Openintelligent123/gpt-oss-20b

TEXT GENERATIONPricing:Input $0.3 / Output $1.2Concurrent Unit Cost:1Model Size:20BQuant:FP8Context Size:32kPublished:Sep 1, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Openintelligent123/gpt-oss-20b is a 21 billion parameter open-weight language model from OpenAI's gpt-oss series, designed for powerful reasoning and agentic tasks. It features configurable reasoning effort, full chain-of-thought access, and native capabilities for function calling, web browsing, and Python code execution. This model is optimized for lower latency and specialized use cases, running efficiently within 16GB of memory.

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OpenAI gpt-oss-20b: Open-Weight Model for Reasoning and Agentic Tasks

Openintelligent123/gpt-oss-20b is a 21 billion parameter model from OpenAI's gpt-oss series, designed for robust reasoning and agentic applications. It is released under a permissive Apache 2.0 license, allowing for broad experimentation, customization, and commercial deployment. The model was trained using OpenAI's harmony response format and requires its use for correct functionality.

Key Capabilities and Features

  • Configurable Reasoning Effort: Users can adjust the reasoning level (low, medium, high) to balance response speed and detail based on specific task requirements.
  • Full Chain-of-Thought Access: Provides complete visibility into the model's reasoning process, aiding in debugging and increasing trust in outputs.
  • Agentic Capabilities: Includes native support for function calling, web browsing, Python code execution, and structured outputs.
  • Fine-tunable: The model is fully customizable through parameter fine-tuning, with gpt-oss-20b being suitable for fine-tuning on consumer hardware.
  • MXFP4 Quantization: Post-trained with MXFP4 quantization of MoE weights, enabling gpt-oss-20b to run efficiently within 16GB of memory.

Use Cases and Deployment

This model is particularly suited for lower latency and specialized applications. It can be deployed using various frameworks including Transformers, vLLM, PyTorch/Triton, Ollama, and LM Studio. The ability to adjust reasoning levels makes it adaptable for tasks ranging from general dialogue to deep analytical processing. Its agentic features make it ideal for automating complex workflows involving external tools.

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