MiniMax M3 vs GPT-OSS 120B on Groq
MiniMax M3 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: GPT-OSS 120B on Groq is cheaper in all three workloads, by 52–60%.
- 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
| MiniMax M3 | GPT-OSS 120B on Groq | |
|---|---|---|
| Served by | Inference APIs | Groq |
| Model author | MiniMax | MiniMax |
| Model id | MiniMaxAI/MiniMax-M3 | openai/gpt-oss-120b |
| Input price | $0.39 / 1M tokens | $0.15 / 1M tokens |
| Output price | $1.56 / 1M tokens | $0.75 / 1M tokens |
| Context window | 512K tokens | 131K tokens |
| Capabilities | Chat, Reasoning, Tool calling, JSON mode | 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 | 1.3 s | not measured by us |
| Output speed | 72 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 | MiniMax M3 | GPT-OSS 120B on Groq | Difference |
|---|---|---|---|
| Chat assistant — 10,000 turns of 800 tokens in, 300 out | $7.80 | $3.45 | 56% |
| Long prompts or RAG — 10,000 requests of 8,000 in, 500 out | $39.00 | $15.75 | 60% |
| Generation-heavy — 10,000 requests of 500 in, 2,000 out | $33.15 | $15.75 | 52% |
GPT-OSS 120B on Groq is cheaper in all three workloads, by 52–60%. 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
- You need more than 131K tokens of context (it takes 512K)
- You want a large-context agent model priced near the Flash tier
- You have outgrown the free tier and cannot upgrade
- Cost is the deciding factor; it is cheaper in every workload above
- 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: MiniMax M3 began answering after 2.74 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 = "MiniMaxAI/MiniMax-M3"
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, MiniMax M3 or GPT-OSS 120B on Groq?
GPT-OSS 120B on Groq is cheaper in all three workloads, by 52–60%. The table above uses list prices: MiniMax M3 $0.39 / 1M tokens input, $1.56 / 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 MiniMax M3 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 MiniMaxAI/MiniMax-M3. 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.
Why does MiniMax M3 use more output tokens than the visible answer?
It is a reasoning model: it thinks before it answers and the thinking is billed as output tokens. Budget for that in output-heavy workloads, and set max_tokens to a few hundred or more so the answer is not cut off.
Spotted a price or limit that has changed? Tell us and we will re-check the provider page.