Source URL: https://simonwillison.net/2025/Feb/5/gemini-2/
Source: Simon Willison’s Weblog
Title: Gemini 2.0 is now available to everyone
Feedly Summary: Gemini 2.0 is now available to everyone
Big new Gemini 2.0 releases today:
Gemini 2.0 Pro (Experimental) is Google’s “best model yet for coding performance and complex prompts" – currently available as a free preview.
Gemini 2.0 Flash is now generally available.
Gemini 2.0 Flash-Lite looks particularly interesting:
We’ve gotten a lot of positive feedback on the price and speed of 1.5 Flash. We wanted to keep improving quality, while still maintaining cost and speed. So today, we’re introducing 2.0 Flash-Lite, a new model that has better quality than 1.5 Flash, at the same speed and cost. It outperforms 1.5 Flash on the majority of benchmarks.
That means Gemini 2.0 Flash-Lite is priced at 7.5c/million input tokens and 30c/million output tokens – half the price of OpenAI’s GPT-4o mini (15c/60c).
Gemini 2.0 Flash isn’t much more expensive: 10c/million for text/image input, 70c/million four audio input, 40c/million for output. Again, cheaper than GPT-4o mini.
I pushed a new LLM plugin release, llm-gemini 0.10, adding support for the three new models:
llm install -U llm-gemini
llm keys set gemini
# paste API key here
llm -m gemini-2.0-flash "impress me"
llm -m gemini-2.0-flash-lite-preview-02-05 "impress me"
llm -m gemini-2.0-pro-exp-02-05 "impress me"
Here’s the output for those three prompts.
Tags: gemini, llm, google, generative-ai, llm-pricing, ai, llms
AI Summary and Description: Yes
Summary: The release of Gemini 2.0 and its variants, including Gemini 2.0 Pro and Flash-Lite, introduces enhancements in coding performance and cost-effectiveness compared to existing models, notably OpenAI’s GPT-4o mini. This information is particularly relevant for developers and organizations leveraging AI technologies, especially in the context of model selection and pricing.
Detailed Description:
The announcement of Gemini 2.0 is significant as it showcases Google’s advancements in large language models (LLMs) and their positioning within the AI landscape. The introduction of different model variants caters to varying performance and pricing needs, which is crucial for businesses and developers looking to optimize their AI capabilities. Here are the major points to consider:
– **Gemini 2.0 Pro**:
– Marketed as Google’s best model yet for coding performance and complex prompts.
– Currently available as a free preview, indicating that users can experiment with significant functionalities at no cost.
– **Gemini 2.0 Flash and Flash-Lite**:
– Flash-Lite is highlighted for offering better quality than version 1.5 Flash while maintaining the same speed and cost.
– It is priced significantly lower than its direct competitor, OpenAI’s GPT-4o mini, indicating a strategic move to attract cost-sensitive customers.
– **Pricing Details**:
– Gemini 2.0 Flash-Lite: 7.5 cents per million input tokens and 30 cents for million output tokens.
– Gemini 2.0 Flash: 10 cents per million for text/image input, 70 cents for audio input, and 40 cents for output.
– These pricing models suggest that Google is aggressively positioning Gemini 2.0 to capture market share by being more cost-effective than similar offerings from OpenAI.
– **Plugin Release**:
– Mention of the new LLM plugin release (llm-gemini 0.10), which enhances user capabilities to interact with Gemini 2.0 models via command-line interface tools.
– Provides practical commands showcasing how to integrate the models into existing workflows, which can be vital for developers in the AI field.
– **Target Audience**:
– This information is particularly relevant for professionals in AI development, cloud computing, and infrastructure security as they navigate choices concerning model performance, security compliance, and economic feasibility.
Overall, the launch of Gemini 2.0 illustrates the competitive dynamics of the AI landscape with direct implications for developers and organizations focusing on both performance and cost. As AI continues to evolve, organizations must stay informed about emerging models and their features, paving the way for informed decision-making in AI deployment and utilization.