Llama 3.3 70B Instruct vs GPT-OSS 120B on Groq
Llama 3.3 70B Instruct is an open-weight chat model you can call on this endpoint; GPT-OSS 120B on Groq is offered from Groq. Below: what each costs for three realistic workloads, the limits that apply, and when to choose which.
Groq figures are public list prices and documented limits, checked 2026-09-16 against the provider page. Inference APIs prices are read live from the price list.
- Cost: It depends on the shape of the workload: Llama 3.3 70B Instruct is cheaper for chat assistant and generation-heavy, GPT-OSS 120B on Groq for long prompts or rag.
- Limits: Groq: Free tier: 30 requests/min, 1,000 requests/day, 8,000 tokens/min, 200,000 tokens/day. Developer-tier upgrades were paused when we checked (Sep 2026). Here: no per-minute or per-day caps.
Side by side
| Llama 3.3 70B Instruct | GPT-OSS 120B on Groq | |
|---|---|---|
| Served by | Inference APIs | Groq |
| Model author | Meta | Meta |
| Model id | meta-llama/Llama-3.3-70B-Instruct-Turbo | openai/gpt-oss-120b |
| Input price | $0.20 / 1M tokens | $0.15 / 1M tokens |
| Output price | $0.50 / 1M tokens | $0.75 / 1M tokens |
| Context window | 131K tokens | 131K tokens |
| Capabilities | Chat, Tool calling, JSON mode, Multilingual | Chat, Reasoning, Tool calling, JSON mode |
| Weights | Open | Open |
| Status | Available | Available |
| Limits | No per-minute or per-day request or token caps; usage draws on a prepaid balance | Free tier: 30 requests/min, 1,000 requests/day, 8,000 tokens/min, 200,000 tokens/day. Developer-tier upgrades were paused when we checked (Sep 2026). |
| Time to first token | 0.62 s | not measured by us |
| Output speed | 98.2 tokens / s | not measured by us |
What three workloads cost
List prices applied to the same work. The cheaper side of each row is in bold.
| Workload | Llama 3.3 70B Instruct | GPT-OSS 120B on Groq | Difference |
|---|---|---|---|
| Chat assistant — 10,000 turns of 800 tokens in, 300 out | $3.10 | $3.45 | 10% |
| Long prompts or RAG — 10,000 requests of 8,000 in, 500 out | $18.50 | $15.75 | 15% |
| Generation-heavy — 10,000 requests of 500 in, 2,000 out | $11.00 | $15.75 | 30% |
It depends on the shape of the workload: Llama 3.3 70B Instruct is cheaper for chat assistant and generation-heavy, GPT-OSS 120B on Groq for long prompts or rag. Reasoning models bill their thinking as output tokens, so real output counts run higher than the visible answer; treat the generation-heavy row as a floor for them.
When to choose which
- Your prompts, evals or output formats were tuned on Llama 3.3 70B and you do not want to re-tune
- You were on Groq: the llama-3.3-70b-versatile id is accepted unchanged
- You have outgrown the free tier and cannot upgrade
- Your volume fits the free tier, or you already have a Developer-tier account
- Raw output speed is the priority; Groq's hardware is built for it
Price, context and speed are measurable; answer quality on your task is not something a table can settle. Both take the same request, so the honest test is to run your own prompts through each.
Measured speed
Medians of three streamed runs on 2026-09-17, public endpoint, a prompt that asks for about 120 words. Time to first token is the first token of any kind, reasoning included; output speed counts every generated token from that point. Reasoning models then think before the visible answer starts: Llama 3.3 70B Instruct began answering after 0.62 s at default settings, which you can shorten with the reasoning controls on each model page. Expect ±30% with time of day. We did not measure Groq and do not quote other people's numbers; read "not measured" as unknown, not slow. Method and raw numbers: speed measurements.
Switching from Groq
Both speak the OpenAI wire format. The edit is the base URL, the key and the model id; then re-run your own prompts, because it is a different model.
import os
from openai import OpenAI
# before: Groq
# client = OpenAI(base_url="https://api.groq.com/openai/v1", api_key=os.environ["GROQ_API_KEY"])
# MODEL = "openai/gpt-oss-120b"
# after: Inference APIs
client = OpenAI(base_url="https://api.inferenceapis.com/v1", api_key=os.environ["INFERENCE_API_KEY"])
MODEL = "meta-llama/Llama-3.3-70B-Instruct-Turbo"
resp = client.chat.completions.create(model=MODEL, messages=[{"role": "user", "content": "Hello"}], max_tokens=500)More detail, including Node, LangChain and LiteLLM: switching OpenAI-compatible providers.
FAQ
Which is cheaper, Llama 3.3 70B Instruct or GPT-OSS 120B on Groq?
It depends on the shape of the workload: Llama 3.3 70B Instruct is cheaper for chat assistant and generation-heavy, GPT-OSS 120B on Groq for long prompts or rag. The table above uses list prices: Llama 3.3 70B Instruct $0.20 / 1M tokens input, $0.50 / 1M tokens output; GPT-OSS 120B on Groq $0.15 / 1M tokens input, $0.75 / 1M tokens output.
Can I switch from GPT-OSS 120B on Groq to Llama 3.3 70B Instruct without rewriting code?
Yes, if you call it through an OpenAI-compatible client. Change the base URL to https://api.inferenceapis.com/v1, swap the API key, and set the model to meta-llama/Llama-3.3-70B-Instruct-Turbo. Because it is a different model, re-run your prompts and evaluations before moving production traffic.
What limits apply to GPT-OSS 120B on Groq?
Free tier: 30 requests/min, 1,000 requests/day, 8,000 tokens/min, 200,000 tokens/day. Developer-tier upgrades were paused when we checked (Sep 2026). On Inference APIs there are no per-minute or per-day request or token caps; usage draws on a prepaid balance.
Spotted a price or limit that has changed? Tell us and we will re-check the provider page.