DeepSeek-V4-Pro-0813 vs Kimi K2.5
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks. DeepSeek-V4-Pro-0813 is 2.2x cheaper per token.
DeepSeek · Moonshot AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 outperforms in 2 benchmarks (CyberGym, Humanity's Last Exam), while Kimi K2.5 is better at 0 benchmarks. DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
On price, DeepSeek-V4-Pro-0813 is roughly 2.2x 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 benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
- cost matters — it's about 2.2x 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 K2.5
- you want predictable pricing at $0.60/M input and $3.00/M output
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 outperforms in 2 benchmarks (CyberGym, Humanity's Last Exam), while Kimi K2.5 is better at 0 benchmarks.
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 1.4x cheaper than Kimi K2.5 ($0.60/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 3.4x cheaper than Kimi K2.5 ($3.00/1M tokens).
In conclusion, Kimi K2.5 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 K2.5, 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 K2.5's 262,100 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Kimi K2.5 is limited to 262,100 tokens.
Input Capabilities
Supported data types and modalities
Kimi K2.5 supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Kimi K2.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Kimi K2.5
License
Usage and distribution terms
Both models are licensed under MIT.
Both models share the same licensing terms, providing consistent usage rights.
MIT
Open weights
MIT
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Kimi K2.5 was released on 2026-01-27.
DeepSeek-V4-Pro-0813 is 7 months newer than Kimi K2.5.
Aug 13, 2026
1 weeks ago
6mo newerJan 27, 2026
7 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 K2.5 is available from Fireworks, Moonshot AI.
DeepSeek-V4-Pro-0813
Kimi K2.5
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Kimi K2.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Kimi K2.5.