Model comparison
Kimi K2.7 Code vs Qwen3 14B
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 35.5 on the Noometry Index. Qwen3 14B costs 2.8× 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 4 categories and Qwen3 14B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Kimi K2.7 Code leads 39.0 to 18.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 95.6% for Kimi K2.7 Code and 66.4% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $0.95 / $4 for Kimi K2.7 Code.
- Kimi K2.7 Code accepts more context: 262K tokens versus 131K.
Side by side
| Kimi K2.7 Code | Qwen3 14B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 43.3 | 35.5 |
| Released | 2026-06-12 | 2025-04 |
| Weights | Open | Open |
| Context window | 262K | 131K |
| Max output | 262K | 8K |
| Input $ / M tokens | $0.95 | $0.35 |
| Output $ / M tokens | $4 | $1.40 |
| Results tracked | 19 | 12 |
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Category by category
Coding Kimi K2.7 Code leads
Kimi K2.7 Code: 42.9 (#95), Qwen3 14B: 37.3 (#195)
| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| SciCode | 47.5% | 31.6% |
| DeepSWE | 30.5% | — |
| FrontierCode | 30.1% | — |
| LMArena WebDev | 1473 | — |
| WeirdML | 54.1% | — |
| ALE-Bench | 886.23 | — |
Agentic & Tool Use Qwen3 14B leads
Kimi K2.7 Code: 24.0 (#122), Qwen3 14B: 29.6 (#83)
| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| APEX-Agents | 37.6% | — |
| Berkeley Function Calling Leaderboard | — | 41% |
| GBAEval | 0.9% | — |
| Vending-Bench 2 | 5,083 | — |
Reasoning Kimi K2.7 Code leads
Kimi K2.7 Code: 39.0 (#61), Qwen3 14B: 18.5 (#280)
| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| CritPt | 10% | 0% |
| Chess Puzzles | 21% | 4% |
| Epoch Capabilities Index | 149.97 | 138.23 |
| SimpleBench | 57.9% | — |
| Kagi LLM Benchmark | — | 49.1% |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| Surface Evolver Bench | 48.8% | — |
Math Kimi K2.7 Code leads
Kimi K2.7 Code: 52.9 (#48), Qwen3 14B: 38.6 (#133)
| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 95.6% | 66.4% |
| FrontierMath (Tiers 1-3) | 54% | — |
| FrontierMath Tier 4 | 12.2% | — |
Knowledge Kimi K2.7 Code leads
Kimi K2.7 Code: 53.5 (#57), Qwen3 14B: 39.3 (#134)
| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 87.9% | 63.8% |
| SimpleQA Verified | 36.5% | — |
| Vectara Hallucination Rate | — | 5.4% |
Long Context Not comparable
Kimi K2.7 Code: —, Qwen3 14B: 38.1 (#204)
| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
Frequently asked questions
Is Kimi K2.7 Code better than Qwen3 14B?
Kimi K2.7 Code is the stronger model overall, scoring 43.3 to 35.5 on the Noometry Index. Qwen3 14B costs 2.8× 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 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; Kimi K2.7 Code lists at $0.95 and $4.
Is Kimi K2.7 Code or Qwen3 14B better for coding?
Kimi K2.7 Code scores higher on coding benchmarks: 42.9 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.7 Code does, with 262K tokens against 131K.
How many benchmarks do Kimi K2.7 Code and Qwen3 14B share?
6 benchmarks have published results for both models. Kimi K2.7 Code has 19 scored results on Noometry and Qwen3 14B has 12.