Gemini 3.6 Flash
gemini-3.6-flashCompare gemini-3.6-flash API pricing, supported endpoints, capabilities and access options on Modelsell.
- Starting price
- Input / Output · 1M
- Context
- 1M
- Maximum input window
- Max output
- 65.5K
- Maximum tokens per response
- Modalities
- →
- Knowledge cutoff
- Mar 2026
- Released
- Jul 2026
Pricing by Supplier
Capabilities / Supported modalities
Provider & data privacy
SentencePiece (Gemini)Performance
Benchmarks
Scores on standardized evaluations. Higher percentages are better — and rank percentile shows
Metrics sourced fromArtificial Analysis 2026-09-30·Gemini 3.6 Flash
This model is not included in the current benchmark snapshot.
Missing models or measurements are not zero scores.
Benchmark charts preserve the source model selection and reasoning settings. Missing models or measurements are not zero scores, and benchmark cost or speed is not this site’s service commitment.
About Gemini 3.6 Flash
让分析跟上迭代节奏
Gemini 3.6 Flash 面向真实工作中的快速分析与执行循环，擅长代码生成、智能体协作和空间关系理解。它适合需要连续读取信息、提出改动、查看结果再修正的项目，也适合将多种材料放在同一任务里综合判断。
不同素材可以围绕同一个问题
模型支持文字、图片、视频、音频和PDF输入，输出文本。比如分析一次产品使用测试，可以结合操作录像、用户讲述与界面说明，整理卡点发生的位置和原因；审查实现时，可以对照截图、设计要求和代码，指出需要调整的逻辑或布局。素材的价值在于互相补充，而不是只把它们各自摘要一遍。
空间关系任务应明确观察目标，例如物体位于哪里、路径如何变化、画面中哪个元素遮挡了另一个元素。涉及精细位置和快速动作时，应让结论对应具体画面，并把看不清的部分保留为待确认。
配合工具逐步完成工作
通过已接入的函数调用、代码执行或检索工具，模型可以参与持续的开发与资料处理流程。把每轮目标控制在可检查的范围内，及时提供测试输出、错误信息或新截图，能让后续修改更有依据。需要近期事实时应结合实时资料；处理影音输入时，结果是文字分析，配音和视频生成需要另行安排。
Use cases and prompting
使用测试分析示例
“结合操作录像、用户访谈音频和页面说明，找出用户从选商品到付款时的阻碍。按发生顺序列出问题，注明对应画面或话语，区分观察到的事实与推测原因。最后提出三项最小改动，并说明怎样验证改善效果。”
素材较长时，先指定需要关注的流程或时间段。
如何用于快速编程循环？
提供当前代码、一个明确目标和可执行的验证方式。每轮把实际测试结果交回，让模型根据失败原因修正，而不是重新生成整套方案。
上传音频或视频后会生成新的媒体吗？
该模型输出文字，适合理解和分析影音内容；制作新视频或语音应使用相应生成模型。
API access
API documentation
Code samples
/v1beta/models/gemini-3.6-flash:generateContent| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Replace <YOUR_API_KEY> with the API key from your token settings.
Authentication
All requests must include Authorization: Bearer <TOKEN> header. Anthropic-formatted endpoints accept the x-api-key header instead.
Generate tokens from the Tokens page; you can scope them to specific models, groups, IPs, and rate-limits.
Supported parameters
| Parameter | Type | Default / range | Description |
|---|---|---|---|
temperature | number | = 10 ~ 2 | Sampling temperature; lower is more deterministic |
top_p | number | = 10 ~ 1 | Nucleus sampling probability mass |
max_tokens | integer | >= 1 | Maximum number of tokens in the response |
frequency_penalty | number | = 0-2 ~ 2 | Penalises repetition of frequent tokens |
presence_penalty | number | = 0-2 ~ 2 | Encourages introducing new topics |
stop | array | — | Up to 4 strings that stop generation |
seed | integer | — | Deterministic sampling seed (best-effort) |
n | integer | = 1>= 1 | Number of completions to generate |
stream | boolean | = false | Stream tokens via Server-Sent Events |
response_format | object | — | Force JSON object or schema-conforming output |
tools | array | — | Tool / function declarations the model may call |
tool_choice | string | autononerequired | Tool-choice policy or specific tool name |
logprobs | boolean | = false | Return per-token log probabilities |
top_logprobs | integer | 0 ~ 20 | Number of top log probabilities returned per token |
logit_bias | object | — | Per-token logit bias map |
user | string | — | End-user identifier for abuse monitoring |
Rate limits
| Supplier | RPM | TPM | RPD |
|---|---|---|---|
| Gemini Cli | Unlimited | Unlimited | Unlimited |
| Google AI Studio | Unlimited | Unlimited | Unlimited |
| Google Vertex | Unlimited | Unlimited | Unlimited |
No restriction
Frequently asked questions about gemini-3.6-flash
What is gemini-3.6-flash?
Compare gemini-3.6-flash API pricing, supported endpoints, capabilities and access options on Modelsell.
How do I call gemini-3.6-flash?
Create an API key with access to gemini-3.6-flash, then use the exact model ID and a supported endpoint from the API access section. Request fields depend on the selected endpoint.
How is gemini-3.6-flash priced?
Pricing depends on the selected provider group and the model billing unit. The current input, output, request, or media prices are shown on this page before sign-up.
What is the context window of gemini-3.6-flash?
The model catalog lists a context window of 1048576 tokens. Check the selected endpoint for request limits.
What is the maximum output of gemini-3.6-flash?
The model catalog lists a maximum output of 65536 tokens. Your request settings may set a lower limit.
How should I evaluate gemini-3.6-flash for my project?
Start with the use cases and prompting guidance on this page, then evaluate the model with representative inputs from your project.