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Google: Gemini 3 Flash

Gemini 3 Flash Overview

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

Gemini 3 Flash Interactive Demo

Model settings

Thinking level

Max output tokens

Default 65,536 · max 65,536

Sign in to adjust thinking and output length per run.

Results appear here. Add an image or pick an example to run Gemini 3 Flash.

Gemini 3 Flash Details & Performance

Details

Resources

—

Vision Tasks

CaptioningChart Question AnsweringClassificationDocument Question AnsweringImage TaggingMulti-Label ClassificationOCRObject DetectionVision LanguageVisual Question Answering

Features

Foundation VisionLLMs with Vision CapabilitiesMultimodal Vision

Usage

Past 30 Days

Performance

Avg. Latency

Gemini 3 Flash Vision Evals

Vision Evals is Roboflow's ground-truth benchmark: every model runs the same real-world samples across five vision tasks, and answers are scored against ground truth.

Evals updated October 13, 2026Pricing updated October 26, 2026

Overall score#27 of 63
65.7%
Avg cost / sample#28 of 63
$0.0024
Avg speed / sample#7 of 63
5.83s
Avg tokens / sample
1.8K

Strengths and weaknesses

Gemini 3 Flash averages 65.7% across the five Vision Evals tasks, ranking #27 of 63 models overall.

Its weakest relative showing is Object Detection, ranking #46 of 63 at 38.6%.

At $0.0024 per sample it is the 28th cheapest of the 63 benchmarked models, and its average inference time of 5.8s per sample makes it the 7th fastest.

Performance profile

Field medianGemini 3 Flash

Field medians: Object Detection 54.4%, Counting 63.5%, Identification 84.4%, OCR 63.6%, Reasoning 57.6%.

Results by task

TaskScoreField (0 to 100)RankCost / sampleSpeed
Object Detection
38.6%
#46 of 63$0.00315.86s
Counting
67.6%
#23 of 63$0.00122.92s
Identification
93.8%
#8 of 63$0.00092.23s
OCR (low)
63.9%
#22 of 47$0.00246.96s
Single value
63.9%
Transcription
90.5%
Structured JSON
80.2%
Text localization
7.6%
OCR (high)
73.1%
#13 of 47$0.01321.69s
Single value
69.1%
Transcription
90.0%
Structured JSON
87.5%
Text localization
36.9%
Reasoning (low)
64.9%
#23 of 63$0.00203.47s
Reasoning (high)
74.2%
#17 of 50$0.00406.41s
  • Thinking longer helps: 9.1 points higher on ocr at high effort for 5.3x the cost and 3.1x the latency.
  • Thinking longer helps: 9.3 points higher on reasoning at high effort for 2x the cost and 1.8x the latency.

Price vs. performance

Score vs. cost

Overall benchmark score against estimated cost per sample, on a log scale. Upper-left is the sweet spot: high quality at low cost.

62 models on the current benchmark · scores and efficiency pooled across all five tasks at low effort · Gemini 3 Flash highlighted

Gemini 3 Flash scores are from a single run per task; a three-run re-run under the current protocol is pending · Methodology

View all Vision Evals →

Gemini 3 Flash Pricing

Gemini 3 Flash costs $0.500 per 1M input tokens and $3.00 per 1M output tokens.

Input$0.500 / 1M tokens
Output$3.00 / 1M tokens
Cached input$0.050 / 1M tokens

Pricing updated Oct 26, 2026

Alternatives to Gemini 3 Flash

Other models worth comparing for similar use cases.

OpenAI
GPT-5.4 Mini
GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.
Anthropic
Claude Sonnet 5
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
Qwen
Qwen3.6 Flash
Qwen3.6-Flash is the production API variant of the Qwen3.6 model series, developed by the Qwen team at Alibaba Group. It is built on the Qwen3.6-35B-A3B architecture, which combines a hybrid linear attention mechanism with sparse Mixture-of-Experts (MoE) routing to achieve high-throughput inference with reduced latency. The model is natively multimodal, processing both text and images within a unified early-fusion architecture, and supports 201 languages and dialects. It operates in a hybrid thinking mode, capable of generating explicit chain-of-thought reasoning before producing a final response, with the option to disable thinking for direct output. A Thinking Preservation feature allows reasoning context to be retained across multi-turn conversations, which is particularly useful for iterative agentic workflows.The model is trained with reinforcement learning scaled across large-scale agent environments and covers a broad range of tasks including agentic coding, frontend development, visual understanding, document processing, and tool use. Compared to the open-weight Qwen3.6-35B-A3B, the Flash API variant extends the default context window to 1 million tokens and includes built-in production features such as native function calling and official tool integrations. The underlying architecture achieves near-100% multimodal training efficiency relative to text-only training, and the model demonstrates strong performance on agentic coding benchmarks including SWE-bench Verified.
Google
Gemma 4 31B
Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.

Other Google Gemini Flash models

Other versions in the same family as Gemini 3 Flash.

GoogleGemini 3.8 FlashGoogleGemini 3.7 FlashGoogleGemini 3.6 FlashGoogleGemini 3.5 FlashGoogleGemini 2.5 Flash

Deploy Gemini 3 Flash with an API

Gemini 3 Flash runs as a hosted REST endpoint through Roboflow Workflows. Pick a task, then hand the prompt to your coding agent or copy the code. Deploying the workflow into a free Roboflow workspace replaces the your-workspace and YOUR_API_KEY placeholders with your own.

