DeepSeek-V3 vs Kimi K3
Kimi K3 leads the LLM Stats Score 53.1 to 15.7. DeepSeek-V3 is 13.4x cheaper per token.
DeepSeek · Moonshot AI · Updated for 2026
Which is better?
Kimi K3 leads the overall LLM Stats Score 53.1 to 15.7, ranking #8 overall.
In the 1 individual benchmarks reported for both models, Kimi K3 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V3 is roughly 13.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K3 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-V3
- cost matters — it's about 13.4x cheaper per token
Choose Kimi K3
- overall performance matters — it scores 53.1 and ranks #8 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
20 reported for DeepSeek-V3 · 31 for Kimi K3
DeepSeek-V3 outperforms in 0 benchmarks, while Kimi K3 is better at 1 benchmark (GPQA).
Kimi K3 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V3 ($0.27/1M tokens) is 10.6x cheaper than Kimi K3 ($2.85/1M tokens).
For output processing, DeepSeek-V3 ($0.89/1M tokens) is 16.0x cheaper than Kimi K3 ($14.25/1M tokens).
In conclusion, Kimi K3 is more expensive than DeepSeek-V3.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K3 has 2129.0B more parameters than DeepSeek-V3, making it 317.3% larger.
Context Window
Maximum input and output token capacity
Kimi K3 accepts 1,048,576 input tokens compared to DeepSeek-V3's 131,072 tokens. Kimi K3 can generate longer responses up to 1,048,576 tokens, while DeepSeek-V3 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K3 supports multimodal inputs, whereas DeepSeek-V3 does not.
Kimi K3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V3
Kimi K3
License
Usage and distribution terms
DeepSeek-V3 is licensed under MIT + Model License (Commercial use allowed), while Kimi K3 uses Kimi K3 License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT + Model License (Commercial use allowed)
Open weights
Kimi K3 License
Open weights
Release Timeline
When each model was launched
DeepSeek-V3 was released on 2024-12-25, while Kimi K3 was released on 2026-07-16.
Kimi K3 is 19 months newer than DeepSeek-V3.
Dec 25, 2024
1.7 years ago
Jul 16, 2026
1 months ago
1.6yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V3 is available from DeepSeek, DeepInfra. Kimi K3 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
DeepSeek-V3
Kimi K3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3 and Kimi K3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3 vs Kimi K3.