# Kimi K2.7 Code vs Qwen3 14B

> Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 35.0 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.

- Canonical page: https://noometry.com/compare/kimi-k2-7-code-vs-qwen3-14b
- Last updated: 2026-10-10
- Shared benchmarks: 6

## 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 38.2 to 18.1.
- 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.

## Snapshot

| | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 43.0 | 35.0 |
| Rank | 90 | 222 |
| Context | 262K | 131K |
| Input $/M | $0.95 | $0.35 |
| Output $/M | $4 | $1.40 |
| Weights | Open | Open |

## Coding

- Kimi K2.7 Code: 42.4 (#92)
- Qwen3 14B: 36.7 (#191)

| 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

- Kimi K2.7 Code: 23.9 (#115)
- Qwen3 14B: 29.4 (#78)

| 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: 38.2 (#56)
- Qwen3 14B: 18.1 (#272)

| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| CritPt | 10% | 0% |
| Chess Puzzles | 21% | 4% |
| Epoch Capabilities Index | 150.01 | 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: 52.6 (#39)
- Qwen3 14B: 37.0 (#102)

| 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: 53.6 (#51)
- Qwen3 14B: 39.2 (#124)

| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| GPQA Diamond | 87.9% | 63.8% |
| SimpleQA Verified | 36.5% | — |
| Vectara Hallucination Rate | — | 5.4% |

## Long Context

- Kimi K2.7 Code: —
- Qwen3 14B: 38.0 (#201)

| Benchmark | Kimi K2.7 Code | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |

## FAQ

### Is Kimi K2.7 Code better than Qwen3 14B?

Kimi K2.7 Code is the stronger model overall, scoring 43.0 to 35.0 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: 42.4 versus 36.7 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.