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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchSet Gemini API generation limits and safety thresholds for the specific model you call: maxOutputTokens is a hard ceiling, temperature controls sampling, and safety settings determine which harm-probability levels are blocked. For Gemini 3, Google recommends leaving temperature at its default of 1.0. Thinking models also spend output tokens on internal reasoning, so an overly small cap can yield a partial or empty answer.
How to set a Gemini API output limit
Use maxOutputTokens in GenerationConfig to cap the number of tokens included in a response candidate. The cap is a ceiling, not a request for the model to produce that many tokens. The default and maximum depend on the model; check the selected model’s output_token_limit in Google’s GenerationConfig API reference before choosing a value.
Leave room for the complete answer rather than setting the limit to the expected answer length with no margin. For thinking-capable models, the limit includes thought tokens as well as the visible response. Google’s thinking guide warns that a hard cap can interrupt reasoning, producing a truncated or empty result with a MAX_TOKENS finish reason. If you want to reduce cost or latency on a thinking model, consider lowering thinking_level instead of imposing a very small output cap.
Configuration names vary by SDK: the API reference uses maxOutputTokens, while some guide examples use max_output_tokens. Use the spelling for your SDK and verify which generation options the chosen model supports; not every parameter is available on every model.
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What temperature should you use for Gemini 3?
For Gemini 3 models, keep temperature at its default value of 1.0, as Google strongly recommends in its Gemini 3 developer guide. The guide cautions that changing temperature—especially setting it below 1.0—can cause unexpected behavior, including looping or degraded performance on complex math and reasoning tasks.
More generally, temperature affects sampling randomness, but its default and supported range are model- and endpoint-dependent. Google’s API reference describes a range of 0.0–2.0, while its troubleshooting guide lists 0.0–1.0 among parameter checks. These are not a single universal range for every model and API path. Check the documentation for your model and endpoint, and test quality for your task rather than assuming a lower value guarantees deterministic output.
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How Gemini API safety thresholds work
Safety settings can be provided per request for four categories, described in Google’s safety settings guide:
- Harassment: negative or harmful comments targeting identity or protected attributes.
- Hate speech: content the guide describes as rude, disrespectful, or profane.
- Sexually explicit content.
- Dangerous content: content promoting, facilitating, or encouraging harmful acts.
A threshold sets the harm-probability level at which content is blocked:
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| Threshold | Probability levels blocked |
|---|---|
BLOCK_ONLY_HIGH |
High |
BLOCK_MEDIUM_AND_ABOVE |
Medium and high |
BLOCK_LOW_AND_ABOVE |
Low, medium, and high |
OFF or BLOCK_NONE |
Listed by Google as available threshold options; check the current guide for their behavior and availability for the model and API path you use. |
When no threshold is set, Google states the default block threshold is Off for Gemini 2.5 and Gemini 3. Do not assume that default applies to other model families. A stricter threshold can block more borderline content; permissive settings can increase the application’s review obligations under Google’s terms. Test realistic safe and unsafe inputs for your use case rather than disabling filters simply to avoid interruptions.
How to detect and handle a safety block
Google assigns a category and probability rating to content. Inspect the feedback fields so your application can distinguish a blocked prompt from a blocked candidate and avoid treating either as an ordinary completed answer:
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promptFeedback.blockReasonidentifies prompt-level blocking.- A candidate’s
finishReasonandsafetyRatingsprovide response and safety information. - A safety-blocked response uses
SAFETYas its finish reason; blocked content is not returned.
When a request is blocked, provide an appropriate fallback—for example, explain that the request could not be completed and offer a safe way to rephrase it. Handle other finish reasons separately: a MAX_TOKENS result may indicate truncation, not a safety decision. The safety settings guide documents these fields.
Safety filters are one control, not a guarantee
Safety settings do not guarantee factual or harmless output. Google’s safety guidance cautions that generated content can be inaccurate, biased, or offensive. Assess the risks of your application, test relevant safe and unsafe cases, gather user feedback, and monitor use. Filtering should be part of that process, not a substitute for evaluating the behavior your application needs.
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When a generation setting causes an error
If a parameter is rejected or behaves unexpectedly, first confirm that it is supported by the selected model and API version. Then check its allowed range and the correct SDK field name in the documentation for that endpoint. Google’s troubleshooting guide specifically recommends checking API version and model feature support when a parameter produces an error.
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