Transparent pricing with no hidden fees. Pay as you go.
gemini-3.7-flashgemini-3.7-flashgemini-3.7-flash| Model | Spec | PoYo price | Official price | You save |
|---|---|---|---|---|
| gemini-3.7-flash | Input | $0.600/1M tokens 120 credits /1M tokens | $0.750/1M tokens Official | 20% |
Cached input | $0.060/1M tokens 12 credits /1M tokens | $0.075/1M tokens Official | 20% | |
Output (including thinking tokens) | $3.00/1M tokens 600 credits /1M tokens | $3.75/1M tokens Official | 20% |
* Actual fees are based on the final output.
Complete guide to using Gemini 3.7 Flash API for Fast, Capable Agents
Gemini 3.7 Flash is Google's generally available Flash workhorse for agentic coding, web development, multimodal reasoning, and complex knowledge work. It supports a 1,048,576-token input context and up to 65,536 output tokens.
Use gemini-3.7-flash through /v1/chat/completions or the Gemini-native /v1beta/models path. PoYo charges 120 input, 12 cached-input, and 600 output credits per million tokens—20% below Google's introductory Standard rates.
Official Google demonstrations show the model moving from planning to finished, interactive results across coding, visual, physical, and document tasks.
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Gemini 3.7 Flash can plan and implement interactive experiences while keeping the visual result and code behavior aligned.
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Stronger design adherence helps turn visual references into polished landing pages and audit existing interfaces for implementation gaps.
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Agentic planning and multimodal understanding let the model interpret a scene, choose actions, and coordinate tools over several steps.
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A million-token context window supports extended document analysis, evidence synthesis, calculations, and report-oriented workflows.
Plan and execute multi-step coding work, navigate repositories, call tools, debug failures, and verify changes with fewer retries.
Turn design references into functional interfaces, audit existing frontends, and iterate with visual feedback.
Reason across long reports, PDFs, research material, and enterprise documents within a 1M-token context.
Combine text, images, video, audio, and PDFs for review, extraction, understanding, and structured answers.
Build agents with function calling, code execution, search grounding, structured output, and URL context.
Choose low, medium, or high thinking levels to balance response speed, cost, and task depth.
Selected August 2026 benchmarks compare Gemini 3.7 Flash with Gemini 3.6 Flash, Claude Sonnet 5, and GPT-5.6 Terra across coding, agentic execution, web development, document work, and long-context reasoning.
| Benchmark | Gemini 3.7 Flash | Gemini 3.6 Flash | Claude Sonnet 5 | GPT-5.6 Terra | Notes |
|---|---|---|---|---|---|
| Artificial Analysis Intelligence Index | 56 | 52 | 55 | 57 | Composite intelligence |
| FrontierCode 1.1 Main | 43.6% | 34.4% | 42.7% | 41.3% | Production code quality |
| DeepSWE v1.1 | 65.3% | 48.6–49.0% | 53.8% | 69.6% | Long-horizon software engineering |
| WebDev Arena (Elo) | 1588 | 1538 | 1541 | 1523 | Web development |
| Terminal-Bench 2.1 | 85.8% | 78.0% | 80.4% | 87.4% | Agentic terminal coding |
| Terminal-Bench 3.0 | 14.9% | 5.4% | 14.6% | 20.8% | General agent capabilities |
| AutomationBench | 30.4% | 17.0% | 10.7% | 23.6% | Enterprise workflow automation |
| GDP.pdf | 34.0% | 22.0% | 28.0% | 24.7% | Complex document processing |
| Harvey LAB-AA (Legal) | 90.7% | 85.1% | 90.1% | 85.2% | Complex legal workflows |
| OSWorld-2.0 | 47.9% | 33.8% | — | 50.2% | Agentic computer use |
| GDM-MRCR v2 (128k) | 97.0% | 91.8% | 81.5% | 93.5% | Long-context performance |
| HLE-Verified | 53.6% | 51.2% | 31.0% | 51.1% | Multidisciplinary expert reasoning |
Selected benchmark results from the supplied Gemini 3.7 Flash knowledge base, current as of August 2026. Scores use each benchmark's published evaluation setup.
1. Create an API key. Sign in and create a PoYo key.
2. Choose a format. Use POST /v1/chat/completions for OpenAI-compatible clients, or POST /v1beta/models/gemini-3.7-flash:generateContent for Gemini-native payloads. Streaming is available through :streamGenerateContent.
3. Set the model. Send gemini-3.7-flash as the model ID. Read the Gemini API documentation.
gemini-3.7-flash model ID with either supported endpoint format./v1/chat/completions for OpenAI-compatible requests or /v1beta/models/gemini-3.7-flash:generateContent for Gemini-native requests. Both non-streaming and streaming flows are supported.Use OpenAI-compatible Chat Completions or Gemini-native GenerateContent with one model ID.
Evaluate and ship Gemini alongside other models without managing another integration credential.
See input, cached-input, and output rates in both credits and US-dollar equivalents.
Test both request formats, review service status, and track usage before moving to production.