Connect your agent to Roboflow (once)

Add the Roboflow MCP server

claude mcp add --transport http roboflow https://mcp.roboflow.com/mcp

Run /mcp and authorize Roboflow in your browser when the OAuth flow opens.

Start a new Claude Code session so the MCP loads, then paste the prompt below (it works the same in any agent).

Deploy this workflow to your Roboflow workspace to use it.

Integrate the Roboflow "Gemini 3 Flash" workflow into my app.

- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gemini-3-flash-object-detection
- Auth: send my Roboflow API key as `api_key` in the request body, read from the ROBOFLOW_API_KEY env var (never hardcode).
- Body: { "api_key": ..., "inputs": { `image`: { type: "url" | "base64", value }, `classes`: string array } }.
- Billing: this workflow needs no provider API key — inference runs on my Roboflow credits. A BYO provider key can be added to the model step in the Roboflow workflow editor later.

With the Roboflow MCP connected, call `workflows_get` on "gemini-3-flash-object-detection" to read the exact input schema (the source of truth), then `workflows_run` on a sample image to confirm the output shape before writing code (the MCP is authenticated, so this needs no key). Without the MCP, use the contract above.

Before running the app, set up these keys so it does not error at runtime:
- `ROBOFLOW_API_KEY` (sent as `api_key`) from https://app.roboflow.com/settings/api
Create a .gitignore'd .env with these variables, using placeholder values for any I haven't given you. Then pause and tell me directly, in your reply: the full path to the .env file, exactly which keys I need to paste in, and the link to get each one. Wait for me to confirm I've added them before you run anything. Do not run the app until I confirm.

Then add the integration to my codebase: match my project's language, framework, and conventions; read every key from environment variables (never hardcode); add basic error handling; and include a small runnable example. If you can't tell what language my project uses, ask me.
Installpip install inference-sdk

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workflow
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="YOUR_API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="your-workspace",
  workflow_id="gemini-3-flash-object-detection",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": ["class1", "class2", "class3"]
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Deploy this workflow to your Roboflow workspace to use it.

// Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
const response = await fetch('https://serverless.roboflow.com/your-workspace/workflows/gemini-3-flash-object-detection', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    api_key: 'YOUR_API_KEY',
    inputs: {
      "image": {"type": "url", "value": "IMAGE_URL"},
      "classes": ["class1", "class2", "class3"]
    }
  })
});

const result = await response.json();
console.log(result);

Deploy this workflow to your Roboflow workspace to use it.

# Inference runs on your Roboflow credits — no provider API key needed. To bill your own provider account instead, add an api_key to the model step in the Roboflow workflow editor.
curl --location 'https://serverless.roboflow.com/your-workspace/workflows/gemini-3-flash-object-detection' \
--header 'Content-Type: application/json' \
--data '{
  "api_key": "YOUR_API_KEY",
  "inputs": {
    "image": {"type": "url", "value": "IMAGE_URL"},
    "classes": ["class1", "class2", "class3"]
  }
}'

Gemini 3 Flash License

Proprietary

Gemini 3 Flash is proprietary: the weights are not distributed, and the Gemini 3 Flash license is the vendor's commercial terms of service that you accept when you call the API.

Commercial use
Permitted under the vendor terms, typically metered per token or per request, with the vendor usage policy applying to your inputs and outputs.
Modification
Not available. Gemini 3 Flash weights are closed, so you can configure prompts and use vendor-hosted fine-tuning where it is offered, but you cannot modify the model itself.
Redistribution
Not permitted. You cannot self-host or resell the model; you build on the hosted API instead.

Vendor terms govern data retention, whether your inputs can be trained on, rate limits, and regional availability, and they can change with notice. Review them if you handle regulated or customer data.

Do I need a commercial license for Gemini 3 Flash?

Proprietary terms are set by the vendor rather than negotiated per project, and no open-source obligation attaches to your code. If you would rather deploy an open model instead of Gemini 3 Flash, Roboflow's licensing page lists the models whose commercial license is included when you deploy on Roboflow.

Do not hesitate to reach out with questions for your commercial project — our team will help you start solving business problems on the first call. See Roboflow commercial licensing for the models included in each plan.

Talk to sales

License information is provided as a guide and is not legal advice.

Frequently Asked Questions About Gemini 3 Flash Vision

Yes. Gemini 3 Flash accepts image input and handles OCR, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Identification at 93.8% (#8 of 63 at low effort). You can test it on your own image in the demo above.

Yes. it scores 63.9% (#22 of 47 at low effort) on Vision Evals OCR, which covers single readings, full transcriptions, structured JSON, and locating text with boxes. Within that, full transcriptions score 90.5%, and returning the fields of a label or form as JSON scores 80.2%.

Not its strength. On Vision Evals, Gemini 3 Flash scores 38.6% mAP@50 on object detection (#46 of 63 at low effort) and 67.6% judge-graded accuracy on object counting. For production counting or precise localization, pairing it with a specialized detector like RF-DETR or your own trained model in a Roboflow Workflow is usually more reliable: detect the objects, then count the detections.

On our benchmark's task mix, Gemini 3 Flash averages $0.0024 per sample at $0.50 per 1M input and $3.00 per 1M output tokens (#28 of 63 on cost), with an average speed of 5.8s per sample across the benchmark. Actual cost depends on your images and prompts.

On the overall Vision Evals ranking, Gemini 3 Flash sits #27 of 63 at 65.7%, just behind GPT-5.6 Terra (66%) and just ahead of Qwen3.6 35B-A3B (64.9%). See the full side-by-side: Gemini 3 Flash vs GPT-5.6 Terra.