gpt-oss-120b vs Llama 3.3 70B
Two well-known open models with opposite habits. gpt-oss-120b reasons step by step and is far stronger at maths and science; Llama 3.3 70B answers straight away and is a steadier writer. Pick by whether the task needs thinking.
Choose gpt-oss-120b if
- Maths, science and logic — 80.1% on GPQA Diamond against Llama’s 50.5%.
- Step-by-step structured tasks.
- An Apache 2.0 licence.
Choose Llama 3.3 70B if
- Drafting and editing, where answers should start immediately.
- Answering from your own documents.
- Predictable, consistent replies.
Side by side
| gpt-oss-120b | Llama 3.3 70B | |
|---|---|---|
| Made by | OpenAI | Meta |
| Released | August 2025 | December 2024 |
| Input price, per 1M tokens | A$0.2867 | A$0.5599 |
| Output price, per 1M tokens | A$1.15 | A$4.31 |
| Cached input, per 1M tokens | A$0.1433 | — |
| Batch (about half price) | No | No |
| Context window | 131K tokens | 128K tokens |
| Longest answer | 131K tokens | — |
| Reads | Text | Text |
| Thinks before answering | Always on | No |
| Open weights | Yes, Apache 2.0 | Yes, Llama 3.3 Community License |
Prices are Roar AI’s published rates, live from our price list. Specs are each lab’s own published figures. Prices are our published US dollar rates converted at A$1.47 to the dollar, our rate for October — the same rate your card is charged.
What a real job costs
Take 1,000 requests, each about 3,000 tokens in and 1,000 out — a long prompt and a page of answer.
gpt-oss-120b does the same job for 3.0× less. Models that think before answering spend extra output tokens doing it, so on hard prompts the real gap can be wider than the rates suggest.
Use both, switch any time
On Roar AI both are on the same key and the same monthly invoice, paid in your local currency. Trying the other one is a one-word change:
client.chat.completions.create(model="gpt-oss-120b", …) client.chat.completions.create(model="llama-3.3-70b", …)
Send the same prompts to both for a day and compare the answers on your own work — that settles it faster than any benchmark. Read the docs.
Try both
on one key
Send the same prompts to each and compare the answers on your own work.