DeepSeek-V4-Flash-0731 vs Kimi K2.7 Code
DeepSeek-V4-Flash-0731 and Kimi K2.7 Code are closely matched at 46.1 and 39.6 on the LLM Stats Score. DeepSeek-V4-Flash-0731 is 12.7x cheaper per token.
DeepSeek · Moonshot AI · Updated for 2026
Which is better?
DeepSeek-V4-Flash-0731 and Kimi K2.7 Code are closely matched on the overall LLM Stats Score at 46.1 and 39.6.
On price, DeepSeek-V4-Flash-0731 is roughly 12.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 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-V4-Flash-0731
- cost matters — it's about 12.7x 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 Jul 2026
Choose Kimi K2.7 Code
- you want predictable pricing at $0.74/M input and $3.50/M output
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 · 9 for Kimi K2.7 Code
DeepSeek-V4-Flash-0731 and Kimi K2.7 Codedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 ($0.09/1M tokens) is 8.2x cheaper than Kimi K2.7 Code ($0.74/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 19.4x cheaper than Kimi K2.7 Code ($3.50/1M tokens).
In conclusion, Kimi K2.7 Code is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.7 Code has 696.0B more parameters than DeepSeek-V4-Flash-0731, making it 228.9% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Kimi K2.7 Code's 262,144 tokens. DeepSeek-V4-Flash-0731 can generate longer responses up to 384,000 tokens, while Kimi K2.7 Code is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K2.7 Code supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Kimi K2.7 Code can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Kimi K2.7 Code
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Kimi K2.7 Code uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Kimi K2.7 Code was released on 2026-06-12.
DeepSeek-V4-Flash-0731 is 2 months newer than Kimi K2.7 Code.
Jul 31, 2026
4 weeks ago
1mo newerJun 12, 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 K2.7 Code is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
DeepSeek-V4-Flash-0731
Kimi K2.7 Code
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Kimi K2.7 Code side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Kimi K2.7 Code.