gpt-oss-120b
OpenAI · United States · Apache 2.0
Commercial use: Yes — free for commercial use
Apache 2.0. Still current, still OpenAI's open-weight flagship, and still one of the few frontier-adjacent US models under a fully permissive licence.
A large, capable model under Apache 2.0 that only activates a small fraction of itself for each word, so it runs far faster than its size suggests.
Why it should matter to you
The catch is memory rather than speed: you must hold all of it in RAM even though only a slice is used per word. Around 65 GB, so a serious workstation or an Ultra-class Mac. Cheap to run, expensive to house — a trade worth understanding before you budget hardware.
How it connects to our work
This shape is now standard at the frontier, and it changes buying advice: for local inference, prioritise memory capacity over raw compute. A machine with a fast processor and 32 GB will not run this; a slower one with 96 GB will.
Ships natively in MXFP4, so the footprint stays around 60-65 GB regardless of further quantisation.
Advantages
- Only 5.1B active parameters per token — very fast for its capability
- Apache 2.0 from a US lab, which matters where Chinese-origin weights face procurement resistance
- Reasoning effort is a per-request setting (low/medium/high) rather than a separate model
Disadvantages
- Needs ~65 GB, so an 80 GB GPU or 96 GB+ Mac
- Heavily safety-tuned and refuses more than most open models, which is a real friction in legitimate domains like security research and clinical text
- World knowledge is thinner than its total size suggests
Reach for it when
US-jurisdiction self-hosting where an Apache 2.0 licence and a Western lab are both requirements, and for agent loops where the reasoning dial saves real money.
Where it falls down
Domains that trip its safety tuning — medical, legal and security text all draw refusals that a less-tuned model handles. Test on your actual content before committing.
Running it
80 GB GPU, or 96 GB+ unified memory. See the hardware sizing tables for how that maps to specific chips and cards, and the quantisation guide for what you give up at each bit width.
Free tiers carrying this model
| Provider | Type | The catch | Live limits |
|---|---|---|---|
| Groq | Inference host | Free-plan limits are per-model and vary widely across the catalogue. Token-per-day caps bite before request caps on long prompts. | check → |
| OpenRouter | Aggregator | Free-pool membership changes without notice — a model you built on can stop being free, and the list above will drift. Data passes through OpenRouter and then the upstream provider, so check the privacy and routing settings before sending anything sensitive. | check → |
| Cerebras Inference | Inference host | Small model catalogue compared with the aggregators. You are choosing it for speed, not selection. | check → |
| NVIDIA NIM | Inference host | It is a credit grant, not a permanent free tier. When the credits run out you are on a paid plan or self-hosting the container. | check → |
| Cloudflare Workers AI | Edge inference | The Neuron accounting makes it hard to predict how many actual requests you get, because different models consume at very different rates. | check → |
Jurisdiction: United States
Best raw capability and the deepest tooling ecosystem. For EU personal data you are relying on a transfer framework rather than on data never leaving the bloc, so check whether your DPA and your customers accept that.
Watch for: Enterprise API tiers usually promise no training on your data; consumer tiers and free tiers frequently do not. The free tier is where this bites.
Frequently asked
Can I use gpt-oss-120b commercially?
Apache 2.0. Still current, still OpenAI's open-weight flagship, and still one of the few frontier-adjacent US models under a fully permissive licence.
What hardware do I need to run gpt-oss-120b?
80 GB GPU, or 96 GB+ unified memory. Weights alone are roughly 65 GB at Q4_K_M and 63 GB at Q8_0. Ships natively in MXFP4, so the footprint stays around 60-65 GB regardless of further quantisation.. Add KV cache on top of that, which grows with your context length.
What licence is gpt-oss-120b released under?
Apache 2.0. Full commercial use, modification and redistribution. Patent grant included. The most permissive licence in common use for open-weight models.
Where can I use gpt-oss-120b for free?
Free tiers carrying it include Groq, OpenRouter, Cerebras Inference, NVIDIA NIM, Cloudflare Workers AI. Limits differ per provider and change often, so check each provider's own limits page. You can also self-host the weights, which has no rate limit at all.
Similar models
gpt-oss-20b
16 GB GPU or Mac. A strong default for local agents.
DeepSeek-V4-Flash
Hosted, or 256 GB+ unified memory at Q4. Not a laptop model.
GLM-5.3-Flash
80-96 GB at Q4_K_M. A workstation or Ultra-class Mac.
Qwen3.8 27B
24 GB GPU at Q4_K_M, or a 32 GB Mac at Q6_K.
Wiring gpt-oss-120b into something real?
We build the evaluation harness, the failover and the cost ceilings around a model like this, so it survives contact with production.
Maps to AI agent development, AI automation development and AI strategy and consulting. Or see it working: our case studies.