Compare · ModelsLive · 2 picked · head to head
DeepSeek V4 Pro vs Kimi K2.7 Code
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
DeepSeek V4 Pro wins on 7/11 benchmarks
DeepSeek V4 Pro wins 7 of 11 shared benchmarks. Leads in speed · reasoning · language.
Category leads
speed·DeepSeek V4 Procoding·Kimi K2.7 Codereasoning·DeepSeek V4 Prolanguage·DeepSeek V4 Promath·DeepSeek V4 Proknowledge·DeepSeek V4 Pro
Hype vs Reality
Attention vs performance
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
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
DeepSeek
$3.4B·Tier 1
moonshotai
private · undisclosed
Head to head
11 benchmarks · 2 models
DeepSeek V4 ProKimi K2.7 Code
Artificial Analysis · Agentic Index
DeepSeek V4 Pro leads by +6.8
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?"
DeepSeek V4 Pro
36.4
Kimi K2.7 Code
29.6
Artificial Analysis · Coding Index
Kimi K2.7 Code leads by +1.4
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.
DeepSeek V4 Pro
59.4
Kimi K2.7 Code
60.8
Artificial Analysis · Quality Index
DeepSeek V4 Pro leads by +2.3
DeepSeek V4 Pro
44.3
Kimi K2.7 Code
42.0
LiveBench · Agentic Coding
Kimi K2.7 Code leads by +13.3
DeepSeek V4 Pro
56.7
Kimi K2.7 Code
70.0
LiveBench · Coding
Kimi K2.7 Code leads by +4.0
DeepSeek V4 Pro
70.0
Kimi K2.7 Code
74.0
LiveBench · Data Analysis
DeepSeek V4 Pro leads by +11.9
DeepSeek V4 Pro
74.5
Kimi K2.7 Code
62.7
LiveBench · If
DeepSeek V4 Pro leads by +6.1
DeepSeek V4 Pro
62.4
Kimi K2.7 Code
56.3
LiveBench · Language
DeepSeek V4 Pro leads by +0.2
DeepSeek V4 Pro
78.1
Kimi K2.7 Code
77.9
LiveBench · Mathematics
DeepSeek V4 Pro leads by +11.1
DeepSeek V4 Pro
90.7
Kimi K2.7 Code
79.6
LiveBench · Overall
DeepSeek V4 Pro leads by +1.7
DeepSeek V4 Pro
73.6
Kimi K2.7 Code
71.9
LiveBench · Reasoning
Kimi K2.7 Code leads by +0.1
DeepSeek V4 Pro
82.7
Kimi K2.7 Code
82.8
Full benchmark table
| Benchmark | 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?" | 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. | 59.4 | 60.8 |
Artificial Analysis · Quality Index | 44.3 | 42.0 |
LiveBench · Agentic Coding | 56.7 | 70.0 |
LiveBench · Coding | 70.0 | 74.0 |
LiveBench · Data Analysis | 74.5 | 62.7 |
LiveBench · If | 62.4 | 56.3 |
LiveBench · Language | 78.1 | 77.9 |
LiveBench · Mathematics | 90.7 | 79.6 |
LiveBench · Overall | 73.6 | 71.9 |
LiveBench · Reasoning | 82.7 | 82.8 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.43 | $0.87 | 1.0M tokens (~524 books) | $5.44 | |
| $0.61 | $3.07 | 262K tokens (~131 books) | $12.26 |