What other models does ByteDance offer?

ByteDance's Seed 2.0 series covers a wide range of capability tiers, from cost-optimized edge inference to specialized coding and GUI automation. Here are some other models ByteDance supports:

• Dola Seed 2.0 Mini: ByteDance's budget-tier model, optimized for latency-sensitive, high-concurrency workloads at $0.10 input / $0.40 output per million tokens. It supports a 256K context window and scores 87.0 on AIME 2025, making it a strong fit for classification, code completion, and structured dialogue at scale

• Dola Seed 2.0 Lite: The mid-tier option in the Seed 2.0 family, balancing output quality and response speed at $0.25 input / $2.00 output per million tokens. It scores 93.0 on AIME 2025 and 87.7 on MMLU-Pro, with multimodal support and a 256K context window, making it a workhorse for production agentic and document-processing applications

• Dola Seed 2.0 Code (preview): A coding-dedicated fine-tune of the Seed 2.0 base, offering the same SWE-Bench Verified score as Pro (76.5%) at roughly 36% lower input cost. It includes fill-in-the-middle completions and multi-file refactoring support tailored for software engineering agents and developer IDE tooling

• UI-TARS-1.5-7B: A 7B-parameter vision-language agent built for GUI interaction across desktop, web, mobile, and game environments. At $0.10 input / $0.20 output per million tokens and a 128K context window, it achieves 84.8% on WebVoyager and is purpose-built for tasks that require understanding and acting on visual interfaces

How does Dola Seed 2.0 Pro differ from ByteDance's other models?

Dola Seed 2.0 Pro is ByteDance's flagship model, positioned at the top of the Seed 2.0 lineup for tasks that demand frontier-level reasoning, comprehensive multimodal understanding, and robust agentic performance.

• Benchmark scores: Pro scores 98.3 on AIME 2025, 88.9 on GPQA Diamond, 87.0 on MMLU-Pro, a Codeforces rating of 3020, 76.5% on SWE-Bench Verified, 87.8 on LiveCodeBench v6, and 89.5 on VideoMME. These are the highest scores in the Seed 2.0 family, and they place Pro in direct competition with GPT-5.2, Claude Opus 4.5, and Gemini 3 Pro

• Context window: Pro supports up to 272K tokens, slightly more than Lite's 256K and significantly more than most comparable frontier models. This is a meaningful advantage for tasks that require reasoning over very long codebases, legal documents, or multi-session conversation histories

• Pricing: At $0.47 per million input tokens and $2.37 per million output tokens, Pro costs approximately 1.9x more on input than Lite and 4.7x more than Mini. For coding-specific workloads, Dola Seed 2.0 Code delivers equivalent SWE-Bench performance at $0.30 input, making Code the more cost-efficient route for software engineering tasks

• Multimodal depth: Pro leads the Seed 2.0 family on VideoMME (89.5 vs. Lite's 87.7 and Mini's 81.2, making it the right choice when video understanding, visual reasoning over charts, or complex image-text tasks are central to the use case

Pro is the model to use when you need the highest available quality across reasoning, code, and multimodal tasks, and the per-request cost is justified by the value of the output.

What models should I consider using alongside Dola Seed 2.0 Pro?

No single model is optimal for every task. Here are models worth pairing with Dola Seed 2.0 Pro depending on what your product needs:

• Dola Seed 2.0 Mini For the high-volume, low-complexity requests in your traffic, Mini handles classification, short summarization, and code completion at approximately 4.7x lower input cost than Pro. Route the reasoning-heavy tail to Pro and everything else to Mini to keep blended costs manageable

• Dola Seed 2.0 Code (preview) For software engineering agents and IDE integrations, Code delivers the same SWE-Bench Verified score as Pro at $0.30 input / $1.20 output per million tokens. Use Code for pure coding workflows and reserve Pro for tasks that combine coding with deep reasoning or multimodal inputs

