Model comparison
Kimi K2.7 Code vs Qwen3.5-9B
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 33.8 on the Noometry Index. Qwen3.5-9B costs 15× less per token, which makes it the better buy when Kimi K2.7 Code's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. Kimi K2.7 Code scores higher in 5 categories and Qwen3.5-9B in 0 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.7 Code leads 52.9 to 34.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 95.6% for Kimi K2.7 Code and 61.7% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
Side by side
| Kimi K2.7 Code | Qwen3.5-9B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 43.3 | 33.8 |
| Released | 2026-06-12 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 262K | 262K |
| Max output | 262K | 66K |
| Input $ / M tokens | $0.95 | $0.10 |
| Output $ / M tokens | $4 | $0.15 |
| Results tracked | 19 | 10 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Kimi K2.7 Code leads
Kimi K2.7 Code: 42.9 (#95), Qwen3.5-9B: 35.9 (#217)
| Benchmark | Kimi K2.7 Code | Qwen3.5-9B |
|---|---|---|
| SciCode | 47.5% | 27.5% |
| DeepSWE | 30.5% | — |
| FrontierCode | 30.1% | — |
| LMArena WebDev | 1473 | — |
| WeirdML | 54.1% | — |
| ALE-Bench | 886.23 | — |
Agentic & Tool Use Kimi K2.7 Code leads
Kimi K2.7 Code: 24.0 (#122), Qwen3.5-9B: 14.5 (#151)
| Benchmark | Kimi K2.7 Code | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| APEX-Agents | 37.6% | — |
| GBAEval | 0.9% | — |
| Vending-Bench 2 | 5,083 | — |
Reasoning Kimi K2.7 Code leads
Kimi K2.7 Code: 39.0 (#61), Qwen3.5-9B: 23.1 (#182)
| Benchmark | Kimi K2.7 Code | Qwen3.5-9B |
|---|---|---|
| CritPt | 10% | 0.3% |
| Chess Puzzles | 21% | 12% |
| Epoch Capabilities Index | 149.97 | 139.46 |
| SimpleBench | 57.9% | — |
| DTBench | — | 71.2% |
| LMCA | — | 24.5% |
| Surface Evolver Bench | 48.8% | — |
Math Kimi K2.7 Code leads
Kimi K2.7 Code: 52.9 (#48), Qwen3.5-9B: 34.8 (#192)
| Benchmark | Kimi K2.7 Code | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 95.6% | 61.7% |
| FrontierMath (Tiers 1-3) | 54% | — |
| FrontierMath Tier 4 | 12.2% | — |
| MathArena Final-Answer Competitions | — | 48.5% |
Knowledge Kimi K2.7 Code leads
Kimi K2.7 Code: 53.5 (#57), Qwen3.5-9B: 46.0 (#84)
| Benchmark | Kimi K2.7 Code | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 87.9% | 79% |
| SimpleQA Verified | 36.5% | — |
Frequently asked questions
Is Kimi K2.7 Code better than Qwen3.5-9B?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 33.8 on the Noometry Index. Qwen3.5-9B costs 15× 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 Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is Kimi K2.7 Code or Qwen3.5-9B better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 35.9 in the Noometry coding category.
Which has the bigger context window?
Both accept 262K tokens.
How many benchmarks do Kimi K2.7 Code and Qwen3.5-9B share?
6 benchmarks have published results for both models. Kimi K2.7 Code has 19 scored results on Noometry and Qwen3.5-9B has 10.