Google: Gemma 4 26B A4B – API Quickstart

Sample code and API for Gemma 4 26B A4B

OpenRouter normalizes requests and responses across providers for you.

Get your API key

Create an API key from your OpenRouter dashboard and set it as an environment variable:

Make your first request

Use google/gemma-4-26b-a4b-it with the OpenRouter API:

OpenRouter supports reasoning-enabled models that can show their step-by-step thinking process. Use the reasoning parameter in your request to enable reasoning, and access the reasoning_details array in the response to see the model's internal reasoning before the final answer. When continuing a conversation, preserve the complete reasoning_details when passing messages back to the model so it can continue reasoning from where it left off. Learn more about reasoning tokens.

In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

Using third-party SDKs

For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

Enable streaming

Add "stream": true to your request body to receive responses as server-sent events:

Endpoint

POSThttps://openrouter.ai/api/v1/chat/completions

AuthorizationBearer $OPENROUTER_API_KEY

Content-Typeapplication/json

HTTP-Refereroptional — your site URL, for rankings

X-Titleoptional — your site name, for rankings

Modelgoogle/gemma-4-26b-a4b-it

Parameters

Name	Type	Default	Description
`reasoning`	map	—	Controls reasoning behavior for models that support thinking tokens, including whether reasoning is enabled, the reasoning effort, maximum reasoning tokens, and whether reasoning is excluded from the response.
`include_reasoning`	boolean	—	Deprecated alias for reasoning.exclude.
`frequency_penalty`	float	`0`	This setting aims to control the repetition of tokens based on how often they appear in the input.
`logit_bias`	map	—	Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100.
`max_tokens`	integer	—	This sets the upper limit for the number of tokens the model can generate in response.
`presence_penalty`	float	`0`	Adjusts how often the model repeats specific tokens already used in the input.
`seed`	integer	—	If specified, the inferencing will sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
`stop`	array	—	Stop generation immediately if the model encounter any token specified in the stop array.
`temperature`	float	`1`	This setting influences the variety in the model's responses.
`top_p`	float	`1`	This setting limits the model's choices to a percentage of likely tokens: only the top tokens whose probabilities add up to P.
`logprobs`	boolean	—	Whether to return log probabilities of the output tokens or not.
`top_logprobs`	integer	—	An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability.