Seed-OSS-36B-Instruct

Craft prompts that are precise and guide the model towards concise, relevant outputs. Avoid open-ended prompts that encourage excessive verbosity unless absolutely necessary.

• Specify Output Length: Explicitly ask for summaries of a certain length (e.g., "Summarize in 3 sentences" or "Provide 5 key bullet points").
• Focus on Key Information: Instruct the model to extract only the most critical details, rather than rephrasing entire sections.
• Iterative Refinement: Use a two-step process: first, generate a draft, then prompt the model to condense or refine it.

While the 512k context window is powerful, using it fully for every request can be costly. Only include truly relevant information.

• Contextual Chunking: Break down large documents and feed the model only the most pertinent sections for a given query.
• Summarize Prior Interactions: For long conversations, summarize previous turns to reduce the input token count while retaining essential context.
• Dynamic Context Loading: Implement logic to load context dynamically based on the user's current query, rather than always sending the full history.

Since Seed-OSS-36B-Instruct can be verbose, implement post-processing to manage output length and content.

• Max Token Limits: Set strict max_tokens parameters in your API calls to prevent overly long responses.
• Post-Generation Truncation: If the model still exceeds desired length, truncate outputs programmatically on your end.
• Content Filtering: Filter out boilerplate or repetitive phrases that don't add value to the user.

For non-real-time applications, batching requests can help amortize the model's latency and improve overall efficiency, even if per-token speed is moderate.

• Group Similar Queries: Combine multiple independent prompts into a single API call if the provider supports it, or process them sequentially in a batch job.
• Asynchronous Processing: Design your application to handle responses asynchronously, allowing the model to work through a queue of requests.

Reserve Seed-OSS-36B-Instruct for tasks where its high intelligence and large context are truly indispensable. For simpler, lower-value tasks, consider more cost-effective models.

• Tiered Model Strategy: Use a smaller, faster, and cheaper model for basic queries, and escalate to Seed-OSS-36B-Instruct only for complex reasoning.
• Fine-tuning Simpler Models: If a specific, repetitive task is causing high costs, consider fine-tuning a smaller model to handle it efficiently.

Its primary strength lies in its exceptional intelligence and reasoning capabilities, scoring 52 on the Artificial Analysis Intelligence Index. This makes it highly effective for complex analytical tasks, deep understanding, and generating high-quality, detailed responses.

Seed-OSS-36B-Instruct boasts a massive 512k token context window, which is significantly larger than many comparable models. This allows it to process and generate responses based on extremely long inputs, making it ideal for document analysis, long-form content, and extended conversations.

While its intelligence is high, its median output speed of 30.1 tokens/s and latency of 2.29 seconds mean it is notably slower than many models. For applications requiring instantaneous responses or very high throughput, it might not be the most optimal choice without careful architectural considerations like batching.

Seed-OSS-36B-Instruct is on the more expensive side, with input tokens at $0.21/M and output tokens at $0.57/M. Its high verbosity also means it tends to generate more tokens, further increasing costs. Users should implement strong cost management strategies, including prompt engineering and output truncation.

The model was developed by ByteDance Seed. It is released under an open license, providing flexibility for users to deploy, fine-tune, and integrate it into their applications, subject to the specific terms of its open-source license.

To mitigate verbosity, use precise prompt engineering to specify desired output length and format (e.g., bullet points, short summaries). Implement max_tokens limits in your API calls and consider post-processing to truncate or filter unnecessary content from the model's responses.

It ranks #4 out of 84 models in the Artificial Analysis Intelligence Index, indicating it is among the top performers for intelligence. However, it ranks lower for speed and price, suggesting a trade-off between intelligence and operational efficiency.