Name: Dola-Seed 2.0 Pro API
Brand: ByteDance

Overview

What Is Dola Seed 2.0 Pro?

Seed 2.0 Pro is the top-tier variant in ByteDance's Seed 2.0 series, accessible globally through the BytePlus platform as "Dola." It's not a general-purpose chatbot, it's an agent model, engineered specifically to plan, reason, and complete multi-step tasks with minimal human handholding.

‍The key distinction: Seed 2.0 Pro is designed to serve as the brain and execution engine for enterprise AI agents, not just a model that generates insights, but one that carries tasks through to completion across digital environments.

API Pricing

Input: $0.65
Output: $3.9

Core Capabilities

Seed 2.0 Pro isn't a single-mode model. It processes text, images, video, and documents, and it connects to live digital environments through native browser and computer use.

Long-chain reasoning

Maintains coherent, multi-step logic over complex tasks without losing track of constraints or prior conclusions. Benchmarks like AIME 2025 (98.3) and IMO gold medal performance reflect this depth.

Multimodal understanding

Processes text, images, video, and documents in a unified context. Handles complex charts, extracts structured data from images, and interprets hour-long videos with temporal awareness.

Browser and computer use

Natively interacts with digital interfaces, navigating web pages, entering forms, retrieving live data, and completing tasks exactly as a human operator would, without extra tooling.

Agentic workflow execution

Optimized for OpenClaw and ReAct architectures. Acts as both analyst and executor, drafting plans, calling tools, managing state, and completing multi-step enterprise workflows end-to-end.

Instruction-following

Achieves top-tier performance on complex instruction-following benchmarks. Handles layered, conditional instructions reliably without drifting from specified constraints.

Code generation

A 3020 Codeforces rating places it in competitive programmer territory. Can generate, debug, and review production code across major languages with strong contextual awareness of full codebases.

Context window and memory

The 256K token context window is already large by industry standards, but when deployed in agentic frameworks like KiloClaw or OpenClaw, the effective memory extends further. The model's strong performance on filesystem navigation means it can read, write, and update memory files on disk — effectively turning a fixed context limit into persistent project memory across sessions.

Video understanding

One capability that genuinely stands out: Seed 2.0 Pro can process hour-long videos and answer substantive questions about their content, motion patterns, and temporal structure. It ranked 3rd overall for vision on the LMSYS Chatbot Arena and achieved leading scores on MotionBench, which tests dynamic scene understanding.

Benchmark Performance

ByteDance published full benchmark results alongside the model card on release. Here's how Seed 2.0 Pro performs across the key evaluation categories.

Benchmark	Category	Score	Context
AIME 2025	Mathematical reasoning	98.3	Top-tier competitive math
Codeforces Rating	Competitive programming	3020	Outperforms GPT-5.2 and Gemini 3 Pro
VideoMME	Video understanding	89.5	Competitive with frontier models
MathVision	Visual math reasoning	SOTA	State-of-the-art at release
MotionBench	Temporal / motion perception	Leading	Top position in dynamic scene understanding
SWE-Bench	Real-world coding tasks	76.5	Trails Claude Opus 4.5 (80.9)
Terminal Bench	CLI agent tasks	55.8	Below GPT-5.2 (62.4)
LMSYS Chatbot Arena	Human preference (vision)	3rd overall	Top 3 for vision globally

Who Is It Actually For?

Seed 2.0 Pro is positioned squarely at enterprise teams building or running autonomous agents, not casual users or single-query tasks. Here's where it makes the most practical sense.

Deep web research

Multi-source, long-horizon research tasks that require synthesizing information across dozens of pages with structured output.

Financial analysis

Processing complex charts, financial documents, and time-series data to produce structured analytical summaries and reports.

Software development

Full codebase navigation, PR review, multi-file edits, and debugging — particularly effective via TRAE IDE integration.

Office automation

Drafting PRDs, summarizing messages, managing calendar workflows, and completing repetitive knowledge-work tasks autonomously.

Video content workflows

Analyzing long-form video, extracting key moments, and integrating with video creation pipelines, including Dola's own native video generation capabilities.

Content moderation at scale

Multimodal content review across text, image, and video with high concurrency, suited to platforms with large-scale UGC pipelines.

Document processing

Extracting structured data from complex PDFs, forms, and scanned documents, feeding downstream enterprise systems with clean, structured output.

Physical inspection

Using image and video understanding to assess product quality, infrastructure conditions, or manufacturing outputs at scale.

How Does It Compare?

A direct feature-level comparison across the four key enterprise AI models active in 2026.

Capability	Seed 2.0 Pro	GPT-5.2	Claude Opus 4.5	Gemini 3 Pro
Context window	256K	128K	200K	1M+
Native video understanding	Yes	Limited	No	Yes
Native browser / computer use	Yes	Via tools	Via tools	Via tools
AIME 2025 reasoning	98.3	Competitive	Competitive	Competitive
SWE-Bench (coding)	76.5	~74	80.9	~70
Hallucination performance	Below average	Strong	Strong	Strong

Where it genuinely excels

For agentic, multi-step enterprise workflows — the kind where you need a model to plan, execute, verify, and adapt without constant human oversight — Seed 2.0 Pro is one of the most capable systems available. Its native browser and computer use, combined with the 256K context and strong instruction-following, means it can actually complete tasks rather than just advise on them. The pricing is not a minor footnote — at this performance level, it fundamentally changes the economics of large-scale AI deployment.

Where it falls short

Real-world code generation (SWE-Bench) still trails Claude Opus 4.5, which matters for teams whose primary use case is production software work. Terminal Bench performance behind GPT-5.2 is a gap for teams building shell-level automation. And the hallucination concern is worth taking seriously for high-stakes outputs where factual accuracy is non-negotiable — financial reporting, legal drafting, medical documentation.

Who should prioritize it

If your team is building autonomous agents for enterprise operations, doing multimodal analysis at scale, or running high-volume API workflows where cost efficiency directly affects product viability — Seed 2.0 Pro deserves serious evaluation. If you primarily need best-in-class code generation or near-zero hallucination tolerance, the comparison still slightly favors Western alternatives in those specific areas.

The honest bottom line: Seed 2.0 Pro is a real frontier model, not marketing. Its strongest arguments are agentic depth, multimodal breadth, and price. Its weakest are hallucinations and terminal-level coding. For the right workload, it's one of the most compelling enterprise AI options available in 2026.

‍

Uptime and performance

Uptime (7d): 100%

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

Dola-Seed 2.0 Pro is documented for complex math and science reasoning, including advanced proofs, research assistance, and quantitative analysis. It is also suited to advanced coding tasks such as code generation, code review, large-scale refactoring, and bug fixing.

The model is documented for agent workflows, including multi-step automation, browser operation, and complex decision chains. It is also described for multimodal document analysis, including understanding complex charts and video content.

How to use Dola-Seed 2.0 Pro API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to bytedance/seed-2-0-pro.

import requests

r = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "bytedance/seed-2-0-pro",
      "messages": [
        {
          "role": "user",
          "content": "Hello!"
        }
      ]
    },
)
print(r.json())

const r = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "bytedance/seed-2-0-pro",
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }),
});
console.log(await r.json());

curl -X POST https://api.aimlapi.com/v1/chat/completions \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"bytedance/seed-2-0-pro","messages":[{"role":"user","content":"Hello!"}]}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.