ByteDanceUI TARS 7B SFT
ByteDance's UI TARS 7B SFT is a chat model that integrates perception, reasoning, grounding, and memory within a single vision-language model, enabling end-to-end task automation. It is genuinely best at interacting with graphical user interfaces using human-like perception, reasoning, and action capabilities, with a notable strength in the WebSRC benchmark, where it achieves a score of 93.6. This model has a context window of 32,768 tokens and accepts both text and image inputs, and is made available under an open-source license.
Input
Output
Context
33K
Max Output
66K
Parameters
8.3B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Estimated GPU requirements for UI TARS 7B SFT
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 UI TARS 7B SFT
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
UI TARS 7B SFT advertises a context window of 32,768 tokens, with a maximum output of 65,537 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. UI TARS 7B SFT 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.
UI TARS 7B SFT accepts Text and Image 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 UI TARS 7B SFT 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; UI TARS 7B SFT 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 8.5 GB at INT8 for the weights and a default context, from the catalog's 8.3B 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, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Oct 14, 2026
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