Model Comparison
Gemma 3 12B vs Qwen3 VL 4B ThinkingWhich is better in 2026?
Qwen3 VL 4B Thinking significantly outperforms across most benchmarks. Gemma 3 12B is 5.2x cheaper per token.
Verdict: Gemma 3 12B vs Qwen3 VL 4B Thinking — which is better?
Gemma 3 12B (by Google) and Qwen3 VL 4B Thinking (by Alibaba Cloud / Qwen Team) 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.
Gemma 3 12B outperforms in 1 benchmarks (IFEval), while Qwen3 VL 4B Thinking is better at 5 benchmarks (AI2D, GPQA, MathVista-Mini, MMLU-Pro, MMMU (val)). Qwen3 VL 4B Thinking significantly outperforms across most benchmarks.
On price, Gemma 3 12B is roughly 5.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 4B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Choose Gemma 3 12B if…
- cost matters — it's about 5.2x cheaper per token
Choose Qwen3 VL 4B Thinking if…
- you want the strongest raw capability — it leads on 5 of 6 shared benchmarks
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
Performance Benchmarks
Comparative analysis across standard metrics
Gemma 3 12B outperforms in 1 benchmarks (IFEval), while Qwen3 VL 4B Thinking is better at 5 benchmarks (AI2D, GPQA, MathVista-Mini, MMLU-Pro, MMMU (val)).
Qwen3 VL 4B Thinking significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Gemma 3 12B ($0.05/1M tokens) is 2.0x cheaper than Qwen3 VL 4B Thinking ($0.10/1M tokens).
For output processing, Gemma 3 12B ($0.10/1M tokens) is 10.0x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, Qwen3 VL 4B Thinking is more expensive than Gemma 3 12B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Gemma 3 12B has 8.0B more parameters than Qwen3 VL 4B Thinking, making it 200.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 4B Thinking accepts 262,144 input tokens compared to Gemma 3 12B's 131,072 tokens. Qwen3 VL 4B Thinking can generate longer responses up to 262,144 tokens, while Gemma 3 12B is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Both Gemma 3 12B and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Gemma 3 12B
Qwen3 VL 4B Thinking
License
Usage and distribution terms
Gemma 3 12B is licensed under Gemma, while Qwen3 VL 4B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
Gemma
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Gemma 3 12B was released on 2025-03-12, while Qwen3 VL 4B Thinking was released on 2025-09-22.
Qwen3 VL 4B Thinking is 6 months newer than Gemma 3 12B.
Mar 12, 2025
1.4 years ago
Sep 22, 2025
10 months ago
6mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Gemma 3 12B is available from DeepInfra. Qwen3 VL 4B Thinking is available from DeepInfra.
Gemma 3 12B
Qwen3 VL 4B Thinking
Outputs Comparison
Key Takeaways
Gemma 3 12B
View details
Qwen3 VL 4B Thinking
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Gemma 3 12B and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
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FAQ
Common questions about Gemma 3 12B vs Qwen3 VL 4B Thinking.