Gemini 3.8 Flash’s edge is intelligence per dollar

Google unveiled Gemini 3.8 Flash and Gemini 3.8 Flash Cyber on Wednesday, claiming the new model is its best reasoning and coding model yet while maintaining the same speed and cost as its predecessor, Gemini 3.7 Flash. Priced at $0.75 per million input tokens and $3.75 per million output tokens, Gemini 3.8 Flash undercuts rivals like Anthropic's Claude Opus 5 ($5 input/$25 output) and OpenAI's GPT-5.6 Sol ($4 input/$20 output) by 4x to 5x, positioning Google to compete on intelligence per dollar as enterprises shift toward efficient models.

Google is adding another lightweight model to Gemini's collection. On Wednesday, the company unveiled Gemini 3.8 Flash https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/?utm source=tw&utm medium=social&utm campaign=og , the latest addition to its lineup and the third release in the Flash series in six weeks. The company claims the model is its best reasoning and coding model yet but maintains the same speed and cost as its predecessor, Gemini 3.7 Flash. The new model comes in two flavors: - Gemini 3.8 Flash, which it calls its "most intelligent workhorse model" the same thing it said about 3.7 Flash https://www.thedeepview.com/articles/google-wagers-gemini-can-win-on-economics just a few weeks ago with improvements in software engineering, agentic tasks and multi-step reasoning. The new model's introductory pricing is set at $0.75 per million input tokens and $3.75 per million output tokens. - Meanwhile, the company also introduced Gemini 3.8 Flash Cyber, its most capable cybersecurity model yet, available specifically for cyber defenders through its Fairwind program https://deepmind.google/fairwind-program/ , a recently-launched DeepMind program to stay ahead on cyber defense. The company says the model offers "frontier-level performance" in vulnerability detection and automated patching. Though the model is still outranked by Anthropic's Claude Opus 5 on benchmarks https://storage.googleapis.com/gweb-uniblog-publish-prod/images/gemini-3.8-flash evals table light 1.original.png for knowledge work tasks, long-horizon software engineering tasks, general agentic tasks and computer use, the model beat out Opus 5 and GPT-5.6 Sol on domain-specific tasks for legal, finance, and biology, as well as for agentic terminal coding. Notably, it beats models from frontier labs in one increasingly important domain: Price. Gemini's rivals cost 4x to 5x more per token, or higher. Anthropic's Opus 5 costs $5 per million input tokens and $25 per million output tokens, while OpenAI's GPT-5.6 Sol runs $4 per million input tokens and $20 per million output tokens. Google's latest addition to the Flash family comes as AI rivals like Anthropic and OpenAI navigate releasing their more powerful, bulky and expensive competitors, Mythos https://www.thedeepview.com/articles/in-fable-5-1-ai-s-cost-war-comes-for-anthropic and Astra https://openai.com/index/path-to-astra/ . Google, meanwhile, hasn't released its own heavyweight model since February, when it released Gemini 3.1 Pro https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-1-pro/ , and is rumored to have scrapped internal candidates for Gemini 3.5 Pro https://x.com/wallstengine/status/2094895853819613281 because they didn't significantly outperform the Flash series. Our Deeper View Google has all of the pieces necessary to succeed in AI, with access to capital, data and compute and a brand name trusted by the public. Still, it's struggling to put forth the same kind of powerful frontier models that Anthropic and OpenAI have been able to at the same speed. However, with the consistent additions to the Flash series, Google may be trying to capitalize on the movement towards efficiency. Token prices are dropping https://www.silicondata.com/products/silicon-index/llm-token-expenditure-index , largely due to enterprises shifting towards open-source, domain-specific, and small models. As a result, Gemini 3.8 Flash's price-to-performance ratio could be attractive to the businesses by providing more intelligence per dollar.