Model comparison
Kimi K3 vs Qwen2.5 7B Instruct
Kimi K3 is the stronger model overall, scoring 59.5 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 20× less per token, which makes it the better buy when Kimi K3'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 K3 scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K3 leads 74.2 to 12.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.2% for Kimi K3 and 2.5% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $3 / $15 for Kimi K3.
- Kimi K3 accepts more context: 1.05M tokens versus 131K.
Side by side
| Kimi K3 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 59.5 | 29.0 |
| Released | 2026-07-16 | 2024-09 |
| Weights | Open | Open |
| Context window | 1.05M | 131K |
| Max output | 1.05M | 8K |
| Input $ / M tokens | $3 | $0.17 |
| Output $ / M tokens | $15 | $0.70 |
| Results tracked | 53 | 15 |
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Category by category
Coding Kimi K3 leads
Kimi K3: 61.0 (#10), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| DeepSWE | 68.5% | — |
| FrontierCode | 44.2% | — |
| LMArena WebDev | 1654 | — |
| FrontierSWE | 25.9% | — |
| SciCode | 59.5% | — |
| WeirdML | 82.6% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1508 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 1,524 | — |
Agentic & Tool Use Kimi K3 leads
Kimi K3: 41.8 (#20), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| APEX-Agents | 50.6% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 32% | — |
| BALROG | — | 7.8% |
| GBAEval | 48.3% | — |
| GDP.pdf | 19% | — |
| Vending-Bench 2 | 5,165 | — |
Reasoning Kimi K3 leads
Kimi K3: 63.0 (#17), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 39% | 0% |
| DTBench | 91.2% | 47.7% |
| LMCA | 52.7% | 6.4% |
| Epoch Capabilities Index | 157.45 | 118.51 |
| ARC-AGI-2 | 60.4% | — |
| SimpleBench | 60.7% | — |
| NYT Connections (extended) | 93.6% | — |
| ARC-AGI-1 | 94.5% | — |
| CritPt | 23.4% | — |
| LMArena Hard Prompts | 1496 | — |
| Mystery Game Puzzles | 26% | — |
| Surface Evolver Bench | 95% | — |
| ForecastBench | 61.1 | — |
Math Kimi K3 leads
Kimi K3: 74.2 (#16), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.2% | 2.5% |
| FrontierMath (Tiers 1-3) | 72.2% | — |
| FrontierMath Tier 4 | 39% | — |
| MathArena Final-Answer Competitions | 87.8% | — |
| ProofBench | 87% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1491 | — |
Knowledge Kimi K3 leads
Kimi K3: 63.2 (#21), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 93.1% | 35.5% |
| SimpleQA Verified | 50.6% | — |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1521 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
Kimi K3: 37.8 (#70), Qwen2.5 7B Instruct: —
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| Blueprint-Bench 2 | 29.5% | — |
| Furniture Assembly | 34.2% | — |
Multilingual Not comparable
Kimi K3: 56.3 (#21), Qwen2.5 7B Instruct: —
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1466 | — |
| LMArena Chinese | 1529 | — |
| LMArena French | 1491 | — |
| LMArena German | 1488 | — |
| LMArena Japanese | 1487 | — |
| LMArena Korean | 1458 | — |
| LMArena Russian | 1482 | — |
| LMArena Spanish | 1472 | — |
Instruction Following Kimi K3 leads
Kimi K3: 77.7 (#14), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1483 | — |
Long Context Not comparable
Kimi K3: 45.8 (#29), Qwen2.5 7B Instruct: —
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1494 | — |
Writing & Preference Kimi K3 leads
Kimi K3: 76.6 (#4), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | Kimi K3 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1476 | — |
| LMArena Creative Writing | 1454 | — |
| EQ-Bench Creative Writing | 2082 | — |
| WildBench | — | 73.1% |
| EQ-Bench 4 | 1339 | — |
| LMArena Multi-Turn | 1488 | — |
Frequently asked questions
Is Kimi K3 better than Qwen2.5 7B Instruct?
Kimi K3 is the stronger model overall, scoring 59.5 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 20× less per token, which makes it the better buy when Kimi K3's lead doesn't matter for your workload.
Which is cheaper, Kimi K3 or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; Kimi K3 lists at $3 and $15.
Is Kimi K3 or Qwen2.5 7B Instruct better for coding?
Kimi K3 scores higher on coding benchmarks: 61.0 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
Kimi K3 does, with 1.05M tokens against 131K.
How many benchmarks do Kimi K3 and Qwen2.5 7B Instruct share?
6 benchmarks have published results for both models. Kimi K3 has 53 scored results on Noometry and Qwen2.5 7B Instruct has 15.