Google’s Gemini series of AI large language models (LLMs) started off rough nearly a year ago with some embarrassing incidents of image generation gone awry, but has steadily improved, and the company appears to be intent on making its second generation effort — Gemini 2.0 — the biggest and best yet for consumers and enterprises.
Today, the company announced the general release of Gemini 2.0 Flash, the introduction of Gemini 2.0 Flash-Lite, and an experimental version of Gemini 2.0 Pro.
These models, designed to support developers and businesses, are now accessible through Google AI Studio and Vertex AI, with Flash-Lite in public preview and Pro available for early testing.
“All of these models will feature multimodal input with text output on release, with more modalities ready for general availability in the coming months,” wrote Koray Kavukcuoglu, chief technology officer of Google DeepMind, in the company’s announcement blog post — showcasing an advantage Google is bringing to the table even as competitors such as DeepSeek and OpenAI continue to launch powerful rivals.
Google plays to its multimodal strenghts
Neither DeepSeek R1 nor OpenAI’s new o3-mini model can accept multimodal inputs, that is, images and file uploads or attachments.
While DeepSeek R1 can accept them on its website and mobile app chat, it performs optical character recognition (OCR) a more than 60 year old technology, to extract the text only from these uploads — not actually understanding or analyzing any of the other features contained therein.
However, both are a new class of “reasoning” models that deliberately take more time to think through answers and reflect on “chains-of-thought” and the correctness of their responses. That’s opposed to typical LLMs like the Gemini 2.0 pro series, so the comparison between Gemini 2.0 and DeepSeek R1 and OpenAI o3 is a bit of an apples-to-oranges.
But there was some news on the reasoning front today from Google, too: Google CEO Sundar Pichai took to the social network X to declare that the Google Gemini mobile app for iOS and Android has been updated with Google’s own rival reasoning model Gemini 2.0 Flash Thinking, and that the model could be connected to Google’s existing hit services Google Maps, YouTube, and Google Search, allowing for a whole new range of AI-powered research and interactions that simply can’t be matched by upstarts without such services like DeepSeek and OpenAI.
I tried it briefly on the Google Gemini iOS app on my iPhone while writing this piece, and it was impressive based on my initial queries, thinking through the commonalities of the top 10 most popular YouTube videos of the last month and also providing me a table of nearby doctors’ offices and opening/closing hours, all within seconds.
Gemini 2.0 Flash enters general release
The Gemini 2.0 Flash model, originally launched as an experimental version in December, is now production-ready.
Designed for high-efficiency AI applications, it provides low-latency responses and supports large-scale multimodal reasoning.
One major benefit over the competition is in its context window, or the number of tokens that the user can add in the form of a prompt and receive back in one back-and-forth interaction with an LLM-powered chatbot or application programming interface.
While many leading models such as OpenAI’s new o3-mini that debuted last week only support 200,000 or fewer tokens — about the equivalent of a 400-500 page novel of information density — Gemini 2.0 Flash supports 1 million, meaning it is is capable of handling vast amounts of information, making it particularly useful for high-frequency and large-scale tasks.
Gemini 2.0 Flash-Lite arrives to bend the cost curve to the lowest yet
Gemini 2.0 Flash-Lite, meanwhile, is an all-new large language model aimed at providing a cost-effective AI solution without compromising on quality.
Google DeepMind states that Flash-Lite outperforms its full-size (larger parameter-count) predecessor, Gemini 1.5 Flash, on third-party benchmarks such as MMLU Pro (77.6% vs. 67.3%) and Bird SQL programming (57.4% vs. 45.6%), while maintaining the same pricing and speed.
It also supports multimodal input and features a context window of 1 million tokens, similar to the full Flash model.
Currently, Flash-Lite is available in public preview through Google AI Studio and Vertex AI, with general availability expected in the coming weeks.
As shown in the table below, Gemini 2.0 Flash-Lite is priced at $0.075 per million tokens (input) and $0.30 per million tokens (output). Flash-Lite is positioned as a highly affordable option for developers, outperforming Gemini 1.5 Flash across most benchmarks while maintaining the same cost structure.
Logan Kilpatrick highlighted the affordability and value of the models, stating: “Gemini 2.0 Flash is the best value prop of any LLM, it’s time to build!”
Indeed, compared to other leading traditional LLMs available via provider API such as OpenAI 4o-mini ($0.15/$0.6 per 1 million tokens in/out), Anthropic Claude ($0.8/$4! per 1M in/out), and even DeepSeek’s traditional LLM V3 ($0.14/$0.28), in Gemini 2.0 Flash appears to be the best bang for the buck.
Gemini 2.0 Pro arrives in experimental availability with 2-million token context window
For users requiring more advanced AI capabilities, the Gemini 2.0 Pro (Experimental) model is now available for testing.
Google DeepMind describes this as its strongest model for coding performance and handling complex prompts. It features a 2 million-token context window and improved reasoning capabilities, with the ability to integrate external tools like Google Search and code execution.
Sam Witteveen, co-founder and CEO of Red Dragon AI and an external Google Developer Expert for Machine Learning who often partners with VentureBeat, discussed the Pro model in a YouTube review. “The new Gemini 2.0 Pro model has a two-million-token context window, supports tools, code execution, function calling, and grounding with Google Search—everything we had in Pro 1.5 but improved.”
He also noted Google’s iterative approach to AI development: “One of the key differences in Google’s strategy is that they release experimental versions of models before they go GA (generally accessible), allowing for rapid iteration based on feedback.”
Performance benchmarks further illustrate the capabilities of the Gemini 2.0 model family. Gemini 2.0 Pro, for instance, outperforms Flash and Flash-Lite across tasks like reasoning, multilingual understanding, and long-context processing.
AI Safety and Future Developments
Alongside these updates, Google DeepMind is implementing new safety and security measures for the Gemini 2.0 models. The company is leveraging reinforcement learning techniques to improve response accuracy, using AI to critique and refine its own outputs. Additionally, automated security testing is being used to identify vulnerabilities, including indirect prompt injection threats.
Looking ahead, Google DeepMind plans to expand the capabilities of the Gemini 2.0 model family, with additional modalities beyond text expected to become generally available in the coming months.
With these updates, Google is reinforcing its push into AI development, offering a range of models designed for efficiency, affordability, and advanced problem-solving, and answering the rise of DeepSeek with its own suite of models ranging from powerful to very powerful and extremely affordable to slightly less (but still considerably) affordable.
Will it be enough to help Google eat into some of the enterprise AI market, which was once dominated by OpenAI and has now been upended by DeepSeek? We’ll keep tracking and let you know!
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