ByteDance releases Seed-OSS-36B with 512K token context window

ByteDance has released Seed-OSS-36B, a new family of open-source large language models featuring a 512,000-token context window—twice the length of OpenAI’s GPT-5. The release continues a trend of Chinese companies shipping powerful open-source AI models under permissive Apache-2.0 licensing, allowing free commercial use without API fees or licensing costs.

What you should know: The Seed-OSS-36B collection includes three variants designed for different use cases and research applications.

• Seed-OSS-36B-Base with synthetic data delivers stronger benchmark performance for general-purpose applications
• Seed-OSS-36B-Base without synthetic data provides a cleaner research baseline free from potential synthetic data bias
• Seed-OSS-36B-Instruct is post-trained for instruction following and task execution

Key technical features: The models combine familiar architecture choices with distinctive capabilities that set them apart from competitors.

• Each model contains 36 billion parameters across 64 layers with a 155,000-token vocabulary
• The 512,000-token context length can process roughly 1,600 pages of text—equivalent to the length of a Christian Bible
• A “thinking budget” feature allows developers to specify how much reasoning the model should perform before delivering answers, with budgets recommended in multiples of 512 tokens

In plain English: Think of parameters as the model’s “brain cells”—more parameters generally mean smarter responses. The context window is like the model’s working memory—how much text it can keep track of at once. The thinking budget is similar to telling someone whether to give you a quick answer or think deeply before responding.

Benchmark performance: Seed-OSS-36B achieves state-of-the-art results across multiple categories among open-source models.

• Math and reasoning: The Instruct variant scores 91.7% on AIME24 and 65 on BeyondAIME
• Coding: Records 67.4 on LiveCodeBench v6, marking another open-source state-of-the-art result
• Long-context handling: Reaches 94.6 on RULER at 128K context length, the highest reported open-source score
• Base model performance: The synthetic-data variant delivers 65.1 on MMLU-Pro and 81.7 on MATH

Deployment accessibility: ByteDance’s Seed Team emphasizes practical implementation features for developers and enterprises.

• Models integrate with Hugging Face Transformers and support 4-bit and 8-bit quantization to reduce memory requirements
• Compatible with vLLM for scalable serving, including configuration examples and API server instructions
• Includes scripts for inference, prompt customization, and tool integration to lower barriers for smaller teams

Why this matters: The release adds competitive pressure in the open-source AI landscape while providing enterprises with high-performance alternatives to proprietary models.

• Apache-2.0 licensing removes restrictive terms that often complicate enterprise adoption decisions
• The combination of strong benchmarks and flexible deployment options positions the models as viable alternatives for math-heavy, coding, and long-context workloads
• ByteDance’s Seed Team, formed in 2023, continues building foundation models that serve both research and applied commercial use cases