Model comparison
Kimi K2 (Jul 2025) vs Qwen2.5 72B Instruct
Kimi K2 (Jul 2025) is the stronger model overall, scoring 41.2 to 31.9 on the Noometry Index.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. Kimi K2 (Jul 2025) scores higher in 9 categories and Qwen2.5 72B Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2 (Jul 2025) leads 42.7 to 19.3.
- The biggest single-benchmark swing is Omni-MATH: 65.4% for Kimi K2 (Jul 2025) and 33% for Qwen2.5 72B Instruct.
- Kimi K2 (Jul 2025) is cheaper at $0.57 / $2.30 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 131K.
Side by side
| Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 41.2 | 31.9 |
| Released | 2025-07-12 | 2024-09 |
| Weights | Open | Open |
| Context window | 262K | 131K |
| Max output | 262K | 8K |
| Input $ / M tokens | $0.57 | $1.40 |
| Output $ / M tokens | $2.30 | $5.60 |
| Results tracked | 42 | 43 |
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Category by category
Coding Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 42.4 (#102), Qwen2.5 72B Instruct: 33.2 (#260)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| WeirdML | 42.8% | 16% |
| LMArena Coding | 1399 | 1292 |
| SWE-bench Verified (bash only) | 63.4% | — |
| Aider Polyglot | 59.1% | — |
| GSO | 4.9% | — |
| BigCodeBench Instruct | — | 45.8% |
| BigCodeBench Complete | — | 55.9% |
| ALE-Bench | 597.5 | — |
Agentic & Tool Use Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 32.4 (#64), Qwen2.5 72B Instruct: 22.1 (#133)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| METR Time Horizons | 59.2% | 35.8% |
| Terminal-Bench | 35.7% | — |
| Berkeley Function Calling Leaderboard | 59.1% | — |
| TheAgentCompany | — | 5.7% |
| BALROG | — | 16.2% |
Reasoning Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 23.3 (#179), Qwen2.5 72B Instruct: 22.3 (#199)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1384 | 1271 |
| Epoch Capabilities Index | 146.01 | 129 |
| ForecastBench | 60.2 | 57.5 |
| SimpleBench | 26.3% | — |
| Kagi LLM Benchmark | 64.4% | — |
| DTBench | — | 62.9% |
| LMCA | — | 13.4% |
| BIG-Bench Hard | — | 79.8% |
| HellaSwag | — | 84.8% |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |
Math Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 42.7 (#83), Qwen2.5 72B Instruct: 19.3 (#287)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| Omni-MATH | 65.4% | 33% |
| LMArena Math | 1397 | 1283 |
| OTIS Mock AIME 2024-2025 | — | 8.1% |
| MATH Level 5 | — | 63.2% |
| FrontierMath (Feb 2025 set) | 21.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 37.3 (#157), Qwen2.5 72B Instruct: 27.0 (#253)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| MMLU-Pro | 81.9% | 63.1% |
| Confabulations | 20.4% | 19.1% |
| GPQA (HELM) | 65.3% | 42.6% |
| LMArena Expert | 1365 | 1245 |
| GPQA Diamond | — | 49.1% |
| Vectara Hallucination Rate | 17.9% | — |
| ARC (AI2) Challenge | — | 94.5% |
| MMLU | — | 85.3% |
| TriviaQA | — | 71.9% |
Multilingual Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 49.6 (#130), Qwen2.5 72B Instruct: 41.0 (#213)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1372 | 1252 |
| LMArena Chinese | 1415 | 1272 |
| LMArena French | 1379 | 1280 |
| LMArena German | 1387 | 1234 |
| LMArena Japanese | 1349 | 1180 |
| LMArena Korean | 1325 | 1188 |
| LMArena Russian | 1385 | 1264 |
| LMArena Spanish | 1386 | 1256 |
Instruction Following Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 71.1 (#156), Qwen2.5 72B Instruct: 65.5 (#221)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| IFEval | 85% | 80.6% |
| LMArena Instruction Following | 1348 | 1254 |
Long Context Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 41.2 (#145), Qwen2.5 72B Instruct: 38.9 (#188)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1353 | 1282 |
| Fiction.LiveBench | 66.7% | — |
| CL-bench | 17.6% | — |
Writing & Preference Kimi K2 (Jul 2025) leads
Kimi K2 (Jul 2025): 62.3 (#78), Qwen2.5 72B Instruct: 46.7 (#215)
| Benchmark | Kimi K2 (Jul 2025) | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1380 | 1269 |
| LMArena Creative Writing | 1350 | 1221 |
| WildBench | 86.2% | 80.2% |
| LMArena Multi-Turn | 1371 | 1272 |
| Short-Story Creative Writing | 85.6% | — |
| EQ-Bench Creative Writing | 1666 | — |
Frequently asked questions
Is Kimi K2 (Jul 2025) better than Qwen2.5 72B Instruct?
Kimi K2 (Jul 2025) is the stronger model overall, scoring 41.2 to 31.9 on the Noometry Index.
Which is cheaper, Kimi K2 (Jul 2025) or Qwen2.5 72B Instruct?
Kimi K2 (Jul 2025) is cheaper. It lists at $0.57 per million input tokens and $2.30 per million output tokens; Qwen2.5 72B Instruct lists at $1.40 and $5.60.
Is Kimi K2 (Jul 2025) or Qwen2.5 72B Instruct better for coding?
Kimi K2 (Jul 2025) scores higher on coding benchmarks: 42.4 versus 33.2 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 Qwen2.5 72B Instruct share?
27 benchmarks have published results for both models. Kimi K2 (Jul 2025) has 42 scored results on Noometry and Qwen2.5 72B Instruct has 43.