Model comparison
Kimi K2.7 Code vs Qwen3.5 27B
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 41.9 on the Noometry Index. Qwen3.5 27B costs 2.1× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. Kimi K2.7 Code scores higher in 4 categories and Qwen3.5 27B in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Kimi K2.7 Code leads 53.5 to 38.0.
- The biggest single-benchmark swing is WeirdML: 54.1% for Kimi K2.7 Code and 39.5% for Qwen3.5 27B.
- Qwen3.5 27B is cheaper at $0.30 / $2.40 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
Side by side
| Kimi K2.7 Code | Qwen3.5 27B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 43.3 | 41.9 |
| Released | 2026-06-12 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 262K | 262K |
| Max output | 262K | 66K |
| Input $ / M tokens | $0.95 | $0.30 |
| Output $ / M tokens | $4 | $2.40 |
| Results tracked | 19 | 28 |
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Category by category
Coding Kimi K2.7 Code leads
Kimi K2.7 Code: 42.9 (#95), Qwen3.5 27B: 38.9 (#168)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| LMArena WebDev | 1473 | 1358 |
| WeirdML | 54.1% | 39.5% |
| ALE-Bench | 886.23 | 349.45 |
| DeepSWE | 30.5% | — |
| FrontierCode | 30.1% | — |
| SciCode | 47.5% | — |
| LMArena Coding | — | 1427 |
Agentic & Tool Use Not comparable
Kimi K2.7 Code: 24.0 (#122), Qwen3.5 27B: —
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| Vending-Bench 2 | 5,083 | 201.98 |
| APEX-Agents | 37.6% | — |
| GBAEval | 0.9% | — |
Reasoning Kimi K2.7 Code leads
Kimi K2.7 Code: 39.0 (#61), Qwen3.5 27B: 27.5 (#117)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| SimpleBench | 57.9% | — |
| NYT Connections (extended) | — | 47.9% |
| CritPt | 10% | — |
| Chess Puzzles | 21% | — |
| Thematic Generalization | — | 45.5% |
| LMArena Hard Prompts | — | 1414 |
| DTBench | — | 82.4% |
| LMCA | — | 34% |
| Surface Evolver Bench | 48.8% | — |
| Epoch Capabilities Index | 149.97 | — |
Math Kimi K2.7 Code leads
Kimi K2.7 Code: 52.9 (#48), Qwen3.5 27B: 38.8 (#127)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 54% | — |
| FrontierMath Tier 4 | 12.2% | — |
| MathArena Final-Answer Competitions | — | 56.7% |
| OTIS Mock AIME 2024-2025 | 95.6% | — |
| LMArena Math | — | 1429 |
Knowledge Kimi K2.7 Code leads
Kimi K2.7 Code: 53.5 (#57), Qwen3.5 27B: 38.0 (#150)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| GPQA Diamond | 87.9% | — |
| SimpleQA Verified | 36.5% | — |
| Vectara Hallucination Rate | — | 12.1% |
| LMArena Expert | — | 1428 |
Multimodal Not comparable
Kimi K2.7 Code: —, Qwen3.5 27B: 39.4 (#59)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | — | 1241 |
Multilingual Not comparable
Kimi K2.7 Code: —, Qwen3.5 27B: 50.8 (#115)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | — | 1390 |
| LMArena Chinese | — | 1478 |
| LMArena French | — | 1410 |
| LMArena German | — | 1393 |
| LMArena Japanese | — | 1345 |
| LMArena Korean | — | 1358 |
| LMArena Russian | — | 1390 |
| LMArena Spanish | — | 1407 |
Instruction Following Not comparable
Kimi K2.7 Code: —, Qwen3.5 27B: 73.5 (#119)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | — | 1393 |
Long Context Not comparable
Kimi K2.7 Code: —, Qwen3.5 27B: 43.1 (#106)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | — | 1413 |
Writing & Preference Not comparable
Kimi K2.7 Code: —, Qwen3.5 27B: 59.3 (#111)
| Benchmark | Kimi K2.7 Code | Qwen3.5 27B |
|---|---|---|
| LMArena Text | — | 1409 |
| LMArena Creative Writing | — | 1362 |
| LMArena Multi-Turn | — | 1410 |
Frequently asked questions
Is Kimi K2.7 Code better than Qwen3.5 27B?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 41.9 on the Noometry Index. Qwen3.5 27B costs 2.1× 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 27B?
Qwen3.5 27B is cheaper. It lists at $0.30 per million input tokens and $2.40 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is Kimi K2.7 Code or Qwen3.5 27B better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 38.9 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 27B share?
4 benchmarks have published results for both models. Kimi K2.7 Code has 19 scored results on Noometry and Qwen3.5 27B has 28.