Models/Chat/GPT-OSS 20B

GPT-OSS 20B API

Available Cheapest

GPT-OSS 20B is the 21B-parameter sibling of GPT-OSS 120B (3.6B active parameters, MXFP4) and the model Groq recommends in place of llama-3.1-8b-instant. It reasons before answering, calls tools and returns JSON. On our 2026-09-22 check it passed all five of our tool-call, JSON, arithmetic, code and instruction tests and streamed at about 100 tokens per second with eight concurrent requests all answered.

Try in playground

ChatReasoningTool callingJSON mode131K context

Context window

131K tokens

Modality

Text → Text

Input

$0.04 / 1M tokens

Output

$0.19 / 1M tokens

Model ID

openai/gpt-oss-20b

Quickstart

You need an API key to call this model. Create a free account or log in.

Set INFERENCE_API_KEY to your key from the API Keys page, then run:

curl https://api.inferenceapis.com/v1/chat/completions \
  -H "Authorization: Bearer $INFERENCE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-oss-20b",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello! What can you do?"}
    ]
  }'

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.inferenceapis.com/v1",
    api_key=os.environ["INFERENCE_API_KEY"],
)
response = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello! What can you do?"},
    ],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.inferenceapis.com/v1",
  apiKey: process.env.INFERENCE_API_KEY,
});
const response = await client.chat.completions.create({
  model: "openai/gpt-oss-20b",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "Hello! What can you do?" },
  ],
});
console.log(response.choices[0].message.content);

When to use it

A good fit when

You are replacing llama-3.1-8b-instant on Groq and want the small model Groq itself recommends
Classification, extraction and short assistant replies where cost per call matters more than depth

Look elsewhere when

Multi-step coding or agent work; use GPT-OSS 120B or DeepSeek V4 Flash
You need more than 131K tokens of context

What it costs in practice

Workload	Cost
Chat assistant — 10,000 turns of 800 tokens in, 300 out	$0.89
Long prompts or RAG — 10,000 requests of 8,000 in, 500 out	$4.15
Generation-heavy — 10,000 requests of 500 in, 2,000 out	$4.00

Computed from the live rates below. This is a reasoning model, and thinking is billed as output, so real output counts run above the visible answer; see controlling reasoning to keep that down. There is no subscription or minimum; new accounts start with free credit.

Endpoint

POST https://api.inferenceapis.com/v1/chat/completions

Headers Authorization: Bearer $INFERENCE_API_KEY
Content-Type: application/json

Compatible OpenAI wire format. /openai/v1/… is accepted too, so Groq-style base URLs work with only the host changed.

Model IDs and aliases

Send any of these as model; they all resolve to this model and bill at its rate.

ID	Note
`openai/gpt-oss-20b`	Canonical
`gpt-oss-20b`	Alias

Request body

Parameter	Type	Description
`model` required	string	Model ID: `openai/gpt-oss-20b` — also accepted: `gpt-oss-20b`
`messages` required	array	Conversation so far. Each item has a `role` (`system`, `user` or `assistant`) and `content`.
`max_tokens`	integer	Maximum number of tokens to generate (includes reasoning tokens — use a few hundred or more).
`temperature`	number	Sampling temperature. Higher values give more varied output.
`top_p`	number	Nucleus sampling cutoff.
`stream`	boolean	Stream the response as server-sent events. The final chunk includes `usage`.
`tools`	array	Tool definitions for function calling (OpenAI format).
`response_format`	object	`{"type": "json_object"}` for JSON mode.
`stop`	string \| array	Sequences where the model stops generating.
`seed`	integer	Seed for more reproducible sampling.

Response

200 OK · application/json

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "model": "openai/gpt-oss-20b",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hi! I can answer questions, write and edit text, help with code and more.",
        "reasoning": "The user is greeting me and asking about capabilities..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": { "prompt_tokens": 28, "completion_tokens": 17, "total_tokens": 45 }
}

Controlling reasoning

Python · openai SDK

resp = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    reasoning_effort="none",        # thinking off
    messages=[{"role": "user", "content": "Extract the invoice number from: ..."}],
    max_tokens=500,
)

Token counts are single measurements from September 17, 2026 and vary with the question; the direction is what matters.

Pricing

Input	$0.04 / 1M tokens
Output	$0.19 / 1M tokens
Billing	Pay as you go from prepaid credits. No subscription, no daily request or token cap. See all pricing.

Notes

This is a reasoning model: set max_tokens to at least a few hundred, or send reasoning_effort: "none" to keep thinking to a minimum.
The response includes a reasoning_content field alongside content.

Compare GPT-OSS 20B with…

GPT-OSS 120B DeepSeek V4 Flash DeepSeek V4.1 Flash GLM 5.3 Flash Llama 3.3 70B Instruct GLM 5.3 DeepSeek V4 Pro MiniMax M3 Kimi K2.7 Code Qwen3.8 Flash Qwen3-VL 235B GPT-OSS 120B on Groq Llama 3.3 70B on Groq GPT-4o mini GPT-4.1 mini Gemini 2.5 Flash DeepSeek V4 Flash (DeepSeek API)

Errors

Errors use the OpenAI envelope: {"error": {"message", "type", "code"}}. 401 missing or invalid key · 402 insufficient_balance · 404 model_not_found · 503 model_unavailable · 502 backend error, safe to retry. Full table with what to retry: API documentation. Errors from other providers: provider error reference.