DeepSeek-V3 0324 vs Kimi K2 0905
Kimi K2 0905 leads the LLM Stats Score 21.6 to 13.4. DeepSeek-V3 0324 is 2.7x cheaper per token.
DeepSeek · Moonshot AI · Updated for 2026
Which is better?
Kimi K2 0905 leads the overall LLM Stats Score 21.6 to 13.4, ranking #188 overall.
In the 3 individual benchmarks reported for both models, Kimi K2 0905 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V3 0324 is roughly 2.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2 0905 also accepts a larger context window (262,144 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 0324
- cost matters — it's about 2.7x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Kimi K2 0905
- overall performance matters — it scores 21.6 and ranks #188 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
5 reported for DeepSeek-V3 0324 · 6 for Kimi K2 0905
DeepSeek-V3 0324 outperforms in 0 benchmarks, while Kimi K2 0905 is better at 3 benchmarks (AIME 2024, GPQA, MMLU-Pro).
Kimi K2 0905 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 0324 ($0.24/1M tokens) is 2.5x cheaper than Kimi K2 0905 ($0.60/1M tokens).
For output processing, DeepSeek-V3 0324 ($0.90/1M tokens) is 2.8x cheaper than Kimi K2 0905 ($2.50/1M tokens).
In conclusion, Kimi K2 0905 is more expensive than DeepSeek-V3 0324.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2 0905 has 329.0B more parameters than DeepSeek-V3 0324, making it 49.0% larger.
Context Window
Maximum input and output token capacity
Kimi K2 0905 accepts 262,144 input tokens compared to DeepSeek-V3 0324's 163,840 tokens. Kimi K2 0905 can generate longer responses up to 262,144 tokens, while DeepSeek-V3 0324 is limited to 163,840 tokens.
License
Usage and distribution terms
DeepSeek-V3 0324 is licensed under MIT + Model License (Commercial use allowed), while Kimi K2 0905 uses a proprietary 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
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V3 0324 was released on 2025-03-25, while Kimi K2 0905 was released on 2025-09-05.
Kimi K2 0905 is 5 months newer than DeepSeek-V3 0324.
Mar 25, 2025
1.5 years ago
Sep 5, 2025
1.0 years ago
5mo 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 0324 is available from DeepInfra, Novita. Kimi K2 0905 is available from Novita.
DeepSeek-V3 0324
Kimi K2 0905
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V3 0324 and Kimi K2 0905 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V3 0324 vs Kimi K2 0905.