Flan T5 Base
Developed by Google, Flan T5 Base is a language model that excels at few-shot performance, achieving strong results even compared to larger models. With a context window of 512 tokens, it is capable of handling a wide range of tasks and languages, including English, Spanish, Japanese, and many others.
Input
Output
Context
1K
Max Output
0K
Parameters
247.6M
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Estimated GPU requirements for Flan T5 Base
Estimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Frequently asked questions about Flan T5 Base
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Flan T5 Base advertises a context window of 512 tokens, with a maximum output of 256 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Flan T5 Base is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Flan T5 Base accepts Text and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for Flan T5 Base is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Not on the managed pool today; Flan T5 Base is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 0.6 GB at INT8 for the weights and a default context, from the catalog's 247.6M parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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