GPT-OSS 20B vs Claude 4.1 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. :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}

1. Advanced Coding Performance

• Achieves 74.5% on SWE-bench Verified, improving the Claude family's state-of-the-art coding abilities.

• Stronger at:

  • Multi-file code refactoring
  • Large codebase debugging
  • Pinpointing exact corrections without unnecessary edits

• Outperforms Opus 4 and shows gains comparable to jumps seen in past major releases.

2. Improved Agentic & Research Capabilities

• Better at maintaining detail accuracy in long research tasks.
• Enhanced agentic search and step-by-step problem solving.
• Performs reliably across complex multi-turn reasoning tasks.

3. Validated by Real-World Users

• GitHub: Better multi-file refactoring and code adjustments.
• Rakuten Group: High precision debugging with minimal collateral changes.
• Windsurf: One standard deviation improvement on their junior dev benchmark—similar magnitude to Sonnet 3.7 → Sonnet 4.

4. Hybrid-Reasoning Benchmark Improvements

• Improvements across TAU-bench, GPQA Diamond, MMMLU, MMMU, AIME (with extended thinking).
• Stronger robustness in long-context reasoning tasks.