystery Game Puzzles | 18% | — |
| DTBench | 90.9% | — |
| LMCA | 37.3% | — |
| Epoch Capabilities Index | 151.05 | — |
Math Kimi K2.6 leads
Kimi K2.6: 57.0 (#41), Qwen3-Coder 480B-A35B Instruct: 37.6 (#150)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Math | 1475 | 1365 |
| 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% | — |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge Kimi K2.6 leads
Kimi K2.6: 54.0 (#54), Qwen3-Coder 480B-A35B Instruct: 37.0 (#162)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Expert | 1491 | 1338 |
| GPQA Diamond | 90.8% | — |
| SimpleQA Verified | 34.9% | — |
| Vectara Hallucination Rate | 10.8% | — |
Multimodal Not comparable
Kimi K2.6: 31.6 (#103), Qwen3-Coder 480B-A35B Instruct: —
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| 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), Qwen3-Coder 480B-A35B Instruct: 47.7 (#148)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Non-English | 1446 | 1346 |
| LMArena Chinese | 1521 | 1357 |
| LMArena French | 1471 | 1398 |
| LMArena German | 1450 | 1325 |
| LMArena Japanese | 1443 | 1310 |
| LMArena Korean | 1427 | 1305 |
| LMArena Russian | 1446 | 1366 |
| LMArena Spanish | 1464 | 1360 |
Instruction Following Kimi K2.6 leads
Kimi K2.6: 76.3 (#43), Qwen3-Coder 480B-A35B Instruct: 71.6 (#147)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Instruction Following | 1451 | 1355 |
Long Context Kimi K2.6 leads
Kimi K2.6: 44.9 (#52), Qwen3-Coder 480B-A35B Instruct: 42.0 (#131)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Longer Query | 1468 | 1378 |
Writing & Preference Kimi K2.6 leads
Kimi K2.6: 68.5 (#26), Qwen3-Coder 480B-A35B Instruct: 55.3 (#147)
| Benchmark | Kimi K2.6 | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|
| LMArena Text | 1455 | 1357 |
| LMArena Creative Writing | 1434 | 1333 |
| LMArena Multi-Turn | 1453 | 1365 |
| EQ-Bench Creative Writing | 1725 | — |
| EQ-Bench 4 | 1202 | — |
Frequently asked questions
Is Kimi K2.6 better than Qwen3-Coder 480B-A35B Instruct?
Kimi K2.6 is the stronger model overall, scoring 47.7 to 38.1 on the Noometry Index.
Which is cheaper, Kimi K2.6 or Qwen3-Coder 480B-A35B Instruct?
Kimi K2.6 is cheaper. It lists at $0.95 per million input tokens and $4 per million output tokens; Qwen3-Coder 480B-A35B Instruct lists at $1.50 and $7.50.
Is Kimi K2.6 or Qwen3-Coder 480B-A35B Instruct better for coding?
Kimi K2.6 scores higher on coding benchmarks: 50.7 versus 35.5 in the Noometry coding category.
Which has the bigger context window?
Both accept 262K tokens.
How many benchmarks do Kimi K2.6 and Qwen3-Coder 480B-A35B Instruct share?
20 benchmarks have published results for both models. Kimi K2.6 has 51 scored results on Noometry and Qwen3-Coder 480B-A35B Instruct has 25.