Roboflow
Google

Google: Gemini 3.5 Flash

Gemini 3.5 Flash Overview

Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.

Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.

Gemini 3.5 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 3.5 Flash.

Gemini 3.5 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 3.5 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 16, 2026

Overall score#4 of 63
81.8%
Avg cost / sample#53 of 63
$0.012
Avg speed / sample#37 of 63
11.52s
Avg tokens / sample
2.4K

Strengths and weaknesses

Gemini 3.5 Flash averages 81.8% across the five Vision Evals tasks, ranking #4 of 63 models overall.

It places in the top three for Counting and Identification.

Its weakest relative showing is Object Detection, ranking #8 of 63 at 70.6%.

At $0.012 per sample it is the 53rd cheapest of the 63 benchmarked models, and its average inference time of 11.5s per sample makes it the 37th fastest.

Performance profile

Field medianGemini 3.5 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 (low)
70.6%
±2.0, Mean of 3 runs, range 68.7 to 72.6
#8 of 63$0.01620.60s
Object Detection (high)
69.8%
±1.8, Mean of 3 runs, range 67.5 to 71.1
#11 of 31$0.02123.88s
Counting (low)
80.6%
±0.7, Mean of 3 runs, range 79.7 to 81.1
#3 of 63$0.007511.75s
Counting (high)
82.4%
±0.0, Mean of 3 runs, range 82.4 to 82.4
#3 of 31$0.01713.63s
Identification (low)
99.0%
±1.6, Mean of 3 runs, range 96.9 to 100.0
#3 of 63$0.00404.70s
Identification (high)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
#2 of 31$0.00688.08s
OCR (low)
76.7%
#4 of 47$0.0137.39s
Single value
71.7%
Transcription
90.0%
Structured JSON
86.8%
Text localization
56.2%
OCR (high)
75.5%
#9 of 47$0.03114.36s
Single value
69.6%
Transcription
85.4%
Structured JSON
88.1%
Text localization
53.3%
Reasoning (low)
82.1%
±2.0, Mean of 3 runs, range 80.1 to 84.1
#6 of 63$0.008214.27s
Reasoning (high)
81.0%
±1.7, Mean of 3 runs, range 79.5 to 82.8
#9 of 50$0.01811.81s
  • Thinking longer does not help: 0.8 points lower on object detection at high effort for 1.3x the cost and 1.2x the latency.
  • Thinking longer helps: 1.8 points higher on counting at high effort for 2.2x the cost and 1.2x the latency.
  • Thinking longer does not help: 1 points lower on identification at high effort for 1.7x the cost and 1.7x the latency.
  • Thinking longer does not help: 1.1 points lower on ocr at high effort for 2.4x the cost and 1.9x the latency.
  • Thinking longer does not help: 1.1 points lower on reasoning at high effort for 2.2x the cost and 0.8x 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 3.5 Flash highlighted

Gemini 3.5 Flash scores are the mean of 3 runs per task at both low and high effort; OCR is a single preliminary run · Methodology

View all Vision Evals →

Gemini 3.5 Flash Pricing

Gemini 3.5 Flash costs $1.50 per 1M input tokens and $9.00 per 1M output tokens.

Input$1.50 / 1M tokens
Output$9.00 / 1M tokens
Cached input$0.150 / 1M tokens

Pricing updated Oct 16, 2026

Alternatives to Gemini 3.5 Flash

Other models worth comparing for similar use cases.

