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Gemini 4 Hydrogen now available on AI Gateway

Source
vercel.com
Author
Aayush Kapoor
Date

Context

Vercel's AI Gateway now serves Google's Gemini 4 Hydrogen, the fast, lower-cost model in the Gemini 4 family that Google released on November 12, the same day as this changelog. The Gateway is a single API that sits in front of many model providers: a developer authenticates once with Vercel, names a model in each request, and the Gateway forwards the call, tracks usage and cost, and lets the developer configure retries and failover. For anyone already sending traffic through it, this changelog means Hydrogen is reachable by setting the model name to google/gemini-4-hydrogen. Vercel's announcement shows that model name working in its AI SDK, in an OpenAI-compatible Chat Completions endpoint, and in coding agents such as Claude Code, Codex and Cursor after running Vercel's CLI setup command, npx vercel ai-gateway setup. The capability claims in the post (better agentic coding, and than Gemini 3.8 Flash in the same latency class) come with no benchmark figures or independent testing.

On cost, the changelog makes one promise: Vercel says the Gateway passes through provider pricing with no markup and charges no platform fee on , including when customers bring their own Google key. The post itself lists no prices, but Vercel's model page shows $1.50 per million input tokens and $7.50 per million output tokens with a 1-million-token , matching Google's list price, and names both Google's direct API and Vertex AI as serving providers. Neither page states rate limits. Bringing your own key has a catch that Vercel's BYOK documentation spells out: if a request made with your key fails, the Gateway retries it on Vercel's own credentials and bills that fallback to your Gateway credits, and BYOK requires a paid tier with purchased credits. The free tier Google offers for Hydrogen on its own Gemini API is not mentioned for the Gateway, so developers who want free usage should not assume it carries over.

Terms in this piece · Glossary
  • tool use — A model's ability to call external functions — run code, search the web, edit files — instead of only generating text.
  • grounding — Tying a model's answers to checkable sources — retrieved documents, live data, tool results — instead of letting it answer from memory alone.
  • inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
  • context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
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