• Claude Sonnet 4.6 When your application requires particularly strong performance on nuanced long-form writing, complex instruction following, or tasks where Anthropic's constitutional training approach produces meaningfully different outputs, Claude Sonnet 4.6 is a strong cross-provider pairing. It also gives you provider redundancy at a comparable capability tier

• Gemini 3 Pro For tasks requiring a context window beyond 272K, or where Google's multimodal grounding and search integration add direct value, Gemini 3 Pro is a natural parallel route. It's also a strong fallback when ByteDance's platform has availability constraints

• UI-TARS-1.5-7B When agentic workflows require interacting with GUIs, navigating browsers, or operating mobile interfaces, UI-TARS handles the visual action layer while Pro handles the reasoning and planning layer. The two models cover complementary modalities in an end-to-end agent stack

What are the challenges of using Dola Seed 2.0 Pro in my product?

Like any production LLM, Dola Seed 2.0 Pro comes with tradeoffs worth planning for:

• Cost at scale: At $2.37 per million output tokens, Pro is one of the more expensive models in the Seed 2.0 family. In high-volume applications, output token costs accumulate quickly, particularly for agentic workflows that generate lengthy chains of reasoning. Without active cost governance, monthly spend can scale faster than anticipated

• Provider dependency: Routing all frontier-quality requests to a single ByteDance endpoint creates fragility. ByteDance's Volcano Engine and BytePlus platforms have a shorter availability track record than AWS or Azure-hosted providers, and a sustained outage without a configured fallback means complete loss of your highest-quality inference path

• Rate limits and access constraints: Pro is ByteDance's flagship model, and access at high throughput may be subject to rate limiting or queue delays during peak demand periods. Applications that need guaranteed low-latency responses for Pro-tier tasks should plan for fallback routing to Lite or a comparable external model

• Knowledge cutoff: Pro's training data has a January 2024 cutoff. For applications that require awareness of post-cutoff events, recent API documentation, or current regulatory changes, retrieval-augmented generation or real-time grounding is necessary to avoid confidently incorrect outputs

Why should I use Merge Gateway to route LLM requests with Dola Seed 2.0 Pro and every other model?

Using Dola Seed 2.0 Pro through Merge Gateway gives you access to the model itself and the infrastructure layer around it:

• Cost governance: Set hard or soft project budgets so Dola Seed 2.0 Pro spend stays within plan. At $2.37 per million output tokens, Pro's costs are significant enough that per-project attribution and budget alerts are worth configuring before you scale. Every request is tracked by model, project, and tag in a unified dashboard across all providers

• One API, every provider: Access Dola Seed 2.0 Pro and every other major LLM through a single endpoint and API key. Change providers by swapping the model string — no application code changes required

• Intelligent routing and automatic failover: Merge routes around ByteDance outages automatically. Routing policies based on cost, latency, or quality can reduce spend by 40–60% without touching your application code

• Build Your Own Router: Define what "best" means for your traffic by selecting from curated ML benchmarks or adding your own eval scores. The router scores each available model against your weights and picks the winner per request, with a plain-language explanation of every decision

• Security and compliance controls: Apply DLP rules and prompt injection protection before every request reaches ByteDance. Enforce per-project model and region policies without adding that logic to your application

How can I start routing requests to Dola Seed 2.0 Pro via Merge Gateway?

Getting Dola Seed 2.0 Pro running through Merge Gateway takes a few minutes:

1. Create an account and get your API key from the dashboard.

2. Install the Merge Gateway SDK: run pip install merge-gateway-sdk (Python) or npm install merge-gateway-sdk (Node). Alternatively, if you're already using the OpenAI SDK, set base_url = "https://api-gateway.merge.dev/v1/openai" and your existing code works as-is.

3. Make your first request using the provider/model format. For Dola Seed 2.0 Pro, the model string is tencent/dola-seed-2.0-pro. Swap the model string to route to any other provider without changing anything else.

4. Configure a routing policy in the dashboard to set failover behavior, cost limits, and optimization strategy. Your first policy can be as simple as naming Dola Seed 2.0 Pro as primary with one fallback.

Full setup instructions and SDK references are in the Merge Gateway docs.