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Fast, Efficient AI with Controllable Reasoning
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Zac Zuo

Gemini 2.5 Flash-Lite - Google's fastest, most cost-efficient model

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Gemini 2.5 Flash-Lite is Google's new, fastest, and most cost-efficient model in the 2.5 family. It offers higher quality and lower latency than previous Lite versions while still supporting a 1M token context window and tool use. Now in preview.

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Zac Zuo
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Hi everyone! The Gemini 2.5 model family is now officially generally available, which is great news, even though many of us have been following the frequent iterations and using the preview versions for a while now. But the brand new model here is Gemini 2.5 Flash-Lite. It's lightweight enough, fast enough, and most importantly, cost-efficient enough – while still being remarkably smart. It has a higher quality than previous Flash-Lite versions but with even lower latency. That's a fantastic combination. For those high-volume, latency-sensitive tasks like classification or translation, having a model this fast that still supports a 1M token context and tool use is a very powerful new option.
Shahriar Hasan

Impressive to see Google optimizing not just for intelligence but also for speed and cost. Does Gemini 2.5 Flash-Lite offer any fine-tuning or custom instruction capabilities for enterprise-level workflows?

Tanmay Parekh

All the best for the launch @sundar_pichai & team!

Sam @CRANQ

I'm impressed by the balance of speed + intelligence & for someone who works w/ high volume tasks, finding a model that holds onto quality while slashing latency is like the best thing ever.

Lowkey rethinking what's possible for my classification projects :)) excited to see how this impacts the AI tooling landscape

Evgenii Zaitsev

Gemini 2.5 Flash-Lite is a fantastic leap forward! The balance between speed, cost efficiency, and quality is exactly what developers need for high-volume tasks. I’ve been excited to see how it improves performance without compromising on accuracy.

Mahendra Rao

Super excited to see the Gemini 2.5 model family evolving — especially the 1 M-token context window and improved reasoning capabilities across modalities. The advancements in code generation are particularly interesting — curious to see how it performs on real-world API workflows.

A quick question: does the Flash‑Lite edition maintain consistent latency when running on mobile or resource-constrained environments? Would love to understand its optimisation approach there.

Planning to experiment with Gemini 2.5 soon for API integration and code-heavy tasks — has anyone benchmarked it yet for code-gen performance (vs. previous Gemini or other LLMs)?

Kudos to the Google DeepMind team — stellar work on the benchmarks!