Compare · ModelsLive · 3 picked · head to head
Qwen3.7 Max vs DeepSeek V4 Pro vs Kimi K2.7 Code
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Qwen3.7 Max wins on 12/17 benchmarks
Qwen3.7 Max wins 12 of 17 shared benchmarks. Leads in speed · language · math.
Category leads
speed·Qwen3.7 Maxcoding·Kimi K2.7 Codereasoning·DeepSeek V4 Prolanguage·Qwen3.7 Maxmath·Qwen3.7 Maxknowledge·Qwen3.7 Max
Hype vs Reality
Attention vs performance
Qwen3.7 Max
#28 by perf·no signal
DeepSeek V4 Pro
#13 by perf·no signal
Kimi K2.7 Code
#42 by perf·no signal
Best value
DeepSeek V4 Pro
3.3x better value than Kimi K2.7 Code
Qwen3.7 Max
27.2 pts/$
$2.50/M
DeepSeek V4 Pro
112.8 pts/$
$0.65/M
Kimi K2.7 Code
34.3 pts/$
$1.84/M
Vendor risk
Mixed exposure
One or more vendors flagged
Alibaba (Qwen)
$293.0B·Tier 1
DeepSeek
$3.4B·Tier 1
moonshotai
private · undisclosed
Head to head
17 benchmarks · 3 models
Qwen3.7 MaxDeepSeek V4 ProKimi K2.7 Code
Artificial Analysis · Agentic Index
DeepSeek V4 Pro leads by +5.8
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?"
Qwen3.7 Max
30.6
DeepSeek V4 Pro
36.4
Kimi K2.7 Code
29.6
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.
Qwen3.7 Max
66.0
DeepSeek V4 Pro
59.4
Kimi K2.7 Code
60.8
Artificial Analysis · Quality Index
Qwen3.7 Max leads by +1.7
Qwen3.7 Max
46.0
DeepSeek V4 Pro
44.3
Kimi K2.7 Code
42.0
LiveBench · Agentic Coding
Kimi K2.7 Code leads by +13.3
Qwen3.7 Max
51.7
DeepSeek V4 Pro
56.7
Kimi K2.7 Code
70.0
LiveBench · Coding
Qwen3.7 Max leads by +0.3
Qwen3.7 Max
74.2
DeepSeek V4 Pro
70.0
Kimi K2.7 Code
74.0
LiveBench · Data Analysis
DeepSeek V4 Pro leads by +2.8
Qwen3.7 Max
71.8
DeepSeek V4 Pro
74.5
Kimi K2.7 Code
62.7
LiveBench · If
Qwen3.7 Max leads by +11.7
Qwen3.7 Max
74.0
DeepSeek V4 Pro
62.4
Kimi K2.7 Code
56.3
LiveBench · Language
Qwen3.7 Max leads by +1.6
Qwen3.7 Max
79.7
DeepSeek V4 Pro
78.1
Kimi K2.7 Code
77.9
LiveBench · Mathematics
DeepSeek V4 Pro leads by +5.4
Qwen3.7 Max
85.3
DeepSeek V4 Pro
90.7
Kimi K2.7 Code
79.6
LiveBench · Overall
Qwen3.7 Max leads by +0.7
Qwen3.7 Max
74.3
DeepSeek V4 Pro
73.6
Kimi K2.7 Code
71.9
LiveBench · Reasoning
Qwen3.7 Max leads by +0.5
Qwen3.7 Max
83.3
DeepSeek V4 Pro
82.7
Kimi K2.7 Code
82.8
Chess Puzzles
Qwen3.7 Max leads by +1.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Qwen3.7 Max
22.0
Kimi K2.7 Code
21.0
FrontierMath-Tier-4-v2-Private
Qwen3.7 Max leads by +21.9
Qwen3.7 Max
34.1
Kimi K2.7 Code
12.2
FrontierMath-Tiers-1-3-v2-Private
Qwen3.7 Max leads by +10.5
Qwen3.7 Max
64.6
Kimi K2.7 Code
54.0
GPQA diamond
Qwen3.7 Max leads by +2.8
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Qwen3.7 Max
88.8
Kimi K2.7 Code
86.0
OTIS Mock AIME 2024-2025
Kimi K2.7 Code leads by +1.4
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Qwen3.7 Max
95.0
Kimi K2.7 Code
96.4
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.
Qwen3.7 Max
58.5
Kimi K2.7 Code
39.2
Full benchmark table
| Benchmark | Qwen3.7 Max | DeepSeek V4 Pro | Kimi K2.7 Code |
|---|---|---|---|
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?" | 30.6 | 36.4 | 29.6 |
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. | 66.0 | 59.4 | 60.8 |
Artificial Analysis · Quality Index | 46.0 | 44.3 | 42.0 |
LiveBench · Agentic Coding | 51.7 | 56.7 | 70.0 |
LiveBench · Coding | 74.2 | 70.0 | 74.0 |
LiveBench · Data Analysis | 71.8 | 74.5 | 62.7 |
LiveBench · If | 74.0 | 62.4 | 56.3 |
LiveBench · Language | 79.7 | 78.1 | 77.9 |
LiveBench · Mathematics | 85.3 | 90.7 | 79.6 |
LiveBench · Overall | 74.3 | 73.6 | 71.9 |
LiveBench · Reasoning | 83.3 | 82.7 | 82.8 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 22.0 | — | 21.0 |
FrontierMath-Tier-4-v2-Private | 34.1 | — | 12.2 |
FrontierMath-Tiers-1-3-v2-Private | 64.6 | — | 54.0 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 88.8 | — | 86.0 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 95.0 | — | 96.4 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 58.5 | — | 39.2 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.25 | $3.75 | 1.0M tokens (~500 books) | $18.75 | |
| $0.43 | $0.87 | 1.0M tokens (~524 books) | $5.44 | |
| $0.61 | $3.07 | 262K tokens (~131 books) | $12.26 |