Google DeepMind’s fast workhorse model (2024/25): multimodal, 1M context, native image and speech (TTS) generation, native tools and Live API. A legacy generation.
Context window
1M
tokens
Release date
11 December 2024
Access:APIHostedDeployment:☁ Cloud
Overview
Access & deployment
APIHosted
Cloud
Weights: Closed
Key parameters
📏 Context: 1M
✓ Tools · ✓ Fine-tuning
📥 Input: text, image, audio, video…
Platforms
Technical specification
Context window
1M
tokens
License
Proprietary
Features:✓ Tool use✓ Fine-tuning
Modalities
⬇ Input
textimageaudiovideodocuments
⬆ Output
textcodeimageaudiostructured_data
Capabilities and applications
Native model capabilities
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Image understanding
Analysing and interpreting the content of images.
Category: vision
Audio understanding
Category: audio
Video understanding
The model's ability to analyse and interpret video content — recognising actions, motion, events and relationships between objects over time.
Category: video
Multimodal understanding
Category: multimodal
Text-to-image generation
Generating an image from a text description (prompt). The model interprets a natural-language instruction and produces a new, coherent visual from scratch — without any input image.
Category: vision
Text to speech
Category: speech
Real-time inference
The model's ability to generate responses with very low latency (>1000 tokens/sec) on specialized inference hardware (e.g. Cerebras WSE), enabling interactive, turn-by-turn collaboration with a human.
Category: coding
Agentic capability
The model's ability to autonomously plan and execute multi-step tasks by sequentially using tools, maintaining context, and adapting to intermediate results.
Category: planning
Multilingual
Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.
Category: language
Application domains
Technical architecture
Core Architecture
Model Form
Training Techniques
Deployment and security
☁ Available on platforms
🔒 Security / Enterprise
✓ Verified enterprise information
Updated: 24 Jul 2026↗ Security documentation