LLM Reference

Seed Models by ByteDance

4 models2024–2026Up to 256K ctx

About

Seed is a family of 4 AI models by ByteDance, released between 2024 and 2026.

Current Variants

Use-when guidance is derived from seed capabilities, context, release, and replacement fields.

4 in view
Seed 1.6Current

Use when the workload needs 256K context, reasoning, and tool use.

2026-03256K contextreasoningtool use

Use when the workload needs 256K context, reasoning, and tool use.

2026-03256K contextreasoningtool use
Seed XLCurrent

Use when provider availability and model metadata match the workload.

2024-09
Seed LargeCurrent

Use when provider availability and model metadata match the workload.

2024-09

Release Timeline

2 release groups
2026-03
2 current
Seed 1.6
256K contextreasoningtool use
Current
Seed 1.6 Flash
256K contextreasoningtool use
Current
2024-09
2 current
Current
Current

Specifications(4 models)

Seed model specifications comparison
ModelReleasedContextVisionMultimodalReasoningFn CallingTool UseStructured Outputs
Seed 1.62026-03256KYesYesYesYesYesYes
Seed 1.6 Flash2026-03256KYesYesYesYesYesYes
Seed XL2024-09NoNoNoNoNoNo
Seed Large2024-09NoNoNoNoNoNo

Frequently Asked Questions

What is Seed used for?
Seed is used for vision and multimodal work, reasoning, and agent workflows and tool use. The family description and listed model capabilities point to those workloads as the best fit.
How does Seed compare to UI-TARS?
Seed by ByteDance is strongest where you need vision and multimodal work, while UI-TARS by ByteDance is the closest related family to check for agents. Seed has 4 listed variants and reaches up to 256K context, while UI-TARS reaches up to 128K context, so compare the specs and pricing tables before choosing a production model.
Which Seed model should I use?
If price is the main constraint, use the pricing table first because Seed does not have complete provider pricing in the local data. For the most capable/latest local choice, evaluate Seed 1.6 with 256K context and reasoning, tool use, function calling, structured outputs, and multimodal inputs.

Models(4)