Anthropic
Claude Sonnet 5.5
Claude Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.On visual and agentic evaluations reported at launch, Sonnet 5.5 scores 61.6% on Chartography, a chart recognition test, compared with 15.6% for Claude Sonnet 5, and 80.1% on OSWorld 2.1, a computer use benchmark measuring screenshot driven control of a desktop environment, compared with 57.0% for Sonnet 5. It reports 70.6% on Terminal-Bench 4.0 for agentic coding. Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.
OpenAI
GPT-5.6 Terra
GPT-5.6 Terra is the mid-tier reasoning model in OpenAI's GPT-5.6 family, which also includes the flagship Sol and the lightweight Luna. Introduced in a limited preview on June 26, 2026, and made broadly available on July 9, 2026, Terra accepts text and image input and produces text output, supporting vision, function calling, tool use, and agentic workflows. It is designed as a balanced option for everyday professional and production workloads — including coding assistance, document analysis, customer support, and multi-step agent tasks — where both output quality and cost efficiency matter. OpenAI positions Terra as delivering performance competitive with GPT-5.5 at approximately half the price, with a context window of around 1,050,000 tokens. On Terminal-Bench 2.1, Terra scores 84.3%, matching Claude Fable 5 on that benchmark. Under OpenAI's Preparedness Framework, Terra is rated High for cybersecurity and biological capabilities, meaning it demonstrates meaningful capability in those domains without reaching the Critical threshold.GPT-5.6 introduces a new naming convention in which the generation number (5.6) is paired with a durable capability tier name (Sol, Terra, or Luna), allowing each tier to advance on its own schedule. Terra carries the API identifier gpt-5.6-terra and supports the same reasoning effort controls available across the family, including adjustable reasoning depth. The model includes prompt caching with explicit cache breakpoints and a 30-minute minimum cache life, with cache writes billed at 1.25x the uncached input rate and cache reads receiving a 90% discount. GPT-5.6 Terra is a proprietary, closed-weights model served through the OpenAI API, Codex, and ChatGPT.
Qwen
Qwen3.7 Flash
Qwen3.7 Flash is the low-latency, cost-oriented tier of Alibaba's Qwen3.7 series, a vision-language reasoning model that accepts interleaved text and image input and returns text. It is built as a hybrid thinking model: like the rest of the Qwen3.7, Qwen3.6, and Qwen3.5 families served through Alibaba Cloud Model Studio, it can either emit an explicit reasoning trace before answering or respond directly, with thinking behavior controlled by an enable_thinking switch that defaults to on for the Qwen3.7 generation. The model exposes a context window of roughly one million tokens and a maximum generation length of 65,536 tokens, which allows long multi-image sequences, long documents, and extended agent trajectories to be held in a single request.Functionally, Qwen3.7 Flash targets multimodal agent workloads rather than pure chat. Reported strengths include object recognition, spatial understanding, and perception of real-world scenes, alongside visual coding, search, and computer-use style interaction where the model reads screen content and reasons over interface state. Weights are not published; the model is a proprietary endpoint positioned below Qwen3.7 Plus and Qwen3.7 Max in the same series, and it supports function calling and tool use for agentic pipelines.

Other Google Gemini Flash models

Other versions in the same family as Gemini 3.5 Flash.

GoogleGemini 3.8 FlashGoogleGemini 3.7 FlashGoogleGemini 3.6 FlashGoogleGemini 3 FlashGoogleGemini 2.5 Flash

Deploy Gemini 3.5 Flash with an API

Gemini 3.5 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 3.5 Flash" workflow into my app.

- Endpoint: POST https://serverless.roboflow.com/<your-workspace>/workflows/gemini-3-5-flash-open-prompt
- 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 }, `prompt`: text } }.
- 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-3-5-flash-open-prompt" 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-3-5-flash-open-prompt",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "prompt": "Describe what you see in the image"
  },
  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-3-5-flash-open-prompt', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    api_key: 'YOUR_API_KEY',
    inputs: {
      "image": {"type": "url", "value": "IMAGE_URL"},
      "prompt": "Describe what you see in the image"
    }
  })
});

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-3-5-flash-open-prompt' \
--header 'Content-Type: application/json' \
--data '{
  "api_key": "YOUR_API_KEY",
  "inputs": {
    "image": {"type": "url", "value": "IMAGE_URL"},
    "prompt": "Describe what you see in the image"
  }
}'

Gemini 3.5 Flash License

Proprietary

Gemini 3.5 Flash is proprietary: the weights are not distributed, and the Gemini 3.5 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 3.5 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 3.5 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 3.5 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

This model is proprietary. The author retains all rights, and use of the model is governed by their specific terms of service or license agreement.

Commercial use depends on the terms set by the model author. Most proprietary commercial models require a paid subscription, API key, or per-call billing. Check the provider’s pricing and terms-of-service for details.

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

Frequently Asked Questions About Gemini 3.5 Flash Vision

Yes. Gemini 3.5 Flash accepts image input and handles OCR, object counting, identification, visual reasoning, and object detection. On Roboflow's Vision Evals its strongest task is Counting at 80.6% (#3 of 63 at low effort). You can test it on your own image in the demo above.

Yes, and it is one of the model's strongest vision skills: it scores 76.7% (#4 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%, and returning the fields of a label or form as JSON scores 86.8%.

Yes. On Vision Evals, Gemini 3.5 Flash scores 70.6% mAP@50 on object detection (#8 of 63 at low effort) and 80.6% judge-graded accuracy on object counting.

On our benchmark's task mix, Gemini 3.5 Flash averages $0.01 per sample at $1.50 per 1M input and $9.00 per 1M output tokens (#53 of 63 on cost), with an average speed of 11.5s per sample across the benchmark. Actual cost depends on your images and prompts.

On the overall Vision Evals ranking, Gemini 3.5 Flash sits #4 of 63 at 81.8%, just behind Claude Opus 5.5 (82%) and just ahead of GPT-6.1 Sol (81.7%). See the full side-by-side: Gemini 3.5 Flash vs Claude Opus 5.5.