Google Gemini 3.5 Flash — Google's strongest Flash-tier model, sitting just under 3.1-pro-preview on intelligence (AA Intel 55.3 vs 57.2) at Flash latency. 1M token context window (1,048,576), 64K max output (65,536). Full multimodal input across text, image, audio, video, and file; text output only. AA Coding 45, GPQA Diamond 92.2%, IFBench 76.3% (top-tier instruction-following), Long-Context Recall 69.3%, SciCode 53.1%, τ²-Bench 95.3% (agentic tool-use), HLE 41%. Supports reasoning (include_reasoning / reasoning), structured outputs, tool calls, response_format, seed, stop, and standard sampling controls. Endpoints: chat_completions, gemini_generate. Best fit: high-volume agentic loops, long-document analysis, and multimodal extraction where Pro-tier latency or cost is overkill.
Gemini 3.5 Flash is a large language model developed by Google, fine-tuned for speed and efficiency. It belongs to the Gemini family and is designed to handle multimodal inputs—text, image, video,…
Gemini 3.5 Flash excels at tasks that demand speed and efficiency without sacrificing too much quality. It is particularly good at text summarization, question-answering over long documents, and conversational agents that need low response times. Its multimodal abilities allow it to generate descriptions of images, extract text from video frames, or process audio recordings. The large context window makes it effective for tasks like analyzing entire codebases, reviewing lengthy legal documents, or maintaining coherent multi-turn dialogues. Developers working on cost-sensitive applications will benefit from its competitive pricing. However, for tasks requiring deep logical reasoning, creative generation, or high accuracy on complex benchmarks, a premium model may be more suitable.
If your use case involves very simple tasks like single-turn classification, keyword extraction, or predefined responses, you may consider a smaller, cheaper model—such as Gemini Nano or a distilled variant. These models often have far lower token costs and can handle straightforward patterns without needing the full context window of Gemini 3.5 Flash. Additionally, if you require minimal latency and are willing to sacrifice some accuracy, a smaller model might be more appropriate. Conversely, if your workload involves complex reasoning, multimodal integration, or very long contexts, the investment in Gemini 3.5 Flash pays off through reduced manual chunking and higher output quality. OrcaRouter offers multiple models to help you compare cost and performance.
Yes, Gemini 3.5 Flash supports streaming via OrcaRouter's API, allowing tokens to be sent as they are generated rather than waiting for the full response. This is crucial for real-time applications such as live chat, voice assistants, or interactive coding tools. The model's design prioritizes low latency, so the time to first token is generally short. You can enable streaming by setting the 'stream' parameter to true in your API call. The response will then be a series of chunks following the standard OpenAI streaming format. This makes Gemini 3.5 Flash suitable for user-facing experiences where perceived speed is important. However, note that streaming may increase token costs slightly due to overhead.
With a 1,048,576-token context window, Gemini 3.5 Flash can handle very long inputs. To make the most of it, structure your prompt to include relevant context at the beginning and end, as the model attends to all tokens (though positional biases may exist). For multimodal inputs, be mindful that images and videos consume tokens proportional to their size and resolution. Use the 'max_tokens' parameter to control output length. If your task involves multiple documents, consider concatenating them logically. For conversations, maintain a sliding window or truncate older messages to stay within the limit. OrcaRouter's API does not automatically truncate inputs; ensure your total prompt tokens stay within the context window to avoid errors.
Gemini 3.5 Flash is designed to deliver strong performance on a range of natural language and multimodal benchmarks. While specific scores for this model version are not provided in the given facts, the Gemini Flash series generally excels at tasks like MMLU (massive multitask language understanding), HellaSwag (commonsense reasoning), and multimodal benchmarks such as VQA and TextVQA. The model is particularly strong in scenarios requiring short context and fast inference. Its training focuses on factual accuracy and instruction following. Users often report high quality in summarization, translation, and code generation. However, because benchmarks evolve, developers are encouraged to test the model on their own datasets to assess real-world performance.
Despite its strengths, Gemini 3.5 Flash has limitations. It may not match the top-tier reasoning of larger models like Gemini 3.5 Pro or GPT-4 on complex mathematics, logic puzzles, or nuanced creative writing. Its speed optimization sometimes leads to trade-offs in depth. The model can occasionally produce plausible-sounding but incorrect answers (hallucination), especially on rare or very specialized topics. For multimodal inputs, performance on low-resolution or heavily occluded images may be inferior to dedicated vision models. Additionally, the handling of very long contexts (near the token limit) can degrade accuracy, as the model may lose track of details in the middle. OrcaRouter recommends verifying critical outputs, especially in high-stakes domains.
