DiffusionGemma 26B — Google Diffusion LLM · Ultra Fast
Available here
Overview
DiffusionGemma 26B is Google's diffusion-based large language model built for speed. Its diffusion architecture enables ultra-fast token generation, making it a standout choice on the Text & Chat board for latency-sensitive workloads.
This model suits developers and teams who prioritize response speed for real-time chat, interactive assistants, or high-throughput text pipelines. With a low per-token price and immediate availability on LongHua AI, it's practical for both experimentation and production use.
Strengths
- Ultra-fast diffusion generation
- Low per-token pricing
- Available free on LongHua AI
- Google research backing
Best for
- Real-time chat assistants
- High-volume content drafting
- Interactive coding companions
- Low-latency API backends
Things to know
- Newer architecture, less battle-tested
- May differ from autoregressive outputs
DiffusionGemma 26B vs other models
FAQ
What does DiffusionGemma 26B cost — is there a free option?
Official pricing is $0.15 per 1M input tokens and $0.60 per 1M output tokens. New users can try it free on LongHua AI with included trial uses.
What is DiffusionGemma 26B best at?
It excels at ultra-fast text generation thanks to its diffusion architecture, making it ideal for real-time chat, interactive tools, and high-throughput pipelines where latency matters most.
How do I choose between DiffusionGemma 26B and peers like Gemini 3.8 Flash, Kimi K3, or Gemini 3.7 Flash?
Pick DiffusionGemma 26B when raw generation speed is the top priority. Choose Gemini Flash variants for broader ecosystem integration, or Kimi K3 for strong reasoning at competitive speed.
How do I use DiffusionGemma 26B on HuaLinYuan / LongHua AI?
It's selectable now in LongHua AI. Sign in, choose DiffusionGemma 26B from the model picker, and start prompting — new users receive free trial credits automatically.
Similar models you can use here
Tell LongHua Tree ASI what you want — it splits the task and assigns the strongest model to each part, then delivers the finished result.