Compare · ModelsLive · 2 picked · head to head
Kimi K2.5 vs Qwen3.5 397B A17B
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Qwen3.5 397B A17B wins on 7/13 benchmarks
Qwen3.5 397B A17B wins 7 of 13 shared benchmarks. Leads in agentic · knowledge · math.
Category leads
speed·Kimi K2.5agentic·Qwen3.5 397B A17Bknowledge·Qwen3.5 397B A17Bmath·Qwen3.5 397B A17Blanguage·Kimi K2.5coding·Qwen3.5 397B A17B
Hype vs Reality
Attention vs performance
Kimi K2.5
#122 by perf·#17 by attention
Qwen3.5 397B A17B
#48 by perf·#2 by attention
Best value
Kimi K2.5
1.3x better value than Qwen3.5 397B A17B
Kimi K2.5
37.0 pts/$
$1.35/M
Qwen3.5 397B A17B
29.6 pts/$
$2.02/M
Vendor risk
Who is behind the model
Moonshot AI
$18.0B·Tier 1
Alibaba (Qwen)
$293.0B·Tier 1
Head to head
13 benchmarks · 2 models
Kimi K2.5Qwen3.5 397B A17B
Artificial Analysis · Agentic Index
Kimi K2.5 leads by +39.1
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
Kimi K2.5
58.9
Qwen3.5 397B A17B
19.9
Artificial Analysis · Coding Index
Qwen3.5 397B A17B leads by +8.7
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
Kimi K2.5
39.5
Qwen3.5 397B A17B
48.2
Artificial Analysis · Quality Index
Kimi K2.5 leads by +25.4
Kimi K2.5
46.8
Qwen3.5 397B A17B
21.4
APEX-Agents
Qwen3.5 397B A17B leads by +10.5
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Kimi K2.5
14.4
Qwen3.5 397B A17B
24.9
Chess Puzzles
Qwen3.5 397B A17B leads by +1.1
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Kimi K2.5
7.4
Qwen3.5 397B A17B
8.5
GPQA diamond
Kimi K2.5 leads by +1.7
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Kimi K2.5
83.5
Qwen3.5 397B A17B
81.8
OpenCompass · AIME2025
Qwen3.5 397B A17B leads by +0.4
Kimi K2.5
91.9
Qwen3.5 397B A17B
92.3
OpenCompass · GPQA-Diamond
Qwen3.5 397B A17B leads by +0.3
Kimi K2.5
88.1
Qwen3.5 397B A17B
88.4
OpenCompass · HLE
Kimi K2.5 leads by +1.1
Kimi K2.5
28.6
Qwen3.5 397B A17B
27.5
OpenCompass · IFEval
Kimi K2.5 leads by +2.4
Kimi K2.5
93.9
Qwen3.5 397B A17B
91.5
OpenCompass · LiveCodeBenchV6
Qwen3.5 397B A17B leads by +2.4
Kimi K2.5
80.6
Qwen3.5 397B A17B
83.0
OpenCompass · MMLU-Pro
Qwen3.5 397B A17B leads by +1.4
Kimi K2.5
86.2
Qwen3.5 397B A17B
87.6
OTIS Mock AIME 2024-2025
Kimi K2.5 leads by +3.3
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Kimi K2.5
92.2
Qwen3.5 397B A17B
88.9
Full benchmark table
| Benchmark | Kimi K2.5 | Qwen3.5 397B A17B |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 58.9 | 19.9 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 39.5 | 48.2 |
Artificial Analysis · Quality Index | 46.8 | 21.4 |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 14.4 | 24.9 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 7.4 | 8.5 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 83.5 | 81.8 |
OpenCompass · AIME2025 | 91.9 | 92.3 |
OpenCompass · GPQA-Diamond | 88.1 | 88.4 |
OpenCompass · HLE | 28.6 | 27.5 |
OpenCompass · IFEval | 93.9 | 91.5 |
OpenCompass · LiveCodeBenchV6 | 80.6 | 83.0 |
OpenCompass · MMLU-Pro | 86.2 | 87.6 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 92.2 | 88.9 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.45 | $2.25 | 262K tokens (~131 books) | $9.00 | |
| $0.55 | $3.50 | 262K tokens (~131 books) | $12.88 |