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Gemini 3.5 Flash
- Multimodal
- Fast Inference
- Code Generation
- Reasoning
- Vision
Gemini 3.5 Flash is Google’s natively multimodal reasoning model optimized for very low latency and cost while maintaining frontier‑level performance, particularly for coding and agentic workflows.
About the model
What is Gemini 3.5 Flash?
Gemini 3.5 Flash is a proprietary large multimodal reasoning model from Google designed to deliver fast, cost‑efficient, frontier‑level intelligence for real‑time applications. It is mainly used to power AI agents that perform complex, long‑horizon tool-using workflows, and to provide high-throughput, low-latency text, code, and multimodal generation in products like the Gemini app, AI Mode in Search, and the Gemini API. It also serves as a workhorse model for enterprise integrations where speed and scale are critical, such as agent platforms and developer tooling. Gemini 3.5 Flash belongs to Google’s Gemini model family and is an evolution of earlier Flash variants like Gemini 3 Flash and Gemini 2.5 Flash.
Model capabilities
5 Core Capabilities
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Multimodal Input
Processes combined text, images, and other media in one request, enabling integrated understanding across visual and textual information.
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Conversational Chat
Supports fast, interactive chat-style conversations with context retention, suitable for assistants, agents, and real-time user interactions.
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Long-Context Handling
Handles very large context windows, enabling analysis, summarization, and question‑answering over lengthy documents and complex sessions.
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Code Generation
Generates and edits code from natural language instructions, helping with implementation details, small utilities, and iterative code refinement.
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Text Summarization
Summarizes long text inputs into concise outputs while preserving key information, useful for documents, articles, and transcripts.
Use cases
6 Most Valuable Use Cases
- Agentic Workflows Automation
- Software Coding Assistance
- Long-Context Document Analysis
- Multimodal Content Understanding
- Search & Web Assistance
- Business Process Orchestration
Transparent pricing
Cost Comparison
LLM API targets significantly lower effective pricing for Gemini 3.5 Flash–class models than major cloud providers.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | Up to 1M tokens (target, Gemini 3.5 Flash–class) | |||||
| Global | $0.075 per 1M tokens | $0.30 per 1M tokens | Up to 1M tokens | ||||
| Google Vertex AI | Regional (e.g. us-central1, europe-west4, asia-northeast1) | 99.9% | $0.075 per 1M tokens | $0.30 per 1M tokens | Up to 1M tokens | ||
| OpenRouter | Global |
Performance benchmarks
Technical Specifications
| Metric | Gemini 3.5 Flash | GPT-4o | Claude 3.5 Haiku |
|---|---|---|---|
| Model Type | Multimodal LLM (text, vision, code) | Multimodal LLM (text, vision, audio) | Multimodal LLM (text, vision) |
| Context Window | 128K–1M* | 128K | 200K |
| Max Output Tokens | — | 16K | — |
| Input Price ($/1M tokens) | $1.50* | $2.50 | $0.80 |
| Output Price ($/1M tokens) | $9.00* | $10.00 | $4.00 |
| Modality Support | Text, images, code | Text, images, audio, video | Text, images |
| Provider | OpenAI | Anthropic |
30-day usage via LLM API
- 12.4B
- Prompt tokens processed (30 days)
- 8.1B
- Completion tokens generated (30 days)
- 27.5M
- API requests served (30 days)
- 99.8%
- Average uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Dynamically route each request to the best model across providers using policies, performance signals, and constraints—without changing your integration or redeploying code.
One endpoint, every model -
Cost-Aware Orchestration
Optimize spend automatically by mixing premium and budget models, enforcing per-request budgets, and monitoring real-time token usage across vendors from a single control plane.
Max performance, minimal spend -
Resilient Fallback Logic
Define provider and model fallbacks that trigger on errors, timeouts, or quality checks so your production workloads stay up even when vendors don’t.
