Model Comparison
DeepSeek-V4-Flash-Max vs DiffusionGemma 26B-A4BWhich is better in 2026?
DeepSeek-V4-Flash-Max significantly outperforms across most benchmarks.
Verdict: DeepSeek-V4-Flash-Max vs DiffusionGemma 26B-A4B — which is better?
DeepSeek-V4-Flash-Max (by DeepSeek) and DiffusionGemma 26B-A4B (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-V4-Flash-Max outperforms in 4 benchmarks (CodeForces, GPQA, Humanity's Last Exam, MMLU-Pro), while DiffusionGemma 26B-A4B is better at 0 benchmarks. DeepSeek-V4-Flash-Max significantly outperforms across most benchmarks.
Choose DeepSeek-V4-Flash-Max if…
- you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
Choose DiffusionGemma 26B-A4B if…
- you want the most recent training data — it shipped Jun 2026
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-Max outperforms in 4 benchmarks (CodeForces, GPQA, Humanity's Last Exam, MMLU-Pro), while DiffusionGemma 26B-A4B is better at 0 benchmarks.
DeepSeek-V4-Flash-Max significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Model Size
Parameter count comparison
DeepSeek-V4-Flash-Max has 258.8B more parameters than DiffusionGemma 26B-A4B, making it 1027.0% larger.
Context Window
Maximum input and output token capacity
Only DeepSeek-V4-Flash-Max specifies input context (1,048,576 tokens). Only DeepSeek-V4-Flash-Max specifies output context (65,536 tokens).
Input Capabilities
Supported data types and modalities
DiffusionGemma 26B-A4B supports multimodal inputs, whereas DeepSeek-V4-Flash-Max does not.
DiffusionGemma 26B-A4B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-Max
DiffusionGemma 26B-A4B
License
Usage and distribution terms
DeepSeek-V4-Flash-Max is licensed under MIT, while DiffusionGemma 26B-A4B 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-Flash-Max was released on 2026-04-23, while DiffusionGemma 26B-A4B was released on 2026-06-10.
DiffusionGemma 26B-A4B is 2 months newer than DeepSeek-V4-Flash-Max.
Apr 23, 2026
2 months ago
Jun 10, 2026
1 months ago
1mo newerKnowledge Cutoff
When training data ends
DiffusionGemma 26B-A4B has a documented knowledge cutoff of 2025-01-01, while DeepSeek-V4-Flash-Max's cutoff date is not specified.
We can confirm DiffusionGemma 26B-A4B's training data extends to 2025-01-01, but cannot make a direct comparison without DeepSeek-V4-Flash-Max's cutoff date.
—
Jan 2025
Outputs Comparison
Key Takeaways
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-Max and DiffusionGemma 26B-A4B side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Flash-Max vs DiffusionGemma 26B-A4B.