Model comparison
Kimi K3 vs Qwen3.5 122B-A10B
Kimi K3 is the stronger model overall, scoring 59.5 to 42.1 on the Noometry Index. Qwen3.5 122B-A10B costs 5.5× less per token, which makes it the better buy when Kimi K3's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. Kimi K3 scores higher in 8 categories and Qwen3.5 122B-A10B in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K3 leads 63.0 to 27.2.
- The biggest single-benchmark swing is NYT Connections (extended): 93.6% for Kimi K3 and 51.7% for Qwen3.5 122B-A10B.
- Qwen3.5 122B-A10B is cheaper at $0.40 / $3.20 per million input/output tokens, against $3 / $15 for Kimi K3.
- Kimi K3 accepts more context: 1.05M tokens versus 262K.
Side by side
| Kimi K3 | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 59.5 | 42.1 |
| Released | 2026-07-16 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 1.05M | 262K |
| Max output | 1.05M | 66K |
| Input $ / M tokens | $3 | $0.40 |
| Output $ / M tokens | $15 | $3.20 |
| Results tracked | 53 | 27 |
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Category by category
Coding Kimi K3 leads
Kimi K3: 61.0 (#10), Qwen3.5 122B-A10B: 39.1 (#162)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena WebDev | 1654 | 1360 |
| SciCode | 59.5% | 35.6% |
| LMArena Coding | 1508 | 1436 |
| DeepSWE | 68.5% | — |
| FrontierCode | 44.2% | — |
| FrontierSWE | 25.9% | — |
| WeirdML | 82.6% | — |
| ALE-Bench | 1,524 | — |
Agentic & Tool Use Not comparable
Kimi K3: 41.8 (#20), Qwen3.5 122B-A10B: —
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| APEX-Agents | 50.6% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 32% | — |
| GBAEval | 48.3% | — |
| GDP.pdf | 19% | — |
| Vending-Bench 2 | 5,165 | — |
Reasoning Kimi K3 leads
Kimi K3: 63.0 (#17), Qwen3.5 122B-A10B: 27.2 (#123)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| NYT Connections (extended) | 93.6% | 51.7% |
| CritPt | 23.4% | 0.9% |
| LMArena Hard Prompts | 1496 | 1421 |
| Mystery Game Puzzles | 26% | 17% |
| DTBench | 91.2% | 84.3% |
| LMCA | 52.7% | 32.2% |
| ARC-AGI-2 | 60.4% | — |
| SimpleBench | 60.7% | — |
| ARC-AGI-1 | 94.5% | — |
| Chess Puzzles | 39% | — |
| Thematic Generalization | — | 51.2% |
| Surface Evolver Bench | 95% | — |
| Epoch Capabilities Index | 157.45 | — |
| ForecastBench | 61.1 | — |
Math Kimi K3 leads
Kimi K3: 74.2 (#16), Qwen3.5 122B-A10B: 39.1 (#112)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Math | 1491 | 1432 |
| FrontierMath (Tiers 1-3) | 72.2% | — |
| FrontierMath Tier 4 | 39% | — |
| MathArena Final-Answer Competitions | 87.8% | — |
| OTIS Mock AIME 2024-2025 | 97.2% | — |
| ProofBench | 87% | — |
Knowledge Kimi K3 leads
Kimi K3: 63.2 (#21), Qwen3.5 122B-A10B: 38.8 (#142)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Expert | 1521 | 1432 |
| GPQA Diamond | 93.1% | — |
| SimpleQA Verified | 50.6% | — |
| Vectara Hallucination Rate | — | 11.2% |
Multimodal Qwen3.5 122B-A10B leads
Kimi K3: 37.8 (#70), Qwen3.5 122B-A10B: 39.6 (#57)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | — | 1245 |
| Blueprint-Bench 2 | 29.5% | — |
| Furniture Assembly | 34.2% | — |
Multilingual Kimi K3 leads
Kimi K3: 56.3 (#21), Qwen3.5 122B-A10B: 51.6 (#107)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | 1466 | 1400 |
| LMArena Chinese | 1529 | 1462 |
| LMArena French | 1491 | 1442 |
| LMArena German | 1488 | 1426 |
| LMArena Japanese | 1487 | 1367 |
| LMArena Korean | 1458 | 1352 |
| LMArena Russian | 1482 | 1400 |
| LMArena Spanish | 1472 | 1424 |
Instruction Following Kimi K3 leads
Kimi K3: 77.7 (#14), Qwen3.5 122B-A10B: 73.8 (#115)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | 1483 | 1399 |
Long Context Kimi K3 leads
Kimi K3: 45.8 (#29), Qwen3.5 122B-A10B: 43.0 (#109)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Longer Query | 1494 | 1410 |
Writing & Preference Kimi K3 leads
Kimi K3: 76.6 (#4), Qwen3.5 122B-A10B: 60.0 (#105)
| Benchmark | Kimi K3 | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | 1476 | 1417 |
| LMArena Creative Writing | 1454 | 1368 |
| LMArena Multi-Turn | 1488 | 1416 |
| EQ-Bench Creative Writing | 2082 | — |
| EQ-Bench 4 | 1339 | — |
Frequently asked questions
Is Kimi K3 better than Qwen3.5 122B-A10B?
Kimi K3 is the stronger model overall, scoring 59.5 to 42.1 on the Noometry Index. Qwen3.5 122B-A10B costs 5.5× less per token, which makes it the better buy when Kimi K3's lead doesn't matter for your workload.
Which is cheaper, Kimi K3 or Qwen3.5 122B-A10B?
Qwen3.5 122B-A10B is cheaper. It lists at $0.40 per million input tokens and $3.20 per million output tokens; Kimi K3 lists at $3 and $15.
Is Kimi K3 or Qwen3.5 122B-A10B better for coding?
Kimi K3 scores higher on coding benchmarks: 61.0 versus 39.1 in the Noometry coding category.
Which has the bigger context window?
Kimi K3 does, with 1.05M tokens against 262K.
How many benchmarks do Kimi K3 and Qwen3.5 122B-A10B share?
24 benchmarks have published results for both models. Kimi K3 has 53 scored results on Noometry and Qwen3.5 122B-A10B has 27.