DeepSeek-V2.5 vs Gemma 3 4B
DeepSeek-V2.5 leads the LLM Stats Score 8.1 to -1.9. Gemma 3 4B is 7.0x cheaper per token.
DeepSeek · Google · Updated for 2026
Which is better?
DeepSeek-V2.5 leads the overall LLM Stats Score 8.1 to -1.9, ranking #281 overall.
In the 3 individual benchmarks reported for both models, DeepSeek-V2.5 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Gemma 3 4B is roughly 7.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemma 3 4B also accepts a larger context window (131,072 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-V2.5
- overall performance matters — it scores 8.1 and ranks #281 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
Choose Gemma 3 4B
- cost matters — it's about 7.0x cheaper per token
- you process long inputs — it offers a 131,072 token context window
- you want the most recent training data — it shipped Mar 2025
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for DeepSeek-V2.5 · 26 for Gemma 3 4B
DeepSeek-V2.5 outperforms in 2 benchmarks (GSM8k, HumanEval), while Gemma 3 4B is better at 1 benchmark (MATH).
DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 7.0x more expensive than Gemma 3 4B ($0.02/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 7.0x more expensive than Gemma 3 4B ($0.04/1M tokens).
In conclusion, DeepSeek-V2.5 is more expensive than Gemma 3 4B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V2.5 has 232.0B more parameters than Gemma 3 4B, making it 5800.0% larger.
Context Window
Maximum input and output token capacity
Gemma 3 4B accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Gemma 3 4B can generate longer responses up to 131,072 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
Input capabilities
Documented input modalities across available providers
Gemma 3 4B supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Gemma 3 4B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Gemma 3 4B
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Gemma 3 4B uses Gemma.
License differences may affect how you can use these models in commercial or open-source projects.
deepseek
Open weights
Gemma
Open weights
Release Timeline
When each model was launched
DeepSeek-V2.5 was released on 2024-05-08, while Gemma 3 4B was released on 2025-03-12.
Gemma 3 4B is 10 months newer than DeepSeek-V2.5.
May 8, 2024
2.4 years ago
Mar 12, 2025
1.5 years ago
10mo newerKnowledge Cutoff
When training data ends
Gemma 3 4B has a documented knowledge cutoff of 2024-08-01, while DeepSeek-V2.5's cutoff date is not specified.
We can confirm Gemma 3 4B's training data extends to 2024-08-01, but cannot make a direct comparison without DeepSeek-V2.5's cutoff date.
—
Aug 2024
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Gemma 3 4B is available from DeepInfra.
DeepSeek-V2.5
Gemma 3 4B
Outputs Comparison
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Gemma 3 4B side-by-side, then vote on the output you prefer.
FAQ
Common questions about DeepSeek-V2.5 vs Gemma 3 4B.