A powerful coding specialist from ByteDance, combining top-tier intelligence and a massive 256k context window with a unique, output-heavy pricing model.
Doubao Seed Code emerges from ByteDance's AI labs as a formidable contender in the specialized field of code generation and analysis. It distinguishes itself not just by its origin but by a potent combination of high-end intelligence and an exceptionally large context window. Scoring an impressive 57 on the Artificial Analysis Intelligence Index, it lands comfortably in the top echelon of models, significantly outperforming the average score of 36. This places it among the most capable models available for tackling complex reasoning, logic, and instruction-following tasks, particularly within the demanding domain of software development.
The model's standout feature is its massive 256,000-token context window. This vast capacity transforms the scale of problems it can address. Instead of analyzing code in small, isolated chunks, developers can feed entire applications, extensive libraries, or lengthy bug reports into a single prompt. This enables holistic analysis, complex refactoring suggestions, and deep dependency tracking that are simply not possible with smaller-context models. Furthermore, Doubao Seed Code is multimodal, accepting both text and image inputs. This opens up powerful workflows such as generating functional code from a UI mockup, interpreting architectural diagrams, or debugging visual glitches described with a screenshot.
This power is paired with a highly strategic, and somewhat unusual, pricing structure. The cost for input tokens is a very competitive $0.17 per million, ranking it among the more affordable options for feeding it large amounts of data. This encourages users to take full advantage of the 256k context window. However, the story changes on the output side, where the price is a much steeper $1.12 per million tokens. This is considerably more expensive than the average and creates a clear economic incentive: the model is optimized for tasks that require a lot of context but produce a concise, high-value answer.
A critical factor to consider alongside this pricing is the model's inherent verbosity. During our Intelligence Index evaluation, Doubao Seed Code generated 67 million tokens, more than double the average of 30 million. This tendency towards talkativeness directly conflicts with its expensive output pricing. Users must be prepared to manage this verbosity through careful prompt engineering or accept that generative tasks will be costly. Compounding these considerations is the current lack of public data on performance metrics like output speed (tokens per second) and latency (time to first token). This introduces an element of uncertainty for applications where real-time performance is critical, requiring teams to conduct their own benchmarking before committing to production deployment.
57 (12 / 134)
N/A tok/s
0.17 $ / 1M tok
1.12 $ / 1M tok
67M tokens
N/A seconds
| Spec | Details |
|---|---|
| Owner | ByteDance Seed |
| License | Proprietary |
| Model Family | Doubao |
| Context Window | 256,000 tokens |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Primary Focus | Code Generation, Analysis, and Refactoring |
| Architecture | Transformer-based |
| Training Data | Proprietary blend, assumed to include a vast corpus of code and technical documentation. |
| API Availability | Available through select API providers. |
Since Doubao Seed Code is available through various API providers, your choice of provider can impact both cost and performance. Without public benchmarks for speed and latency, initial selection often hinges on pricing, reliability, and ease of integration. We recommend testing your primary use case across multiple providers before making a long-term commitment.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Cost | Direct or Volume Providers | Providers offering pricing closest to the base model rate without significant markups are ideal for maximizing cost-efficiency. | May come with fewer features, higher latency, or less robust support compared to premium offerings. |
| Highest Performance | Specialized AI Platforms | Look for providers who advertise optimized infrastructure for ByteDance models, which may result in lower latency and higher throughput. | This performance almost always comes at a premium, with higher per-token costs or platform fees. |
| Maximum Reliability | Enterprise-Grade Providers | Providers with strong Service Level Agreements (SLAs), dedicated support channels, and a proven track record of uptime. | Typically the most expensive option, often requiring annual contracts or minimum spend commitments. |
| Easiest Integration | Aggregator Platforms | Platforms that provide a unified API for multiple models can make it simple to switch to or experiment with Doubao Seed Code. | Can introduce a small amount of latency and may have slightly higher costs than accessing the model directly. |
Note: Provider offerings and performance change frequently. The recommendations above are general guidelines. Always conduct your own benchmarks with realistic workloads to validate performance and cost for your specific application.
