Gemini 3 Flash Preview

gemini-3-flash-preview

Run Gemini 3 Flash Preview in a hosted Python Task with structured output, web search, extended thinking, file inputs and MCP connectors.

Google's most balanced model built for speed, scale, and frontier intelligence. Default model across several Google surfaces with strong coding and state-of-the-art reasoning. Supports thinking, structured outputs, and search grounding across text, images, video, audio, and PDFs.

What Gemini 3 Flash Preview supports in qbash

Structured outputReplies as JSON that matches a schema you pass.
MCP connectorsCalls tools on MCP connectors during a request.
Web searchSearches the web while it answers.
Extended thinkingReasons through a problem before it replies.
Image inputReads WEBP, PNG, JPEG and GIF images.
Document inputReads PDF, TEXT, DOC, XLS, RTF and 7 more files.
Prompt cachingRepeated input is billed at the cached rate.

What is qbash?

qbash is where you build Tasks: Python programs that run a business process on managed infrastructure. Your code runs the steps and calls a model like Gemini 3 Flash Preview only at the ones that need judgment, with integrations, memory and files available in the same script.

A developer writes the Task once, and anyone on the team runs it from a form, on a schedule, from a webhook or from chat. Each Task is versioned and every run is logged.

How Tasks work

Specifications

Context window: 1.05Mtokens
Temperature: 0 to 2default 1

Structured output with Gemini 3 Flash Preview in a Python Task

qbash.ai.raw calls Gemini 3 Flash Preview from a Task. Pass a JSON Schema as output_schema and the reply comes back as a Python dict that matches it, ready to route to a connector.

main.py
ticket = qbash.inputs.get("ticket")

triage = qbash.ai.raw(
    f"Summarize this support ticket and rate its urgency:\n\n{ticket}",
    model="gemini-3-flash-preview",
    output_schema={
        "type": "object",
        "properties": {
            "summary": {"type": "string"},
            "urgency": {"type": "string", "enum": ["low", "medium", "high"]},
        },
        "required": ["summary", "urgency"],
    },
)

# triage is a dict, e.g. {"summary": "...", "urgency": "high"}
if triage["urgency"] == "high":
    qbash.integrations.slack.create_message(
        channel="#support",
        text=triage["summary"],
    )

qbash.output(triage)

Structured output in Tasks

Connect Gemini 3 Flash Preview to MCP servers

In qbash chat and a Task's inline prompts, you can attach MCP connectors to Gemini 3 Flash Preview. It calls their tools during the request, for example to read a Postgres table or post to a Slack channel.

This is MCP on the model side. Adding MCP servers to Cursor or Claude Desktop is a separate setup, and qbash can publish your Tasks as an MCP server for those clients as well.

Browse connectors Connect a client

Use Gemini 3 Flash Preview with web search

Turn on Web Search in an inline prompt's model bar and Gemini 3 Flash Preview searches the web while it answers.

Provider web tools

Extended thinking with Gemini 3 Flash Preview

Turn on Thinking in the same Settings popover to have Gemini 3 Flash Preview reason through a problem before it replies.