Model comparison
Kimi K2 (Jul 2025) vs Qwen3 14B
Kimi K2 (Jul 2025) is the stronger model overall, scoring 41.2 to 35.5 on the Noometry Index. Qwen3 14B costs 1.6× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.
Last verified . 5 shared benchmarks.
Alibaba (Qwen)Summary
- They share 5 benchmarks with published results for both. Kimi K2 (Jul 2025) scores higher in 5 categories and Qwen3 14B in 1 category; 6 gaps are clear of the uncertainty.
- The widest gap is in coding, where Kimi K2 (Jul 2025) leads 42.4 to 37.3.
- The biggest single-benchmark swing is Berkeley Function Calling Leaderboard: 59.1% for Kimi K2 (Jul 2025) and 41% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $0.57 / $2.30 for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 131K.
Side by side
| Kimi K2 (Jul 2025) | Qwen3 14B | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 41.2 | 35.5 |
| Released | 2025-07-12 | 2025-04 |
| Weights | Open | Open |
| Context window | 262K | 131K |
| Max output | 262K | 8K |
| Input $ / M tokens | $0.57 | $0.35 |
| Output $ / M tokens | $2.30 | $1.40 |
| Results tracked | 42 | 12 |
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Category by category
Coding Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 42.4 (#102), Qwen3 14B: 37.3 (#195)
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| SWE-bench Verified (bash only) | 63.4% | — |
| Aider Polyglot | 59.1% | — |
| SciCode | — | 31.6% |
| GSO | 4.9% | — |
| WeirdML | 42.8% | — |
| LMArena Coding | 1399 | — |
| ALE-Bench | 597.5 | — |
Agentic & Tool Use Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 32.4 (#64), Qwen3 14B: 29.6 (#83)
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 59.1% | 41% |
| Terminal-Bench | 35.7% | — |
| METR Time Horizons | 59.2% | — |
Reasoning Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 23.3 (#179), Qwen3 14B: 18.5 (#280)
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | 64.4% | 49.1% |
| Epoch Capabilities Index | 146.01 | 138.23 |
| SimpleBench | 26.3% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
| LMArena Hard Prompts | 1384 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| ForecastBench | 60.2 | — |
Math Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 42.7 (#83), Qwen3 14B: 38.6 (#133)
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |
| Omni-MATH | 65.4% | — |
| LMArena Math | 1397 | — |
| FrontierMath (Feb 2025 set) | 21.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Qwen3 14B leads
Kimi K2 (Jul 2025): 37.3 (#157), Qwen3 14B: 39.3 (#134)
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| Vectara Hallucination Rate | 17.9% | 5.4% |
| GPQA Diamond | — | 63.8% |
| MMLU-Pro | 81.9% | — |
| Confabulations | 20.4% | — |
| GPQA (HELM) | 65.3% | — |
| LMArena Expert | 1365 | — |
Multilingual Not comparable
Kimi K2 (Jul 2025): 49.6 (#130), Qwen3 14B: —
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1372 | — |
| LMArena Chinese | 1415 | — |
| LMArena French | 1379 | — |
| LMArena German | 1387 | — |
| LMArena Japanese | 1349 | — |
| LMArena Korean | 1325 | — |
| LMArena Russian | 1385 | — |
| LMArena Spanish | 1386 | — |
Instruction Following Not comparable
Kimi K2 (Jul 2025): 71.1 (#156), Qwen3 14B: —
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| IFEval | 85% | — |
| LMArena Instruction Following | 1348 | — |
Long Context Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 41.2 (#145), Qwen3 14B: 38.1 (#204)
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | 66.7% | 62.5% |
| CL-bench | 17.6% | — |
| LMArena Longer Query | 1353 | — |
Writing & Preference Not comparable
Kimi K2 (Jul 2025): 62.3 (#78), Qwen3 14B: —
| Benchmark | Kimi K2 (Jul 2025) | Qwen3 14B |
|---|---|---|
| LMArena Text | 1380 | — |
| LMArena Creative Writing | 1350 | — |
| Short-Story Creative Writing | 85.6% | — |
| EQ-Bench Creative Writing | 1666 | — |
| WildBench | 86.2% | — |
| LMArena Multi-Turn | 1371 | — |
Frequently asked questions
Is Kimi K2 (Jul 2025) better than Qwen3 14B?
Kimi K2 (Jul 2025) is the stronger model overall, scoring 41.2 to 35.5 on the Noometry Index. Qwen3 14B costs 1.6× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.
Which is cheaper, Kimi K2 (Jul 2025) 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 (Jul 2025) lists at $0.57 and $2.30.
Is Kimi K2 (Jul 2025) or Qwen3 14B better for coding?
Kimi K2 (Jul 2025) scores higher on coding benchmarks: 42.4 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
Kimi K2 (Jul 2025) does, with 262K tokens against 131K.
How many benchmarks do Kimi K2 (Jul 2025) and Qwen3 14B share?
5 benchmarks have published results for both models. Kimi K2 (Jul 2025) has 42 scored results on Noometry and Qwen3 14B has 12.