Model comparison
Kimi K2.7 Code vs Qwen3.5 122B-A10B
Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 41.6 on the Noometry Index. Qwen3.5 122B-A10B costs 1.6× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Last verified . 3 shared benchmarks.
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. Kimi K2.7 Code scores higher in 4 categories and Qwen3.5 122B-A10B in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.7 Code leads 52.6 to 36.4.
- The biggest single-benchmark swing is SciCode: 47.5% for Kimi K2.7 Code and 35.6% for Qwen3.5 122B-A10B.
- Qwen3.5 122B-A10B is cheaper at $0.40 / $3.20 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
Side by side
| Kimi K2.7 Code | Qwen3.5 122B-A10B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 43.0 | 41.6 |
| Released | 2026-06-12 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 262K | 262K |
| Max output | 262K | 66K |
| Input $ / M tokens | $0.95 | $0.40 |
| Output $ / M tokens | $4 | $3.20 |
| Results tracked | 19 | 27 |
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Category by category
Coding Kimi K2.7 Code leads
Kimi K2.7 Code: 42.4 (#92), Qwen3.5 122B-A10B: 38.8 (#155)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena WebDev | 1473 | 1360 |
| SciCode | 47.5% | 35.6% |
| DeepSWE | 30.5% | — |
| FrontierCode | 30.1% | — |
| WeirdML | 54.1% | — |
| LMArena Coding | — | 1436 |
| ALE-Bench | 886.23 | — |
Agentic & Tool Use Not comparable
Kimi K2.7 Code: 23.9 (#115), Qwen3.5 122B-A10B: —
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| APEX-Agents | 37.6% | — |
| GBAEval | 0.9% | — |
| Vending-Bench 2 | 5,083 | — |
Reasoning Kimi K2.7 Code leads
Kimi K2.7 Code: 38.2 (#56), Qwen3.5 122B-A10B: 26.4 (#121)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| CritPt | 10% | 0.9% |
| SimpleBench | 57.9% | — |
| NYT Connections (extended) | — | 51.7% |
| Chess Puzzles | 21% | — |
| Thematic Generalization | — | 51.2% |
| LMArena Hard Prompts | — | 1421 |
| Mystery Game Puzzles | — | 17% |
| DTBench | — | 84.3% |
| LMCA | — | 32.2% |
| Surface Evolver Bench | 48.8% | — |
| Epoch Capabilities Index | 150.01 | — |
Math Kimi K2.7 Code leads
Kimi K2.7 Code: 52.6 (#39), Qwen3.5 122B-A10B: 36.4 (#114)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 54% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 95.6% | — |
| LMArena Math | — | 1432 |
Knowledge Kimi K2.7 Code leads
Kimi K2.7 Code: 53.6 (#51), Qwen3.5 122B-A10B: 38.2 (#138)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| GPQA Diamond | 87.9% | — |
| SimpleQA Verified | 36.5% | — |
| Vectara Hallucination Rate | — | 11.2% |
| LMArena Expert | — | 1431 |
Multimodal Not comparable
Kimi K2.7 Code: —, Qwen3.5 122B-A10B: 39.0 (#52)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Vision | — | 1245 |
Multilingual Not comparable
Kimi K2.7 Code: —, Qwen3.5 122B-A10B: 51.8 (#101)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Non-English | — | 1400 |
| LMArena Chinese | — | 1462 |
| LMArena French | — | 1442 |
| LMArena German | — | 1425 |
| LMArena Japanese | — | 1364 |
| LMArena Korean | — | 1353 |
| LMArena Russian | — | 1400 |
| LMArena Spanish | — | 1423 |
Instruction Following Not comparable
Kimi K2.7 Code: —, Qwen3.5 122B-A10B: 74.0 (#109)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Instruction Following | — | 1399 |
Long Context Not comparable
Kimi K2.7 Code: —, Qwen3.5 122B-A10B: 43.0 (#105)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Longer Query | — | 1410 |
Writing & Preference Not comparable
Kimi K2.7 Code: —, Qwen3.5 122B-A10B: 60.2 (#98)
| Benchmark | Kimi K2.7 Code | Qwen3.5 122B-A10B |
|---|---|---|
| LMArena Text | — | 1417 |
| LMArena Creative Writing | — | 1369 |
| LMArena Multi-Turn | — | 1416 |
Frequently asked questions
Is Kimi K2.7 Code better than Qwen3.5 122B-A10B?
Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 41.6 on the Noometry Index. Qwen3.5 122B-A10B costs 1.6× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Which is cheaper, Kimi K2.7 Code 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 K2.7 Code lists at $0.95 and $4.
Is Kimi K2.7 Code or Qwen3.5 122B-A10B better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.4 versus 38.8 in the Noometry coding category.
Which has the bigger context window?
Both accept 262K tokens.
How many benchmarks do Kimi K2.7 Code and Qwen3.5 122B-A10B share?
3 benchmarks have published results for both models. Kimi K2.7 Code has 19 scored results on Noometry and Qwen3.5 122B-A10B has 27.