Gemini 3.8 Flash

3.8 Flash · Family: Gemini

Gemini Flash-family model (Google DeepMind) for agentic tasks, software engineering and complex workflows; 1M-token context window.

✓ Active✓ Public accessLLMMultimodalReasoning modelTool-using model📁 Gemini

Context window

tokens

Max output

64,000

tokens

🔬Google DeepMindResearch lab 🏢GoogleOwner

Access:APIHostedDeployment:☁ Cloud

Overview

Gemini 3.8 Flash is a model in the Gemini Flash family developed by Google DeepMind, positioned as a workhorse model for agentic tasks, software engineering and complex enterprise workflows. It provides advanced reasoning at the latency and scale typical of Flash models. It accepts text, image, video, audio and PDF input, with a 1M-token context window and a maximum output of 64k tokens. It supports function calling, search as a tool and computer use. The model is generally available via the API and Google hosted interfaces.

Classification

LLMMultimodalReasoning modelTool-using model

Family: Gemini

Access & deployment

APIHosted

Cloud

Weights: Closed

Key parameters

📏 Context: 1M

✓ Tools

📥 Input: text, image, video, audio…

Platforms

Vertex AI Google Antigravity Gemini Enterprise Agent Platform Google AI Studio

Technical specification

Context window

tokens

Max output tokens

64,000

tokens per response

Features:✓ Tool use

Modalities

⬇ Input

textimagevideoaudiodocuments

⬆ Output

text

Capabilities and applications

Native model capabilities

Reasoning

The model's ability to reason logically and solve complex problems.

Category: reasoning

Multi-step reasoning

Carrying out multi-step chains of reasoning across long, complex tasks.

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

Multimodal understanding

Category: multimodal

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

Function Calling

Category: planning

Structured output

Producing data in structured formats such as JSON.

Category: structured_generation

Audio understanding

Category: audio

Image understanding

Analysing and interpreting the content of images.

Category: vision

Video Understanding

Category: video

Chart understanding

Reading and interpreting charts, tables and diagrams.

Category: vision

OCR

Recognising text within images and documents.

Category: vision

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

Planning

Forming and executing action plans for complex tasks.

Category: planning

Interleaved Multimodal Input

Category: reasoning

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

Agentic coding

Multi-hour, multi-step programming tasks performed autonomously by the model: cloning a repository, running tests, iterating on fixes, integrating with CLI tools. Characteristic of Codex variants (GPT-5.1-Codex-Mini, Codex-Max).

Category: coding

Computer use

The model's ability to operate a computer interface by interpreting screenshots and generating actions such as clicks, typing, and navigating applications.

Category: planning