GPT OSS 20B is OpenAI's 20.9-billion-parameter open-weight Mixture-of-Experts language model, of which roughly 3.61 billion parameters are active on any given token, giving it a sparsity profile that sits between compact dense models and the largest open MoE releases. According to an independent deployment study, this sparse activation lets the model approach the throughput of much larger dense baselines while leaving only a fraction of the weights in play per inference. The variant served on Cloudflare Workers AI under the identifier @cf/openai/gpt-oss-20b is positioned by OpenAI for powerful reasoning, agentic workflows, and versatile developer use cases, with the 20B size specifically aimed at lower latency and local or specialized deployments rather than maximum reasoning depth.
In practice, the Cloudflare-hosted release is configured as a text-to-text model with reasoning and function calling enabled, and it fits comfortably inside a 128,000-token context window. A deployment-focused benchmark on a single H100 GPU found the model delivering higher decode throughput and noticeably lower peak memory than dense 30 to 34-billion-parameter baselines at matched context lengths, with smoother behavior under bf16 precision across context lengths from a few hundred to several thousand tokens. These characteristics make it a sensible choice for teams that want an open-weight reasoning model they can route through Cloudflare's inference layer for tool-using assistants, code generation, and other agentic pipelines where moderate latency and a long context matter more than the deepest possible chain-of-thought.
