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Gemma 4 26B A4B (Free)

gemma-4-26b-a4b-it:free

Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind, featuring 25.2B total parameters with only 3.8B activated per token—delivering near 31B-class quality at a fraction of the compute cost. It supports multimodal inputs including text, images, and video (up to 60s at 1fps). The model includes a 256K token context window, native function calling, configurable thinking/reasoning modes, and structured output support. Released under the Apache 2.0 license, it is well suited for efficient, production-ready multimodal and agentic applications.

Context
262.1K tokens
Endpoint
Get API KeyCompare

Pricing

Input$0 / 1M
Output$0 / 1M
Cache Write$0 / 1M
Cache Read$0 / 1M
Prompt cache writes and reads are included at no additional cost.

Quick Start

Select an endpoint and copy a working example for this model.

Endpoint:
python
from openai import OpenAI client = OpenAI(    api_key="YOUR_API_KEY",    base_url="https://api.apertis.ai/v1") response = client.chat.completions.create(    model="gemma-4-26b-a4b-it:free",    messages=[        {"role": "user", "content": "Hello!"}    ],    max_tokens=1024,    temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(#     model="gemma-4-26b-a4b-it:free",#     messages=[{"role": "user", "content": "Hello!"}],#     extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )

Supported Parameters

API docs
Common7 params
modelmessagesmax_tokenstemperaturetop_pstreamtools
Extended4 params
reasoning_effortstream_optionsthinkingextra_body

Cursor IDE Model IDs

Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.

gemma-4-26b-a4b-it:free

Decision guidance

Fit the request contract before production.

Coding-agent gateway guide

Use this model when

  • The workload includes code generation or code-oriented text tasks listed in the current catalog record.
  • The required task matches the listed capabilities: text-to-text, text-to-code, translation.
  • The request depends on listed features such as vision, thinking, function-calling.
  • The input fits within the published 262.1K-token context record, with output and system overhead budgeted separately.

Check before production

  • Confirm the production client uses one of the listed request surfaces: /v1/chat/completions, /v1/responses, /v1/messages.
  • Estimate a representative request from the current Free price fields instead of extrapolating from a tiny prompt.
  • Check the observed-availability card and run your own timeout, retry, and fallback test before relying on the model.

Compare with Other Models

See how this model compares to others from the same provider.

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Context
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Context
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