GPT-OSS 20B vs Claude 3 Opus

• 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.
• 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.
• Adjustable reasoning effort: you can trade latency vs quality by tuning “reasoning effort” levels.
• Efficient hardware requirements (for its class): designed to run on a single 16 GB-class GPU or optimized local deployments for lower latency applications.
• 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.
• Flexibility: since weights are available, you can self-host, fine-tune, or deploy offline, giving more control than closed API models.

1. Intelligence & Reasoning

• Highest capability in the Claude 3 family
• Near-human comprehension and fluency
• Excels at MMLU, GPQA, GSM8K, advanced reasoning tasks

2. Complex Problem Solving

• Best for research, strategy, multi-step planning
• Handles ambiguous, open-ended tasks with ease

3. Vision & Multimodal Capabilities

• Strong chart/graph understanding
• Processes documents, technical diagrams, and dense visual data

4. Recall & Long-Context Reasoning

• Near-perfect recall (>99% on NIAH benchmark)
• Handles very large documents and multi-file workflows

5. Enterprise-Grade Accuracy

• Significantly reduced hallucinations
• High correctness rate for factual queries