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OpenAI has returned to its open-source roots with the release of gpt-oss-120b and gpt-oss-20b, two advanced open-weight language models designed for real-world performance at minimal cost. Trained using techniques from OpenAI's frontier systems like o3 and o4-mini, these models excel in reasoning tasks, tool use, and efficient deployment. Available under the Apache 2.0 license, they outperform similarly sized open models and are optimized for consumer hardware, making them ideal for developers, businesses, and researchers worldwide. These GPT OSS models prioritize usability across environments, supporting context lengths up to 128k tokens and text-based interactions for code generation, math solving, and external tool integration such as web search or Python execution. They also feature adjustable reasoning levels—low, medium, and high—to optimize latency versus performance.
| Model | Layers | Total Params | Active Params Per Token | Total Experts | Active Experts Per Token | Context Length |
|---|---|---|---|---|---|---|
| gpt-oss-20b | 36 | 117B | 5.1B | 128 | 4 | 128K |
| gpt-oss-20b | 24 | 21B | 3.6B | 32 | 4 | 128K |
The gpt-oss-20b stands out as a compact yet powerful model with 21 billion total parameters, activating 3.6 billion per token through a Mixture-of-Experts (MoE) architecture. It matches or exceeds OpenAI's o3-mini across key benchmarks, including competition math (AIME 2024 & 2025), general problem solving (MMLU and HLE), and health queries (HealthBench). Optimized for edge devices, it requires only 16 GB of memory and supports native MXFP4 quantization, enabling smooth runs on laptops or mobiles with inference speeds of 160-180 tokens per second. This makes gpt-oss-20b perfect for low-latency applications like local chatbots or on-device AI, while its strong few-shot function calling and chain-of-thought (CoT) reasoning enhance chatgpt oss alternatives. With Rotary Positional Embeddings (RoPE) and the open-sourced o200k_harmony tokenizer, it handles multilingual tasks efficiently, perfect for testing ideas without big gear.
In contrast, gpt-oss-120b offers robust capabilities with 117 billion total parameters, activating 5.1 billion per token via MoE and a Transformer backbone with alternating dense and sparse attention. It achieves near-parity with o4-mini on reasoning benchmarks and outperforms it in health (HealthBench), agentic evaluations (TauBench), and competition coding (Codeforces). Fitting on a single 80 GB GPU like the Nvidia H100, it leverages 4-bit quantization and grouped multi-query attention for high efficiency. Suited for enterprise workflows, gpt-oss-120b excels in complex tool use, structured outputs, and adjustable reasoning efforts, surpassing proprietary models like GPT-4o in select areas. Its architecture supports seamless integration for research or customized AI, making it a top choice for developers seeking openai gpt-oss power in scalable, cost-effective setups.
The Apache 2.0 license lets you modify, share, and use gpt-oss models for any project—personal or commercial—with no limits or fees. Unlike stricter licenses, it opens doors for developers and businesses to tweak openai gpt-oss freely, boosting innovation in fields like healthcare and finance.
Safety comes first in gpt-oss, with OpenAI's Preparedness Framework filtering out risks like CBRN threats and using advanced training to refuse harmful prompts. Even if someone tries malicious fine-tuning, tests show it stays safe below high-risk levels.
OpenAI gpt-oss shines in chain-of-thought (CoT) reasoning with adjustable levels for speed or depth, plus native tool calling for web search, Python execution, and agentic workflows. It beats benchmarks like AIME math and HealthBench, ideal for complex chatgpt oss tasks in gpt oss.
Run gpt-oss on your own hardware for full privacy—no data leaves your device, avoiding leaks or subpoenas from cloud services. This cuts deployment costs to near zero compared to openai pricing, with efficient designs fitting consumer gear like laptops (for gpt-oss-20b) or single GPUs (for gpt-oss-120b).
Head to HuggingFace for easy gpt oss download. Search for "openai/gpt-oss-20b" or "openai/gpt-oss-120b" on huggingface.co. Use the Hugging Face CLI: run huggingface-cli download openai/gpt-oss-20b in your terminal. Models come quantized for efficiency, and you can spin up a server with vLLM for testing. This community hub also offers guides for fine-tuning with Transformers.
OpenAI gpt-oss works on many platforms for local deployment, with gpt oss ollama and lm studio being popular picks for simple setups. Start with Ollama by installing the app, then pull the model using ollama pull gpt-oss:20b or ollama pull gpt-oss:120b. Chat offline with ollama run gpt-oss:20b. It runs fast on good hardware, like lightning quick on RTX cards or decent speeds around 35 tokens per second on M4 Macs. Adjust reasoning levels to fit your setup, and check Ollama's docs for custom prompts. For LM Studio, download the app and search for "gpt-oss-20b" or "gpt-oss-120b" in the discover tab. Load it up and start prompting right away. You'll get solid performance, such as 58 to 70 tokens per second on M4 Max or up to 221 on high-end GPUs like RTX 5090. It's great for low-latency tasks on edge devices—make sure to update to the latest version.
Skip the setup and try gpt oss instantly on gpt-oss.me. Our free playground lets you test gpt-oss-20b or gpt-oss-120b with adjustable reasoning and tool calls—no downloads required. It's a quick way to explore features before local integration.
| Aspect | gpt-oss-120b | Claude Opus 4.1 |
|---|---|---|
| Reasoning & Benchmarks | Near-parity with o4-mini; excels in AIME math (96.6% with tools), HealthBench, TauBench agentic tasks; matches o3-mini in MMLU/HLE. | Tops SWE-bench Verified at 74.5% (up from 72.5% in Opus 4); GPQA 79.6-83% with reasoning, TerminalBench 35.5%; outperforms GPT-4.1 in coding. |
| Tool Use & Capabilities | Native support for web search, Python execution, structured outputs, few-shot calling; adjustable reasoning levels (low/medium/high). | Excellent tool integration and multimodal support; superior in long-running code/text tasks but proprietary. |
| Safety & Ethics | Preparedness Framework with adversarial fine-tuning; observable CoT for misuse detection; $500K Red Teaming Challenge. | Prioritizes ethics with enhanced filters; edges in proprietary safeguards, including improved refusal behaviors. |
| Cost & Accessibility | Free under Apache 2.0; local runs on 80GB GPU (120b) or 16GB (20b); no API fees. | Subscription-based; API pricing applies (higher for advanced features); no open weights, cloud-dependent. |
| Deployment & Customization | Open-source weights via Hugging Face; easy fine-tuning for on-premises privacy. | Limited customization without API; newer training data (April 2025) but no local weights. |