Compare · ModelsLive · 2 picked · head to head
Kimi K2.7 Code vs Qwen3.7 Max
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Qwen3.7 Max wins on 15/18 benchmarks
Qwen3.7 Max wins 15 of 18 shared benchmarks. Leads in speed · knowledge · math.
Category leads
speed·Qwen3.7 Maxknowledge·Qwen3.7 Maxmath·Qwen3.7 Maxcoding·Kimi K2.7 Codereasoning·Qwen3.7 Maxlanguage·Qwen3.7 Max
Hype vs Reality
Attention vs performance
Kimi K2.7 Code
#69 by perf·#17 by attention
Qwen3.7 Max
#41 by perf·#2 by attention
Best value
Kimi K2.7 Code
1.2x better value than Qwen3.7 Max
Kimi K2.7 Code
30.5 pts/$
$1.84/M
Qwen3.7 Max
24.5 pts/$
$2.50/M
Vendor risk
Who is behind the model
Moonshot AI
$18.0B·Tier 1
Alibaba (Qwen)
$293.0B·Tier 1
Head to head
18 benchmarks · 2 models
Kimi K2.7 CodeQwen3.7 Max
Artificial Analysis · Agentic Index
Qwen3.7 Max leads by +1.0
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 K2.7 Code
29.6
Qwen3.7 Max
30.6
Artificial Analysis · Coding Index
Qwen3.7 Max leads by +5.2
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 K2.7 Code
60.8
Qwen3.7 Max
66.0
Artificial Analysis · Quality Index
Qwen3.7 Max leads by +20.2
Kimi K2.7 Code
25.8
Qwen3.7 Max
46.0
Chess Puzzles
Kimi K2.7 Code leads by +2.1
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Kimi K2.7 Code
16.9
Qwen3.7 Max
14.8
FrontierMath-Tier-4-v2-Private
Qwen3.7 Max leads by +21.9
Kimi K2.7 Code
12.2
Qwen3.7 Max
34.1
FrontierMath-Tiers-1-3-v2-Private
Qwen3.7 Max leads by +10.5
Kimi K2.7 Code
54.0
Qwen3.7 Max
64.6
GPQA diamond
Qwen3.7 Max leads by +4.0
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Kimi K2.7 Code
83.8
Qwen3.7 Max
87.9
LiveBench · Agentic Coding
Kimi K2.7 Code leads by +18.3
Kimi K2.7 Code
70.0
Qwen3.7 Max
51.7
LiveBench · Coding
Qwen3.7 Max leads by +0.3
Kimi K2.7 Code
74.0
Qwen3.7 Max
74.2
LiveBench · Data Analysis
Qwen3.7 Max leads by +9.1
Kimi K2.7 Code
62.7
Qwen3.7 Max
71.8
LiveBench · If
Qwen3.7 Max leads by +17.8
Kimi K2.7 Code
56.3
Qwen3.7 Max
74.0
LiveBench · Language
Qwen3.7 Max leads by +1.8
Kimi K2.7 Code
77.9
Qwen3.7 Max
79.7
LiveBench · Mathematics
Qwen3.7 Max leads by +5.7
Kimi K2.7 Code
79.6
Qwen3.7 Max
85.3
LiveBench · Overall
Qwen3.7 Max leads by +2.4
Kimi K2.7 Code
71.9
Qwen3.7 Max
74.3
LiveBench · Reasoning
Qwen3.7 Max leads by +0.5
Kimi K2.7 Code
82.8
Qwen3.7 Max
83.3
OTIS Mock AIME 2024-2025
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Kimi K2.7 Code
95.5
Qwen3.7 Max
95.5
SimpleBench
Qwen3.7 Max leads by +15.0
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Kimi K2.7 Code
49.5
Qwen3.7 Max
64.5
SimpleQA Verified
Qwen3.7 Max leads by +19.3
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Kimi K2.7 Code
36.5
Qwen3.7 Max
55.8
Full benchmark table
| Benchmark | Kimi K2.7 Code | Qwen3.7 Max |
|---|---|---|
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?" | 29.6 | 30.6 |
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. | 60.8 | 66.0 |
Artificial Analysis · Quality Index | 25.8 | 46.0 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 16.9 | 14.8 |
FrontierMath-Tier-4-v2-Private | 12.2 | 34.1 |
FrontierMath-Tiers-1-3-v2-Private | 54.0 | 64.6 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 83.8 | 87.9 |
LiveBench · Agentic Coding | 70.0 | 51.7 |
LiveBench · Coding | 74.0 | 74.2 |
LiveBench · Data Analysis | 62.7 | 71.8 |
LiveBench · If | 56.3 | 74.0 |
LiveBench · Language | 77.9 | 79.7 |
LiveBench · Mathematics | 79.6 | 85.3 |
LiveBench · Overall | 71.9 | 74.3 |
LiveBench · Reasoning | 82.8 | 83.3 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 95.5 | 95.5 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 49.5 | 64.5 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 36.5 | 55.8 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.61 | $3.07 | 262K tokens (~131 books) | $12.26 | |
| $1.25 | $3.75 | 1.0M tokens (~500 books) | $18.75 |
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