DeepSeek-V4-Flash-0731 vs Kimi K3
Kimi K3 leads the LLM Stats Score 53.0 to 44.7. DeepSeek-V4-Flash-0731 is 63.3x cheaper per token.
DeepSeek · Moonshot AI · Updated for 2026
Which is better?
Kimi K3 leads the overall LLM Stats Score 53.0 to 44.7, ranking #8 overall.
In the 4 individual benchmarks reported for both models, Kimi K3 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Flash-0731 is roughly 63.3x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Flash-0731
- cost matters — it's about 63.3x cheaper per token
- you want the most recent training data — it shipped Jul 2026
Choose Kimi K3
- overall performance matters — it scores 53.0 and ranks #8 on LLM Stats
- your work emphasizes coding and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
9 reported for DeepSeek-V4-Flash-0731 · 31 for Kimi K3
DeepSeek-V4-Flash-0731 outperforms in 0 benchmarks, while Kimi K3 is better at 4 benchmarks (AutomationBench, DeepSWE, Terminal-Bench 2.1, Toolathlon).
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-V4-Flash-0731 ($0.06/1M tokens) is 47.5x cheaper than Kimi K3 ($2.85/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 79.2x cheaper than Kimi K3 ($14.25/1M tokens).
In conclusion, Kimi K3 is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K3 has 2496.0B more parameters than DeepSeek-V4-Flash-0731, making it 821.1% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. Both models can generate responses up to 1,048,576 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K3 supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Kimi K3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Kimi K3
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Kimi K3 uses Kimi K3 License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Kimi K3 License
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Kimi K3 was released on 2026-07-16.
DeepSeek-V4-Flash-0731 is 1 month newer than Kimi K3.
Jul 31, 2026
1 months ago
2w newerJul 16, 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-Flash-0731 is available from DeepInfra, Novita, Fireworks. Kimi K3 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
DeepSeek-V4-Flash-0731
Kimi K3
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Kimi K3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Kimi K3.