DeepSeek-V4-Pro-0813 vs Kimi K2.7 Code
DeepSeek-V4-Pro-0813 leads the LLM Stats Score 54.1 to 39.6. DeepSeek-V4-Pro-0813 is 2.6x cheaper per token.
DeepSeek · Moonshot AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 leads the overall LLM Stats Score 54.1 to 39.6, ranking #7 overall.
On price, DeepSeek-V4-Pro-0813 is roughly 2.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- overall performance matters — it scores 54.1 and ranks #7 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 2.6x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Kimi K2.7 Code
- you want predictable pricing at $0.74/M input and $3.50/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
12 reported for DeepSeek-V4-Pro-0813 · 9 for Kimi K2.7 Code
DeepSeek-V4-Pro-0813 and Kimi K2.7 Codedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 1.7x cheaper than Kimi K2.7 Code ($0.74/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 4.0x cheaper than Kimi K2.7 Code ($3.50/1M tokens).
In conclusion, Kimi K2.7 Code is more expensive than DeepSeek-V4-Pro-0813.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 600.0B more parameters than Kimi K2.7 Code, making it 60.0% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Kimi K2.7 Code's 262,144 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Kimi K2.7 Code is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K2.7 Code supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Kimi K2.7 Code can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Kimi K2.7 Code
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 is licensed under MIT, while Kimi K2.7 Code uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Kimi K2.7 Code was released on 2026-06-12.
DeepSeek-V4-Pro-0813 is 2 months newer than Kimi K2.7 Code.
Aug 13, 2026
2 weeks ago
2mo newerJun 12, 2026
2 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Kimi K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
DeepSeek-V4-Pro-0813
Kimi K2.7 Code
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Kimi K2.7 Code side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Kimi K2.7 Code.