DeepSeek-V4-Pro-0813 vs Kimi K2 Instruct
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks. Kimi K2 Instruct is 1.1x cheaper per token.
DeepSeek · Moonshot AI · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 outperforms in 1 benchmarks (Humanity's Last Exam), while Kimi K2 Instruct is better at 0 benchmarks. DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
On price, Kimi K2 Instruct is roughly 1.1x 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 1 of 1 shared benchmarks
- 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 Instruct
- cost matters — it's about 1.1x cheaper per token
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 outperforms in 1 benchmarks (Humanity's Last Exam), while Kimi K2 Instruct 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 1.1x cheaper than Kimi K2 Instruct ($0.50/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 1.7x more expensive than Kimi K2 Instruct ($0.50/1M tokens).
In conclusion, DeepSeek-V4-Pro-0813 is more expensive than Kimi K2 Instruct.*
* 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 Instruct, 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 Instruct's 200,000 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Kimi K2 Instruct is limited to 200,000 tokens.
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 Instruct was released on 2025-07-11.
DeepSeek-V4-Pro-0813 is 13 months newer than Kimi K2 Instruct.
Aug 13, 2026
1 weeks ago
1.1yr newerJul 11, 2025
1.1 years 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 Instruct is available from Fireworks, Novita.
DeepSeek-V4-Pro-0813
Kimi K2 Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Kimi K2 Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Kimi K2 Instruct.