Model Comparison
DeepSeek-V2.5 vs Gemma 3 12BWhich is better in 2026?
DeepSeek-V2.5 shows notably better performance in the majority of benchmarks. Gemma 3 12B is 2.8x cheaper per token.
Verdict: DeepSeek-V2.5 vs Gemma 3 12B — which is better?
DeepSeek-V2.5 (by DeepSeek) and Gemma 3 12B (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-V2.5 outperforms in 2 benchmarks (GSM8k, HumanEval), while Gemma 3 12B is better at 1 benchmark (MATH). DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.
On price, Gemma 3 12B is roughly 2.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemma 3 12B also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V2.5 if…
- you want the strongest raw capability — it leads on 2 of 3 shared benchmarks
Choose Gemma 3 12B if…
- cost matters — it's about 2.8x 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
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V2.5 outperforms in 2 benchmarks (GSM8k, HumanEval), while Gemma 3 12B is better at 1 benchmark (MATH).
DeepSeek-V2.5 shows notably better performance in the majority of benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V2.5 ($0.14/1M tokens) is 2.8x more expensive than Gemma 3 12B ($0.05/1M tokens).
For output processing, DeepSeek-V2.5 ($0.28/1M tokens) is 2.8x more expensive than Gemma 3 12B ($0.10/1M tokens).
In conclusion, DeepSeek-V2.5 is more expensive than Gemma 3 12B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V2.5 has 224.0B more parameters than Gemma 3 12B, making it 1866.7% larger.
Context Window
Maximum input and output token capacity
Gemma 3 12B accepts 131,072 input tokens compared to DeepSeek-V2.5's 8,192 tokens. Gemma 3 12B can generate longer responses up to 131,072 tokens, while DeepSeek-V2.5 is limited to 8,192 tokens.
Input Capabilities
Supported data types and modalities
Gemma 3 12B supports multimodal inputs, whereas DeepSeek-V2.5 does not.
Gemma 3 12B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V2.5
Gemma 3 12B
License
Usage and distribution terms
DeepSeek-V2.5 is licensed under deepseek, while Gemma 3 12B 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 12B was released on 2025-03-12.
Gemma 3 12B is 10 months newer than DeepSeek-V2.5.
May 8, 2024
2.2 years ago
Mar 12, 2025
1.3 years ago
10mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V2.5 is available from DeepSeek, DeepInfra, Hyperbolic. Gemma 3 12B is available from DeepInfra.
DeepSeek-V2.5
Gemma 3 12B
Outputs Comparison
Key Takeaways
DeepSeek-V2.5
View detailsDeepSeek
Gemma 3 12B
View detailsDetailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V2.5 and Gemma 3 12B side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V2.5 vs Gemma 3 12B.