DeepSeek-R1-0528 vs Gemma 4 31B
Gemma 4 31B leads the LLM Stats Score 33.1 to 24.1. Gemma 4 31B is 6.0x cheaper per token.
DeepSeek · Google · Updated for 2026
Which is better?
Gemma 4 31B leads the overall LLM Stats Score 33.1 to 24.1, ranking #107 overall.
In the 3 individual benchmarks reported for both models, Gemma 4 31B wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Gemma 4 31B is roughly 6.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemma 4 31B 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-R1-0528
- you want predictable pricing at $0.50/M input and $2.15/M output
Choose Gemma 4 31B
- overall performance matters — it scores 33.1 and ranks #107 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- cost matters — it's about 6.0x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Apr 2026
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for DeepSeek-R1-0528 · 12 for Gemma 4 31B
DeepSeek-R1-0528 outperforms in 0 benchmarks, while Gemma 4 31B is better at 3 benchmarks (GPQA, Humanity's Last Exam, MMLU-Pro).
Gemma 4 31B 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-R1-0528 ($0.50/1M tokens) is 5.6x more expensive than Gemma 4 31B ($0.09/1M tokens).
For output processing, DeepSeek-R1-0528 ($2.15/1M tokens) is 6.3x more expensive than Gemma 4 31B ($0.34/1M tokens).
In conclusion, DeepSeek-R1-0528 is more expensive than Gemma 4 31B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1-0528 has 640.3B more parameters than Gemma 4 31B, making it 2085.7% larger.
Context Window
Maximum input and output token capacity
Gemma 4 31B accepts 262,144 input tokens compared to DeepSeek-R1-0528's 163,840 tokens. Gemma 4 31B can generate longer responses up to 262,144 tokens, while DeepSeek-R1-0528 is limited to 163,840 tokens.
Input capabilities
Documented input modalities across available providers
Gemma 4 31B supports multimodal inputs, whereas DeepSeek-R1-0528 does not.
Gemma 4 31B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-R1-0528
Gemma 4 31B
License
Usage and distribution terms
DeepSeek-R1-0528 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-R1-0528 was released on 2025-05-28, while Gemma 4 31B was released on 2026-04-02.
Gemma 4 31B is 10 months newer than DeepSeek-R1-0528.
May 28, 2025
1.3 years ago
Apr 2, 2026
5 months ago
10mo newerKnowledge Cutoff
When training data ends
Gemma 4 31B has a documented knowledge cutoff of 2025-01-01, while DeepSeek-R1-0528'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-R1-0528's cutoff date.
—
Jan 2025
Provider Availability
DeepSeek-R1-0528 is available from DeepInfra, DeepSeek, Novita. Gemma 4 31B is available from DeepInfra, FriendliAI, Novita, Together.
DeepSeek-R1-0528
Gemma 4 31B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-R1-0528 and Gemma 4 31B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-R1-0528 vs Gemma 4 31B.