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Dola-Seed 2.0 Lite
It covers roughly 95% of enterprise tasks at about half the cost of the flagship Pro variant — without sacrificing the multimodal depth that serious applications actually need.
Dola-Seed 2.0 Lite is ByteDance's mid-tier general-purpose AI model built for real production workloads.
Most teams don't need the absolute ceiling of AI performance, they need a model that's fast, affordable, and genuinely capable across a wide variety of real tasks. Seed 2.0 Lite is ByteDance's answer to that gap.
Seed 2.0 Lite scores 93.0 on AIME 2025, just 5 points behind the Pro variant. For most engineering and research workflows, the gap simply isn't perceptible in day-to-day use.
The model accepts text, images, and video as inputs natively, not as bolted-on adapters. This makes it suited for tasks like chart interpretation, visual QA, and document layout extraction out of the box.
Tool calling and function invocation are first-class features, not afterthoughts. Lite handles multi-step instruction chains reliably, making it a strong backbone for autonomous agents and automated pipelines.
A 256K-token context window (expandable to 262K) means roughly 524 pages of text in a single call. Complex documents, lengthy codebases, or multi-chapter reports no longer need to be chunked manually.
Seed 2.0 Lite records a best-case TTFT (Time to First Token) of around 621ms. For interactive assistants and real-time agent loops, that kind of responsiveness changes what's actually deployable in production.
The Seed 2.0 family (led by the Pro Preview) ranked 6th overall on the LMSYS Chatbot Arena for text tasks and 3rd for vision tasks, among the highest positions ever achieved by a non-Western model at launch. Lite, as the production-aligned sibling, inherits much of the same architecture and training pipeline.
ByteDance itself acknowledges a few areas where the Seed 2.0 family still trails the leading Western models. The Pro version lags behind Claude Opus 4.5 on SWE-Bench (76.5 vs 80.9) and falls short of GPT-5.2 on Terminal Bench and hallucination control. Lite, being a step down from Pro, will reflect some of these same characteristics. For heavy-duty code generation or scenarios where hallucination rates are a critical risk, it's worth running your own comparative tests before committing at scale.
Because Lite targets the 95% of real-world enterprise tasks that don't require frontier-level reasoning, its use case profile is deliberately broad. These are the scenarios where it earns its reputation.
Parsing tables from PDFs, extracting structured data from invoices, converting dense regulatory filings into clean JSON — Lite handles complex unstructured-to-structured workflows reliably. Its performance on OmniDocBench 1.5 and DUDE confirms this is a genuine strength, not marketing.
Whether you're building a research agent that searches, reads, and synthesizes across sources, or an automated code reviewer that opens PRs and checks diffs, Lite's native tool-calling support and long context make the architecture straightforward. It handles sustained instruction chains without degrading midway through complex tasks.
A Codeforces rating of 2233 and a 73.5 on SWE-Bench put Lite in the top tier for code comprehension. It reads large codebases in context, identifies logical flaws, rewrites modules, and explains the reasoning — all in one pass. For teams without a dedicated AI code model, Lite is often sufficient.
Charts, screenshots, diagrams, product images, and video frames — Lite processes these natively alongside text. This enables analytics dashboards that describe graphs in plain language, UI testing agents that verify layouts, and e-commerce tools that extract product attributes from images.
The MMLU-Pro score of 87.7 — notably above the Pro variant's 87.0 on the same benchmark — reflects a broad, deep knowledge base. Lite handles literature reviews, competitive research briefs, technical summaries, and cross-domain synthesis with a degree of accuracy that justifies trust in production.
What is the difference between Dola-Seed 2.0 Lite and Seed 2.0 Pro?
Seed 2.0 Pro is ByteDance's flagship model, optimized for frontier reasoning, research-grade tasks, and competition-level performance. It scores 98.3 on AIME 2025 and achieves a 3020 Codeforces rating. Seed 2.0 Lite is the general production model, it scores 93.0 on AIME 2025, costs roughly half as much, and is specifically tuned to balance quality and speed for everyday enterprise workloads. For most applications, Lite delivers Pro-level results at significantly lower cost.
Can Seed 2.0 Lite process images and video?
Yes, multimodal input is a core capability, not an optional add-on. Seed 2.0 Lite natively accepts text, images, and video as inputs. This makes it suitable for tasks such as chart analysis, screenshot understanding, product image attribute extraction, and video content summarization. It achieves state-of-the-art results on MathVision and MotionBench, the latter measuring temporal understanding in video sequences.
How does the 256K context window compare in practice?
A 256K context window corresponds to roughly 524 pages of text in a single API call. This makes Seed 2.0 Lite particularly effective for long-form document processing, full codebase reviews, extended research synthesis, and agentic tasks that need to maintain awareness across long interaction histories. For comparison, many competing mid-tier models still cap out at 128K tokens, requiring manual chunking for large documents.
