Model Comparison
DeepSeek-V4-Flash-Max vs Gemini 3.5 Flash-LiteWhich is better in 2026?
Both models are evenly matched across the benchmarks. DeepSeek-V4-Flash-Max is 6.8x cheaper per token.
Verdict: DeepSeek-V4-Flash-Max vs Gemini 3.5 Flash-Lite — which is better?
DeepSeek-V4-Flash-Max (by DeepSeek) and Gemini 3.5 Flash-Lite (by Google) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
DeepSeek-V4-Flash-Max outperforms in 1 benchmarks (GDPval-AA), while Gemini 3.5 Flash-Lite is better at 1 benchmark (SWE-Bench Pro). Both models are evenly matched across the benchmarks.
On price, DeepSeek-V4-Flash-Max is roughly 6.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Choose DeepSeek-V4-Flash-Max if…
- cost matters — it's about 6.8x cheaper per token
- you need open weights you can self-host or fine-tune
Choose Gemini 3.5 Flash-Lite if…
- you want the most recent training data — it shipped Jul 2026
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-Max outperforms in 1 benchmarks (GDPval-AA), while Gemini 3.5 Flash-Lite is better at 1 benchmark (SWE-Bench Pro).
Both models are evenly matched across the benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-Max ($0.10/1M tokens) is 3.0x cheaper than Gemini 3.5 Flash-Lite ($0.30/1M tokens).
For output processing, DeepSeek-V4-Flash-Max ($0.20/1M tokens) is 12.5x cheaper than Gemini 3.5 Flash-Lite ($2.50/1M tokens).
In conclusion, Gemini 3.5 Flash-Lite is more expensive than DeepSeek-V4-Flash-Max.*
* Using a 3:1 ratio of input to output tokens
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 65,536 tokens.
Input Capabilities
Supported data types and modalities
Gemini 3.5 Flash-Lite supports multimodal inputs, whereas DeepSeek-V4-Flash-Max does not.
Gemini 3.5 Flash-Lite can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-Max
Gemini 3.5 Flash-Lite
License
Usage and distribution terms
DeepSeek-V4-Flash-Max is licensed under MIT, while Gemini 3.5 Flash-Lite uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
DeepSeek-V4-Flash-Max was released on 2026-04-23, while Gemini 3.5 Flash-Lite was released on 2026-07-21.
Gemini 3.5 Flash-Lite is 3 months newer than DeepSeek-V4-Flash-Max.
Apr 23, 2026
3 months ago
Jul 21, 2026
2 days ago
2mo newerKnowledge Cutoff
When training data ends
Gemini 3.5 Flash-Lite has a documented knowledge cutoff of 2026-03-31, while DeepSeek-V4-Flash-Max's cutoff date is not specified.
We can confirm Gemini 3.5 Flash-Lite's training data extends to 2026-03-31, but cannot make a direct comparison without DeepSeek-V4-Flash-Max's cutoff date.
—
Mar 2026
Provider Availability
DeepSeek-V4-Flash-Max is available from DeepInfra, DeepSeek, Fireworks, Novita. Gemini 3.5 Flash-Lite is available from Google.
DeepSeek-V4-Flash-Max
Gemini 3.5 Flash-Lite
Outputs Comparison
Key Takeaways
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-Max and Gemini 3.5 Flash-Lite side-by-side, then vote on the output you prefer.
| Feature | | |
|---|
FAQ
Common questions about DeepSeek-V4-Flash-Max vs Gemini 3.5 Flash-Lite.