Stay online, fail safely -
End-to-End Observability
Trace every request across models and providers with logs, metrics, and structured payloads to debug latency, failures, and quality issues in one console.
See every token hop -
Task-Level Abstractions
Ship faster with high-level tasks—chat, tools, rerank, embed—so you can swap underlying models or providers without refactoring your application logic.
Code to tasks, not models -
High-Throughput Batch Jobs
Run large-scale inference jobs—embeddings, scoring, generation—through a unified batch API with concurrency controls, retries, and progress tracking built in.
Scale jobs, not scripts
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a low-cost general-purpose model for high-volume API traffic and experimentation.
- You need fast responses for chatbots, simple agents, and customer-support automations.
- Your use case involves lightweight code generation, refactoring, and short code explanations.
- Your use case involves summarizing short to medium documents, emails, or web pages.
- You need multilingual understanding and translation for everyday content across many major languages.
- Your use case involves basic image understanding, captioning, and simple visual question answering.
- You need inexpensive fine-tuning or prompt-tuning to specialize a model for specific tasks.
Avoid if...
- You need frontier-level reasoning and accuracy comparable to state-of-the-art flagship models.
- Your workload requires highly reliable, domain-expert answers for medical, legal, or financial decisions.
- You need complex multi-step tool use, planning, or large autonomous agents with long horizons.
- Your workload requires consistently strong performance on very long-context documents or codebases.
- You need best-in-class code generation, debugging, and architecture design for large software systems.
- Your workload requires advanced vision reasoning over technical diagrams, dense charts, or medical images.
FAQ
Frequently Asked Questions
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What is Gemini 3.5 Flash?
Gemini 3.5 Flash is a lightweight, multimodal Google model optimized for fast, low-cost inference on text and image tasks.
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What modalities does Gemini 3.5 Flash support via LLM.API?
Through LLM.API, Gemini 3.5 Flash supports text input and output, and image input with text output for vision-language tasks.
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What is the context window of Gemini 3.5 Flash?
Gemini 3.5 Flash supports up to a 1 million token context window, suitable for very long conversations or documents.
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What is Gemini 3.5 Flash best suited for?
Gemini 3.5 Flash is best for high-throughput, latency-sensitive workloads like chatbots, routing, classification, and lightweight reasoning over text and images.
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How is Gemini 3.5 Flash priced on LLM.API?
LLM.API exposes Gemini 3.5 Flash with per-token pricing, typically significantly cheaper than flagship reasoning models; check the LLM.API pricing page for current rates.
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How fast is Gemini 3.5 Flash in practice?
Gemini 3.5 Flash is designed for low latency and high throughput, generally returning responses faster than larger Gemini reasoning models at similar token counts.
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How do I call Gemini 3.5 Flash through LLM.API?
Set the model field to "google/gemini-3.5-flash" in your LLM.API completion or chat endpoint request, and pass prompts like with other text models.
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How does Gemini 3.5 Flash compare to larger Gemini 3.5 models?
Gemini 3.5 Flash is cheaper and faster but generally less capable at complex reasoning, coding, and nuanced instruction following than the larger Gemini 3.5 models.
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Does Gemini 3.5 Flash support structured outputs via LLM.API?
Yes, you can use JSON-style or tool-calling schemas through LLM.API, but outputs are not guaranteed to be perfectly well-formed in all cases.
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What are the main limitations of Gemini 3.5 Flash?
Gemini 3.5 Flash may struggle with deep multi-step reasoning, precise long-code generation, and domain-expert tasks compared to heavier, more capable models.
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Can I fine-tune Gemini 3.5 Flash through LLM.API?
Direct fine-tuning of Gemini 3.5 Flash is not exposed via LLM.API; instead, use prompt engineering, retrieval, and system prompts to adapt behavior.
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Does Gemini 3.5 Flash support streaming responses on LLM.API?
Yes, LLM.API can stream Gemini 3.5 Flash tokens incrementally, which is recommended for chat applications needing minimal perceived latency.
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