gemma-4-26b

gemma-4-26b is a budget-tier language model from Google with 26B parameters. gemma-4-26b is an open-weights model, meaning the weights are publicly available for self-hosting, fine-tuning, and deployment on your own infrastructure — not just through Google's API. Gemma 4 is Google's newest open-weights family, featuring a 26B model with vision support and a 256K context window — competitive with much larger proprietary models on many benchmarks.

gemma-4-26b is an open-weights model with no direct API cost through Google. Self-hosted deployments incur GPU compute costs depending on the hardware used.

Its 256K-token context window is in the top tier for production models, enabling full-document analysis, long multi-turn conversations, and large codebase understanding without chunking. Median latency (P50 time-to-first-token) is approximately 250ms — fast enough for real-time interactive applications. Throughput is approximately 200 tokens/second — fast enough to stream responses at reading speed with headroom for concurrent requests. Maximum output length is 8K tokens — adequate for most generation tasks including essays, code functions, and detailed responses.

gemma-4-26b is best suited to cost, self-hosted. Supported capabilities include vision inputs (images alongside text), function calling / tool use for agentic workflows, streaming token output for real-time UIs. Vision support enables document analysis, screenshot understanding, diagram interpretation, and visual question answering — images are converted to tokens at the standard input rate based on resolution. Function calling support makes it suitable for building AI agents, structured data extraction pipelines, and any workflow requiring the model to invoke external APIs or return typed JSON. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how gemma-4-26b compares to similar models across these dimensions.

gemma-4-26b sits in the budget tier — the most cost-efficient models, best for high-volume simple tasks, classification, extraction, and applications where cost per call is the primary constraint. As an open-weights model, gemma-4-26b can be self-hosted on GPU compute (see the GPU rental comparison on this site) to eliminate per-token API costs entirely — the break-even point versus API pricing depends on your volume and the GPU hourly rate. When comparing gemma-4-26b to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (73/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.