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GPT OSS 120B

Preview
openai/gpt-oss-120b
Try it in Playground
TOKEN SPEED
~500 TPS
Powered bygroq
INPUT
Text
OUTPUT
Text
CAPABILITIES
Tool Use, Browser Search, Code Execution, JSON Object Mode, Reasoning
OpenAI logoOpenAI
Model card

OpenAI's flagship open-weight MoE model with 120B total parameters. Designed for high-capability agentic use, it matches or surpasses proprietary models like OpenAI o4-mini on many benchmarks. With long-context reasoning, competitive math/coding performance, and robust health knowledge, it is ideal for advanced research, autonomous tools, and agentic applications.

PRICING

Input
$0.15
6.7M / $1
Output
$0.75
1.3M / $1

LIMITS

CONTEXT WINDOW
131,072
MAX OUTPUT TOKENS
32,766

QUANTIZATION

This uses Groq's TruePoint Numerics, which reduces precision only in areas that don't affect accuracy, preserving quality while delivering significant speedup over traditional approaches. Learn more here.

Key Technical Specifications

Model Architecture

Built on a Mixture-of-Experts (MoE) architecture with 120B total parameters (5.1B active per forward pass). Features 36 layers with 128 MoE experts using Top-4 routing per token. Equipped with Grouped Query Attention and rotary embeddings, using RMSNorm pre-layer normalization with 2880 residual width.

Performance Metrics

The GPT-OSS 120B model demonstrates exceptional performance across key benchmarks:
  • MMLU (General Reasoning): 90.0%
  • SWE-Bench Verified (Coding): 62.4%
  • HealthBench Realistic (Health): 57.6%
  • MMMLU (Multilingual): 81.3% average

Use Cases

Frontier-Grade Agentic Applications
Deploy for high-capability autonomous agents with advanced reasoning, tool use, and multi-step problem solving that matches proprietary model performance.
Advanced Research & Scientific Computing
Ideal for research applications requiring robust health knowledge, biosecurity analysis, and scientific reasoning with strong safety alignment.
High-Accuracy Mathematical & Coding Tasks
Excels at competitive programming, complex mathematical reasoning, and software engineering tasks with state-of-the-art benchmark performance.
Multilingual AI Assistants
Build sophisticated multilingual applications with strong performance across 81+ languages and cultural contexts.

Best Practices

  • Utilize variable reasoning modes (low, medium, high) to balance performance and latency based on your specific use case requirements.
  • Leverage the Harmony chat format with proper role hierarchy (System > Developer > User > Assistant) for optimal instruction following and safety compliance.
  • Take advantage of the model's preparedness testing for biosecurity and alignment research while respecting safety boundaries.
  • Use the full 131K context window for complex, multi-step workflows and comprehensive document analysis.
  • Structure tool definitions clearly when using web browsing, Python execution, or function calling capabilities for best results.

Get Started with GPT-OSS 120B

Experience openai/gpt-oss-120b on Groq:

shell
pip install groq
Python
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="openai/gpt-oss-120b",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)

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