DeepSeek-V4-Pro-0813 vs Gemma 4 31B
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks. Gemma 4 31B is 2.8x cheaper per token.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-V4-Pro-0813 outperforms in 1 benchmarks (Humanity's Last Exam), while Gemma 4 31B is better at 0 benchmarks. DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
On price, Gemma 4 31B is roughly 2.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Pro-0813 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 benchmark, pricing, and model metadata for 2026.
Choose DeepSeek-V4-Pro-0813
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Aug 2026
Choose Gemma 4 31B
- cost matters — it's about 2.8x cheaper per token
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Pro-0813 outperforms in 1 benchmarks (Humanity's Last Exam), while Gemma 4 31B is better at 0 benchmarks.
DeepSeek-V4-Pro-0813 significantly outperforms across most benchmarks.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Pro-0813 ($0.43/1M tokens) is 3.3x more expensive than Gemma 4 31B ($0.13/1M tokens).
For output processing, DeepSeek-V4-Pro-0813 ($0.87/1M tokens) is 2.3x more expensive than Gemma 4 31B ($0.38/1M tokens).
In conclusion, DeepSeek-V4-Pro-0813 is more expensive than Gemma 4 31B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-0813 has 1569.3B more parameters than Gemma 4 31B, making it 5111.7% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Pro-0813 accepts 1,048,576 input tokens compared to Gemma 4 31B's 262,144 tokens. DeepSeek-V4-Pro-0813 can generate longer responses up to 393,216 tokens, while Gemma 4 31B is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Gemma 4 31B supports multimodal inputs, whereas DeepSeek-V4-Pro-0813 does not.
Gemma 4 31B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Pro-0813
Gemma 4 31B
License
Usage and distribution terms
DeepSeek-V4-Pro-0813 is licensed under MIT, while Gemma 4 31B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Pro-0813 was released on 2026-08-13, while Gemma 4 31B was released on 2026-04-02.
DeepSeek-V4-Pro-0813 is 4 months newer than Gemma 4 31B.
Aug 13, 2026
1 weeks ago
4mo newerApr 2, 2026
4 months ago
Knowledge Cutoff
When training data ends
Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while DeepSeek-V4-Pro-0813's cutoff date is not specified.
We can confirm Gemma 4 31B's training data extends to 2025-01-01, but cannot make a direct comparison without DeepSeek-V4-Pro-0813's cutoff date.
—
Jan 2025
Provider Availability
DeepSeek-V4-Pro-0813 is available from DeepSeek, DeepInfra, Novita, Together. Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together.
DeepSeek-V4-Pro-0813
Gemma 4 31B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V4-Pro-0813 and Gemma 4 31B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V4-Pro-0813 vs Gemma 4 31B.