To understand the practical cost implications of Doubao Seed Code's pricing, let's model a few common scenarios. These examples highlight how the balance between cheap input and expensive output plays out in different situations, demonstrating where the model is most and least cost-effective.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Codebase Refactoring Analysis | 150k tokens (code files) | 15k tokens (suggestions) | A core strength, leveraging the large context window for a high-value, concise output. | ~$0.042 |
| Chatbot-style Code Helper | 20k tokens (history) | 20k tokens (new code) | A balanced, multi-turn conversation where output costs begin to dominate. | ~$0.026 |
| Verbose Documentation Generation | 5k tokens (code file) | 15k tokens (full docs) | A task where high output verbosity makes the model relatively expensive. | ~$0.018 |
| Image-to-Code UI Generation | ~1.5k tokens (image + prompt) | 8k tokens (HTML/CSS) | A multimodal task where the generated code output is the main cost driver. | ~$0.009 |
The model is most cost-effective for input-heavy, output-light tasks like analysis or summarization of large codebases. Conversational or generative tasks, where the output token count is high, can become significantly more expensive than competitors with more balanced pricing.
Given Doubao Seed Code's unique pricing structure—cheap inputs, expensive outputs—a specific playbook is required to manage costs effectively. The primary goal is to control the number of tokens the model generates without sacrificing the quality of the response. The following strategies are essential for any team deploying this model at scale.
Your prompt is the primary tool for controlling output length. Be explicit in your instructions to keep responses concise.
The model's cheap input cost and large context window should be used to your advantage. Avoid chatty, back-and-forth interactions that rack up expensive output tokens with each turn.
Always use the max_tokens parameter in your API calls as a hard safety net. This is the most reliable way to prevent runaway generation and cap the maximum cost of any single request.
max_tokens accordingly. This prevents the model's natural verbosity from generating unexpectedly long and expensive responses.For some workflows, you can pair Doubao Seed Code with a cheaper, faster model to optimize costs. This is particularly effective when you need both high-quality generation and concise final output.
Doubao Seed Code is a large language model developed by ByteDance, the parent company of TikTok. It is specifically optimized for tasks related to software development, such as code generation, analysis, debugging, and refactoring. It is distinguished by its high intelligence score, very large 256,000-token context window, and multimodal (text and image) input capabilities.
This model is ideal for developers, engineering teams, and companies building developer tools. Its strengths are best utilized in scenarios that require understanding and reasoning over large volumes of code or technical documentation. Use cases include:
The "context window" refers to the amount of information (measured in tokens, which are roughly ¾ of a word) that the model can consider at one time. A 256,000-token context window is exceptionally large. It means you can provide the model with approximately 190,000 words of text in a single prompt. This allows it to analyze entire software projects, multiple large documents, or very long conversation histories at once, leading to more coherent and contextually-aware responses.
Doubao Seed Code has an asymmetric pricing model. Its input price ($0.17/1M tokens) is very competitive, often cheaper than models like OpenAI's GPT-4 series or Anthropic's Claude 3 family. However, its output price ($1.12/1M tokens) is on the more expensive side, comparable to or even exceeding some of these premium competitors. This makes it cost-effective for tasks with large inputs and small outputs, but expensive for tasks that generate a lot of text.
Doubao Seed Code is multimodal, meaning it can process more than just text. It accepts both text and images as input. This allows for powerful use cases where visual information is key, such as providing a screenshot of a web application and asking the model to write the code to replicate it, or feeding it an architectural diagram for analysis.
The performance of a model, including its output speed (tokens per second) and latency (time to first token), can vary depending on the provider hosting the model and the current load on their systems. Artificial Analysis has not yet completed standardized benchmarking for Doubao Seed Code across providers. For any application where performance is critical, it is essential to run your own tests to measure these metrics before deploying to production.