Gemini 3.8 Flash

Gemini 3.8 Flash — Text Generation (LLM)

What is Gemini 3.8 Flash?

Gemini 3.8 Flash is Google's most intelligent Flash-tier large language model, engineered for long-horizon software engineering, autonomous agents, and complex enterprise workflows at Flash speed. It accepts text, image, video, audio, and PDF inputs and returns text, with a 1-million-token context window and up to 64k output tokens. Released in September 2026, it builds on Gemini 3.7 Flash with substantial gains in coding, agentic tasks, and multi-step reasoning. The headline design choice is that it works harder: on difficult goals it takes smaller reasoning steps, calls tools iteratively, and verifies its work.

Key Features

• •1M-token context window and 64k maximum output tokens
• •Tunable thinking via the effort parameter (low, medium, high)
• •Built-in tools: code execution, function calling, search grounding, Google Maps grounding, URL context, file search, computer use (preview), context caching
• •Structured outputs via a JSON schema (response_format)
• •Multimodal inputs: text, image, video, audio, and PDF

Best Use Cases

• •Autonomous software engineering — writing, debugging, and refactoring self-contained programs end to end
• •Agentic coding loops and iterative tool-calling workflows
• •Document-heavy enterprise workflows, plus finance and legal agentic analysis
• •Multi-step STEM and professional reasoning
• •Grounded question answering backed by live Google Search

Prompt Tips and Output Quality

• •State the output format explicitly, for example "return only code in one Python block".
• •Raise effort to high for complex, multi-step coding or reasoning; use low for fast, simple tasks; medium is a balanced default.
• •For verifiable coding, ask the model to include its own test or assert. In testing, the model wrote a complete stack-based virtual machine whose generated program ran and asserted factorial(6) == 720.
• •Attach image, video, or PDF inputs for OCR, visual Q&A, or summarization, and enable web_search for time-sensitive facts.

How to Use Gemini 3.8 Flash

Gemini 3.8 Flash is a text-output LLM for coding, agents, and multi-step reasoning. Send a single prompt; optionally attach an image, video, or PDF, enable web_search, request structured output with response_format, and dial reasoning depth with effort.

Core Parameters

• •prompt (required): your instruction or question. Be specific and name the output format you want.
• •effort: low, medium, or high reasoning. High makes the model take extra reasoning steps before answering; low is fastest.
• •web_search: grounds answers in live Google Search — use for news or time-sensitive queries.
• •response_format: pass a JSON schema to force machine-readable output.
• •image / video: optional multimodal inputs for OCR, visual Q&A, or summarization.

Recommended Settings

• •Complex coding / multi-step reasoning: effort=high. Tested end to end: with effort=high the model returned a complete, self-contained stack-based virtual machine with loop control flow, and the generated program executed and its own assert confirmed factorial(6) == 720.
• •Everyday tasks / low latency: effort=low.
• •Balanced default: effort=medium (Google's recommended default).
• •Structured extraction: set response_format to a JSON schema with strict: true.
• •Grounded factual answers: web_search=true.

Example prompt to try

This prompt ran end to end at the settings shown and returned a working program:

• •One-shot runnable code (effort=high). Prompt: "Write a single self-contained Python file (standard library only) that implements a tiny stack-based virtual machine … opcodes PUSH/POP/ADD/SUB/MUL/DUP/SWAP/JMP/JZ/STORE/LOAD/PRINT/HALT … a bytecode program that uses JMP/JZ to compute 6! in a loop … assert the result equals 720 …" The output was a complete VM that printed 720 when run. Tip: for any coding task, ask the model to include an assert or self-test so you can verify the result immediately.

Further prompts the model is built for (suggestions): "Refactor this module and add unit tests", "Write a three.js scene that renders a rotating solar system", "Extract every invoice line item from this PDF as JSON".

Tips

• •Keep multi-step goals explicit; at higher effort the model verifies its own work.
• •Use the 1M-token context to paste whole files or long documents in one call.
• •Prefer effort=medium or low when latency and token budget matter more than maximum depth.

Does Gemini 3.8 Flash generate images or audio?

No — it returns text only.

Which thinking levels are supported?

Low, medium, and high; minimal is not supported and returns an error.

How large is the context window?

Up to 1M input tokens and 64k output tokens.

Can it browse the web?

Yes, through search grounding (web_search) and URL context.

Is it multimodal?

Yes — it accepts text, image, video, audio, and PDF inputs.

How does it differ from Gemini 3.7 Flash?

It adds substantial coding, agentic, and reasoning gains at similar speed, while 3.7 Flash remains a strong efficiency-first option.