Gemma 4 26B A4B

Gemma 4 26B A4B by Google excels in the Coding category, where it ranks #49 with a composite score of 73/100. Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at a fraction of the compute cost. Supports multimodal input including text, images, and video (up to 60s at 1fps). Features a 256K token context window, native function calling, configurable thinking/reasoning mode, and structured output support. Released under Apache 2.0. It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Gemma 4 26B A4B is priced at $0.13 per million input tokens and $0.40 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Gemma 4 26B A4B holds rank #49 out of 6 models tracked. Its quality rank is #49 and adoption rank is #49. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Gemma 4 26B A4B has been evaluated across 6 different signals. Its strongest areas include Capabilities (83/100), Benchmarks (72/100), Pricing (100/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 73/100 reflects a weighted combination of all tracked signals.

Gemma 4 26B A4B is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check Google's website for current free tier availability and promotional offers.

Gemma 4 26B A4B supports a 262K token context window (262,144 tokens total). That translates to roughly 196,608 words in a single prompt. This is large enough to process entire codebases, research papers, or long conversation histories in one shot.

Gemma 4 26B A4B can generate up to 262K output tokens (262,144 tokens) per response. That is roughly 196,608 words. This is enough for generating complete code files, detailed reports, or long-form content in a single response.

Gemma 4 26B A4B supports image understanding (vision), function/tool calling, structured JSON output, extended reasoning/chain-of-thought, streaming responses. Function calling lets you integrate it with external APIs and tools programmatically. Vision support means it can analyze images, screenshots, and diagrams alongside text. These capabilities determine which workflows and integrations the model can handle natively.

Yes, Gemma 4 26B A4B is an open-source model. You can download the weights, run it locally, fine-tune it for your use case, or deploy it on your own infrastructure. Many cloud providers also offer hosted versions if you prefer not to manage the infrastructure yourself. Self-hosting gives you full control over data privacy and eliminates per-token API costs.

Gemma 4 26B A4B was developed by Google. It was released on April 3, 2026. You can access it through Google's API or download the model weights directly. Check our provider page for all models from Google and how they compare against each other.

Pick Gemma 4 26B A4B when you need a solid balance of cost and capability for everyday development tasks, content generation, and standard API integrations. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Gemma 4 26B A4B through Google's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.