GPT-5 vs GPT-OSS 20B

1. High reasoning capability

• Designed for intelligent reasoning across complex domains.
• Supports reasoning tokens and adjustable reasoning effort.

2. Strong coding and agentic performance

• Optimized for multi-step coding tasks, tool-use chains, and agent workflows.
• Handles complex logic, planning, and structured problem solving reliably.

3. Multimodal input

• Accepts text + image as input.
• Produces text outputs with strong instruction following.

4. Extensive tool support

• Works with Web Search, File Search, Image Generation (as a tool), Code Interpreter, MCP, and more.
• Integrated across Chat Completions, Responses API, Realtime, Assistants, Batch, Embeddings, etc.

• Open-weight / Apache 2.0 licensed: you can use, modify, and deploy freely (commercially & academically) under permissive terms.
• Large model size (≈ 21B parameters) with Mixture-of-Experts (MoE) architecture: only ~3.6B parameters active per token, yielding efficient inference. :contentReference[oaicite:1]{index=1}
• Very long context window support: up to ~128 K tokens (or ~131 K tokens per some sources) enabling in-depth reasoning, long documents, or multi-turn context. :contentReference[oaicite:2]{index=2}
• Adjustable reasoning effort: you can trade latency vs quality by tuning “reasoning effort” levels. :contentReference[oaicite:3]{index=3}
• Efficient hardware requirements (for its class): designed to run on a single 16 GB-class GPU or optimized local deployments for lower latency applications. :contentReference[oaicite:4]{index=4}
• Strong for tasks such as reasoning, tool-use, structured output, chain-of-thought debugging: because the model is open and you can inspect its chain of thought. :contentReference[oaicite:5]{index=5}
• Flexibility: since weights are available, you can self-host, fine-tune, or deploy offline, giving more control than closed API models. :contentReference[oaicite:6]{index=6}