Overview

GPT Oss 20B delivers exceptional reasoning capabilities within a compact 21B parameter architecture. Designed for complex problem-solving, this thinking model features a massive 131,072 token context window, allowing developers to process extensive documentation or long-form content without losing coherence. Optimized with MXFP4 quantization, it balances high performance with efficient inference speeds. Crucially for multilingual applications, the model demonstrates robust proficiency in Arabic and English, making it ideal for pipelines requiring nuanced understanding across both languages without compromising on logical depth. This specialized focus ensures high accuracy for regional linguistic tasks while maintaining global standards.

Integration is streamlined for immediate deployment via standard API endpoints. Developers can achieve a first successful call within minutes of reviewing our documentation, ensuring rapid prototyping and iteration. The model operates on a pro tier with an 8x credit multiplier, providing transparent cost management for production workloads. Whether building research pipelines or enterprise applications, GPT Oss 20B provides the reliability and linguistic flexibility needed for scalable solutions. We recommend reviewing our comparative guides alongside the 120B variant to evaluate performance benchmarks and select the optimal configuration for your specific latency and accuracy requirements. Our support team ensures seamless onboarding for all production environments.

Specifications

Display Name GPT Oss 20B

Family Gptoss

Category Thinking

Parameters 21B

Context Window 131,072 tokens

Quantization MXFP4

Min Tier Pro

Status Available

Pricing

8×

credits per token

1K 8,000 Credits

10K 80,000 Credits

100K 800,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="gpt-oss:20b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

import anthropic

client = anthropic.Anthropic(
    base_url="https://llmapi.resayil.io/v1",
    api_key="YOUR_API_KEY"
)

message = client.messages.create(
    model="gpt-oss:20b",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "gpt-oss:20b",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-oss:20b",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Processing extensive legal documents for clause extraction

Reasoning through complex mathematical problems accurately and efficiently

Understanding and refactoring large software codebases efficiently

Summarizing lengthy research papers into key points

Advanced logical reasoning for strategic decision making

In-Depth Guide

Full Guide

Complete Guide to GPT Oss 20B — LLM Resayil

GPT Oss 20B