Gemini 4 Hydrogen launch post cover

Cost/1M tokens

input
$1.50
cached
$0.075
output
$7.50
cache write
$0.0833
Context
1Mtokens
Released
Today

Gemini 4 Hydrogen is a model from Google DeepMind. The launch announcement centres on agentic workflows, with Google DeepMind saying the model stands out for speed and efficiency. It is built for everyday coding and agent-driven tasks.

How it compares

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Additional source evidence
Source model
google/gemini-4-hydrogen
Source family
gemini
Last updated
2026-11-12
Knowledge cutoff
2026-03
Model context
1M
Model output limit
66K
Open weights
No
File attachments
Yes
Reasoning
Yes
Tool calling
Yes
Temperature control
No

Published benchmarks

BenchmarkScoreEvaluation detailsEvidence
SWE-sweep13.5 · %—Source
Terminal-Bench57 · cheating-adjusted4.1Source
Agents' Last Exam71.6 · score1.1Source
AutomationBench-AA76.1 · %—Source
Vending-Bench14549 · bank balance (USD)2Source
OSWorld77.6 · offline subset2.1Source
MKA53.4 · Climb WSR accuracy—Source
Chartography66.6 · %—Source
Terminal-Bench-Science51.4 · %0.1Source
HLE-Diamond58.8 · %—Source
Complete source record (JSON)
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  "id": "google/gemini-4-hydrogen",
  "name": "Gemini 4 Hydrogen",
  "description": "Google's Gemini 4 model for everyday coding and agentic workflows, with four thinking levels and a 1M-token context window",
  "family": "gemini",
  "last_updated": "2026-11-12",
  "knowledge": "2026-03",
  "attachment": true,
  "reasoning": true,
  "tool_call": true,
  "temperature": false,
  "modalities": {
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  "limit": {
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  "benchmarks": [
    {
      "name": "SWE-sweep",
      "score": 13.5,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-hydrogen/",
      "metric": "%"
    },
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      "name": "Terminal-Bench",
      "score": 57,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-hydrogen/",
      "metric": "cheating-adjusted",
      "version": "4.1"
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    {
      "name": "Agents' Last Exam",
      "score": 71.6,
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      "name": "AutomationBench-AA",
      "score": 76.1,
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      "name": "Vending-Bench",
      "score": 14549,
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      "metric": "bank balance (USD)",
      "version": "2"
    },
    {
      "name": "OSWorld",
      "score": 77.6,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-hydrogen/",
      "metric": "offline subset",
      "version": "2.1"
    },
    {
      "name": "MKA",
      "score": 53.4,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-hydrogen/",
      "metric": "Climb WSR accuracy"
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    {
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      "metric": "%"
    },
    {
      "name": "Terminal-Bench-Science",
      "score": 51.4,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-hydrogen/",
      "metric": "%",
      "version": "0.1"
    },
    {
      "name": "HLE-Diamond",
      "score": 58.8,
      "source": "https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-hydrogen/",
      "metric": "%"
    }
  ],
  "release_date": "2026-11-12",
  "open_weights": false
}

Text provenance

Gemini app/web

Google SynthID text watermarking

Google DeepMind says text generated in the Gemini app and web experience is watermarked with SynthID by adjusting token probabilities.

Official explanation

Frequently asked questions

API and pricing

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Generated from Vertical Tabs data, updated November 12, 2026.

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