Model comparison
Kimi K2.5 vs Qwen2.5 7B Instruct
Kimi K2.5 is the stronger model overall, scoring 48.1 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 2.9× less per token, which makes it the better buy when Kimi K2.5's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. Kimi K2.5 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 K2.5 leads 51.8 to 12.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 92.2% for Kimi K2.5 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 $0.45 / $2.25 for Kimi K2.5.
- Kimi K2.5 accepts more context: 262K tokens versus 131K.
Side by side
| Kimi K2.5 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Moonshot AI | Alibaba (Qwen) |
| Noometry Index | 48.1 | 29.0 |
| Released | 2026-01-27 | 2024-09 |
| Weights | Open | Open |
| Context window | 262K | 131K |
| Max output | 262K | 8K |
| Input $ / M tokens | $0.45 | $0.17 |
| Output $ / M tokens | $2.25 | $0.70 |
| Results tracked | 51 | 15 |
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Category by category
Coding Kimi K2.5 leads
Kimi K2.5: 48.8 (#53), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified | 73.8% | — |
| SWE-bench Verified (bash only) | 70.8% | — |
| LMArena WebDev | 1437 | — |
| SWE-bench Multilingual | 67.3% | — |
| SciCode | 49% | — |
| WeirdML | 45.6% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1474 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 821.65 | — |
Agentic & Tool Use Kimi K2.5 leads
Kimi K2.5: 34.2 (#48), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| Terminal-Bench | 43.2% | — |
| OSWorld | 63.3% | — |
| BALROG | — | 7.8% |
| Vending-Bench 2 | 1,198 | — |
Reasoning Kimi K2.5 leads
Kimi K2.5: 31.2 (#80), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 12% | 0% |
| Epoch Capabilities Index | 148.03 | 118.51 |
| ARC-AGI-2 | 11.8% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 78.5% | — |
| NYT Connections (extended) | 69.9% | — |
| ARC-AGI-1 | 65.3% | — |
| CritPt | 3.1% | — |
| EnigmaEval | 3.4% | — |
| Thematic Generalization | 69.4% | — |
| LMArena Hard Prompts | 1453 | — |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
Math Kimi K2.5 leads
Kimi K2.5: 51.8 (#53), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 92.2% | 2.5% |
| MathArena Final-Answer Competitions | 62.3% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1470 | — |
| FrontierMath (Feb 2025 set) | 27.9% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Kimi K2.5 leads
Kimi K2.5: 53.6 (#56), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 87.6% | 35.5% |
| Humanity's Last Exam | 24.4% | — |
| SimpleQA Verified | 34.3% | — |
| MMLU-Pro | — | 53.9% |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1466 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
Kimi K2.5: 41.1 (#39), Qwen2.5 7B Instruct: —
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1269 | — |
| LMArena Document | 1430 | — |
Multilingual Not comparable
Kimi K2.5: 53.9 (#53), Qwen2.5 7B Instruct: —
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1433 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1454 | — |
| LMArena German | 1441 | — |
| LMArena Japanese | 1421 | — |
| LMArena Korean | 1410 | — |
| LMArena Russian | 1435 | — |
| LMArena Spanish | 1450 | — |
Instruction Following Kimi K2.5 leads
Kimi K2.5: 75.3 (#64), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1431 | — |
Long Context Not comparable
Kimi K2.5: 52.1 (#7), Qwen2.5 7B Instruct: —
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| Fiction.LiveBench | 86.1% | — |
| CL-bench | 19.3% | — |
| CL-bench Life | 13.2% | — |
| LMArena Longer Query | 1445 | — |
Writing & Preference Kimi K2.5 leads
Kimi K2.5: 65.1 (#53), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | Kimi K2.5 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1445 | — |
| LMArena Creative Writing | 1423 | — |
| EQ-Bench Creative Writing | 1579 | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1444 | — |
Frequently asked questions
Is Kimi K2.5 better than Qwen2.5 7B Instruct?
Kimi K2.5 is the stronger model overall, scoring 48.1 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 2.9× less per token, which makes it the better buy when Kimi K2.5's lead doesn't matter for your workload.
Which is cheaper, Kimi K2.5 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 K2.5 lists at $0.45 and $2.25.
Is Kimi K2.5 or Qwen2.5 7B Instruct better for coding?
Kimi K2.5 scores higher on coding benchmarks: 48.8 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.5 does, with 262K tokens against 131K.
How many benchmarks do Kimi K2.5 and Qwen2.5 7B Instruct share?
4 benchmarks have published results for both models. Kimi K2.5 has 51 scored results on Noometry and Qwen2.5 7B Instruct has 15.