Most teams don't need the absolute ceiling of AI performance, they need a model that's fast, affordable, and genuinely capable across a wide variety of real tasks. Seed 2.0 Lite is ByteDance's answer to that gap.
Seed 2.0 Lite scores 93.0 on AIME 2025, just 5 points behind the Pro variant. For most engineering and research workflows, the gap simply isn't perceptible in day-to-day use.
The model accepts text, images, and video as inputs natively, not as bolted-on adapters. This makes it suited for tasks like chart interpretation, visual QA, and document layout extraction out of the box.
Tool calling and function invocation are first-class features, not afterthoughts. Lite handles multi-step instruction chains reliably, making it a strong backbone for autonomous agents and automated pipelines.
A 256K-token context window (expandable to 262K) means roughly 524 pages of text in a single call. Complex documents, lengthy codebases, or multi-chapter reports no longer need to be chunked manually.
Seed 2.0 Lite records a best-case TTFT (Time to First Token) of around 621ms. For interactive assistants and real-time agent loops, that kind of responsiveness changes what's actually deployable in production.
The Seed 2.0 family (led by the Pro Preview) ranked 6th overall on the LMSYS Chatbot Arena for text tasks and 3rd for vision tasks, among the highest positions ever achieved by a non-Western model at launch. Lite, as the production-aligned sibling, inherits much of the same architecture and training pipeline.
ByteDance itself acknowledges a few areas where the Seed 2.0 family still trails the leading Western models. The Pro version lags behind Claude Opus 4.5 on SWE-Bench (76.5 vs 80.9) and falls short of GPT-5.2 on Terminal Bench and hallucination control. Lite, being a step down from Pro, will reflect some of these same characteristics. For heavy-duty code generation or scenarios where hallucination rates are a critical risk, it's worth running your own comparative tests before committing at scale.
Because Lite targets the 95% of real-world enterprise tasks that don't require frontier-level reasoning, its use case profile is deliberately broad. These are the scenarios where it earns its reputation.
Parsing tables from PDFs, extracting structured data from invoices, converting dense regulatory filings into clean JSON — Lite handles complex unstructured-to-structured workflows reliably. Its performance on OmniDocBench 1.5 and DUDE confirms this is a genuine strength, not marketing.
Whether you're building a research agent that searches, reads, and synthesizes across sources, or an automated code reviewer that opens PRs and checks diffs, Lite's native tool-calling support and long context make the architecture straightforward. It handles sustained instruction chains without degrading midway through complex tasks.
A Codeforces rating of 2233 and a 73.5 on SWE-Bench put Lite in the top tier for code comprehension. It reads large codebases in context, identifies logical flaws, rewrites modules, and explains the reasoning — all in one pass. For teams without a dedicated AI code model, Lite is often sufficient.
Charts, screenshots, diagrams, product images, and video frames — Lite processes these natively alongside text. This enables analytics dashboards that describe graphs in plain language, UI testing agents that verify layouts, and e-commerce tools that extract product attributes from images.
The MMLU-Pro score of 87.7 — notably above the Pro variant's 87.0 on the same benchmark — reflects a broad, deep knowledge base. Lite handles literature reviews, competitive research briefs, technical summaries, and cross-domain synthesis with a degree of accuracy that justifies trust in production.
What is the difference between Dola-Seed 2.0 Lite and Seed 2.0 Pro?
Seed 2.0 Pro is ByteDance's flagship model, optimized for frontier reasoning, research-grade tasks, and competition-level performance. It scores 98.3 on AIME 2025 and achieves a 3020 Codeforces rating. Seed 2.0 Lite is the general production model, it scores 93.0 on AIME 2025, costs roughly half as much, and is specifically tuned to balance quality and speed for everyday enterprise workloads. For most applications, Lite delivers Pro-level results at significantly lower cost.
Can Seed 2.0 Lite process images and video?
Yes, multimodal input is a core capability, not an optional add-on. Seed 2.0 Lite natively accepts text, images, and video as inputs. This makes it suitable for tasks such as chart analysis, screenshot understanding, product image attribute extraction, and video content summarization. It achieves state-of-the-art results on MathVision and MotionBench, the latter measuring temporal understanding in video sequences.
How does the 256K context window compare in practice?
A 256K context window corresponds to roughly 524 pages of text in a single API call. This makes Seed 2.0 Lite particularly effective for long-form document processing, full codebase reviews, extended research synthesis, and agentic tasks that need to maintain awareness across long interaction histories. For comparison, many competing mid-tier models still cap out at 128K tokens, requiring manual chunking for large documents.
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