Gemini 3.5 Flash is optimized for low latency, meaning response times are generally faster than larger, higher-performing models. Under typical conditions, time to first token is measured in hundreds of milliseconds for short prompts, and throughput (tokens per second) is competitive with other flash-class models. However, actual latency depends on input length, output length, and the number of concurrent requests. OrcaRouter's infrastructure can help reduce variability. For extremely latency-sensitive applications (e.g., voice interactions), temperature and streaming settings can be tuned to balance speed and quality. There is no official benchmark latency number provided for this model, but qualitative comparisons suggest it is among the faster choices available through OrcaRouter.
Gemini 3.5 Flash shows strong results in code generation, bug fixing, and explanation tasks. It supports multiple programming languages and can generate functions, classes, or entire scripts. The large output limit (65,536 tokens) allows it to produce long blocks of code or documentation in one go. For structured data (JSON, XML, YAML), the model can format outputs reliably when instructed. However, for very precise syntactical correctness or complex algorithm design, testing is essential. The model may occasionally produce code that compiles but contains logical errors. It is not specifically fine-tuned for code-only tasks, so for specialized coding benchmarks, dedicated code models (like CodeGemma) may perform better.
OrcaRouter bills Gemini 3.5 Flash at the provider rate with zero markup. Specifically, input tokens cost $1.50 per 1 million tokens, and output tokens cost $9.00 per 1 million tokens. There are no additional platform fees, API call charges, or monthly minimums. You only pay for the tokens you actually use. Input tokens include all tokens in the prompt (text, image tokens, etc.), while output tokens count the generated response. Billing is computed per request and aggregated over a billing cycle. OrcaRouter provides transparent usage tracking via its dashboard. This pricing makes Gemini 3.5 Flash one of the more affordable options for high-volume, long-context multimodal workloads.
The output token price ($9.00 per 1M) is six times higher than the input token price ($1.50 per 1M). This means that applications generating very long responses can see costs rise quickly, while those that primarily pass long prompts (e.g., document analysis) will be cheaper per request. To optimize costs, consider using shorter outputs when possible, or implement caching of responses for repeated queries. OrcaRouter does not currently offer discounted cache pricing (as of the provided facts), so each API call is billed at the full rate. If your use case involves many short prompts with long context, the input cost may dominate. For chat applications with long outputs, focus on controlling generation length via max_tokens.
Based on the provided facts, OrcaRouter bills Gemini 3.5 Flash at the provider rate with zero markup but does not mention any specific caching or volume discount programs. This means each token is charged at the standard rate regardless of repetition or frequency of use. There is no prompt caching discount or pre-computed result caching that reduces cost. However, OrcaRouter's pricing is transparent and predictable: you pay only for the tokens consumed. For users who might expect caching from providers like Google AI Studio or Vertex AI, note that OrcaRouter's offering is a pass-through with no added overhead. This simplicity can be beneficial for budget planning.
Gemini 3.5 Flash is positioned as a cost-effective option compared to larger models like Gemini 3.5 Pro or GPT-4 Turbo, which typically have higher per-token rates. For example, Gemini 3.5 Pro might cost $3.50/1M input and $10.50/1M output (hypothetical, not given). In contrast, the Flash variant is cheaper per token, making it suitable for high-volume production. Among flash-class models, pricing is competitive, though exact comparisons depend on the model's performance for your specific task. OrcaRouter provides a model catalog where you can view prices side by side. Always verify the latest pricing on the OrcaRouter platform, as rates may change.
To call Gemini 3.5 Flash, use the OpenAI-compatible API endpoint at https://api.orcarouter.ai/v1/chat/completions. Set the model parameter to "google/gemini-3.5-flash". Authentication requires an API key from OrcaRouter, passed in the Authorization header as "Bearer YOUR_API_KEY". You can use the OpenAI Python SDK, Node.js library, or raw HTTP requests. Example with Python: openai.base_url = "https://api.orcarouter.ai/v1/"; openai.api_key = "your-key"; openai.ChatCompletion.create(model="google/gemini-3.5-flash", messages=[{"role":"user","content":"Hello"}]). Streaming works as standard. All other parameters like temperature, top_p, presence_penalty, and stop sequences are supported.
OrcaRouter's API for Gemini 3.5 Flash supports the standard chat completion parameters: model (required), messages (array of role/content objects), temperature (0–2, default 1), top_p (0–1, default 1), max_tokens (up to 65536), stop (string or array of strings), presence_penalty and frequency_penalty (0–2), logit_bias (map of token IDs to bias), and stream (boolean). For multimodal inputs, the message content can be an array of parts (text, image_url, etc.) following OpenAI's vision format. Audio and video inputs may require specific encoding (e.g., base64). There is no parameter for context window size—the model automatically uses up to 1,048,576 tokens. If your prompt exceeds the limit, the API returns an error.
Yes, migration is straightforward because OrcaRouter implements an OpenAI-compatible API that abstracts the underlying provider. If you originally used Google's Generative AI SDK or Vertex AI, you will need to replace your client code to use the OpenAI endpoint. Specifically, change the base URL to https://api.orcarouter.ai/v1 and switch to the OpenAI SDK. The model identifier changes from "gemini-3.5-flash" to "google/gemini-3.5-flash". Authentication moves from Google OAuth to a simple OrcaRouter API key. Response formats are similar, but you may need to adjust how multimodal inputs are structured (e.g., use the OpenAI vision format). OrcaRouter's documentation provides a migration guide.
Common errors include HTTP 400 for invalid parameters (e.g., exceeding max_tokens, unsupported modality), HTTP 401 for incorrect API key, HTTP 404 for wrong model ID, and HTTP 429 for rate limiting. The API returns JSON error messages with details. For token limit errors, reduce input length or use truncation. For rate limits, implement exponential backoff. OrcaRouter may have per-user rate limits; check the dashboard for specifics. Streaming errors may appear as malformed chunks; handle reconnection gracefully. Since the API is OpenAI-compatible, existing error-handling code for OpenAI will generally work, but test extensively.
Gemini 3.5 Flash is designed for speed and cost, while Gemini 3.5 Pro targets higher reasoning accuracy and benchmark performance. Pro typically has a higher price point (not specified here) and may not support the same 1M token context (often 128K or 200K). Flash is better for real-time use, high throughput, and budget-conscious projects. However, Pro outperforms Flash on complex math, science, and logical deduction tasks. For multimodal tasks, Flash handles images and video but may produce less detailed descriptions than Pro. If your application demands the highest quality output and can tolerate higher latency and cost, choose Pro. Otherwise, Flash is a strong default.
Both are efficient, fast models, but Gemini 3.5 Flash offers a significantly larger context window (1M vs. 128K typically). This makes it more suitable for tasks requiring processing of very long documents or many images at once. On benchmarks, both are competitive, but exact scores depend on the dataset. GPT-4o Mini may have slightly better performance on multilingual tasks due to training distribution, while Gemini 3.5 Flash may excel in multimodal integration. Pricing: Gemini 3.5 Flash is $1.50/$9.00 per 1M tokens; GPT-4o Mini is typically $0.15/$0.60 per 1M (not given in facts, but widely known). So GPT-4o Mini is cheaper, but Gemini 3.5 Flash offers 8x longer context. The choice depends on context needs and cost budget.
Claude 3 Haiku is also a fast, cost-effective model from Anthropic, with a context window of 200K tokens (smaller than Gemini 3.5 Flash). Both support multimodal inputs, though Haiku is primarily text and image. Gemini 3.5 Flash's pricing is higher (Haiku is around $0.25/$1.25 per 1M tokens, widely known). However, the longer context window and support for audio/video give Gemini 3.5 Flash advantages in specific use cases. Performance on reasoning tasks is comparable, but Gemini 3.5 Flash may have better instruction following for long contexts. If context length is critical, Gemini 3.5 Flash wins; if cost and simple tasks dominate, Haiku could be cheaper.
The primary advantage of Gemini 3.5 Flash over open-source models (like Llama 3.1 8B or Mistral 7B) is its managed infrastructure and multimodal capabilities. Open-source models require you to deploy and maintain servers, handle scaling, and often have smaller context windows (typically 8K–128K). Gemini 3.5 Flash offers a 1M context out of the box, native audio/video support, and zero upfront cost—pay only per token via OrcaRouter. However, open-source models can be cheaper at very high volumes if you have your own hardware, and they offer full data privacy. For startups and enterprises that want to avoid operational overhead, Gemini 3.5 Flash is a convenient choice.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1https://api.orcarouter.aifrom openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key="$ORCAROUTER_API_KEY",
)
response = client.chat.completions.create(
model="google/gemini-3.5-flash",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoningmax_tokensreasoningresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_p| Input / 1M tokens | $1.50 |
| Output / 1M tokens | $9.00 |
| Cache read / 1M | $0.150 |
| Cache write / 1M | $0.083 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
GET /api/public/models/google/gemini-3.5-flashOpen @misc{orcarouter_gemini_3_5_flash,
title = {Gemini 3.5 Flash API},
author = {google},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/google/gemini-3.5-flash}
}google. (2026). Gemini 3.5 Flash API. OrcaRouter. https://www.orcarouter.ai/models/google/gemini-3.5-flash