OpenAI · Efficient
GPT 5 Mini
A lightweight version of GPT-5 for cost-sensitive applications. Balances advanced capabilities with efficient resource usage.
- Reasoning
- Vision
- Input
- Text, Images
- Output
- Text
- Reasoning effort
- minimal · low · medium · high
- Discovery tier
- Efficient
Pricing
USD per 1M tokens- Input
- $0.25
- Output
- $2.00
- Cache read
- $0.025
Reference prices as published by the model provider. API example
Specifications
- Context window
- 400K
- Max output
- 128K
- Average output (7 days)
- 891 tokens
- Longer than 67% of 465 models
Side by side
Compare models
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Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
Intelligence Index
16.8
Tested at high reasoning effort
Better than 45% of 326 scored models
Indexes
Coding Index
15.6Composite of the coding evaluations
Better than 10% of 168 scored models
Math Index
90.7Composite of the math evaluations
Better than 88% of 118 scored models
Agentic work
AutomationBench-AA
6.5%Workflow automation, penalized for guardrail violations
Better than 31% of 173 scored models
AutomationBench-AA tasks completed
0.9%Workflows finished end to end without a guardrail violation
Better than 32% of 173 scored models
AA-Briefcasev1.1
428 EloAgentic knowledge work, rated head to head
Better than 15% of 166 scored models
GDPval-AAv2.1
766 EloEconomically valuable occupational tasks, rated head to head
Better than 24% of 187 scored models
Document reasoning
GDP.pdf
8.4%Professional reasoning over PDF documents, all-pass rate
Better than 32% of 171 scored models
AA-LCRv1.1
72.3%Reasoning across long documents
Better than 54% of 309 scored models
MLCR-AA
8.9%Long-context reasoning over medical records
Better than 32% of 121 scored models
Reasoning and knowledge
Humanity’s Last Exam
21.5%Expert-level questions across academic fields
Better than 49% of 323 scored models
GPQA Diamond
82.8%Graduate-level science questions
Better than 53% of 291 scored models
MMLU-Pro
83.7%Multi-subject knowledge and reasoning
Better than 78% of 132 scored models
IFBench
75.4%Following precise output instructions
Better than 84% of 215 scored models
Coding
Terminal-Benchv4.0
0.0%Practical software engineering in a terminal
Better than 0% of 171 scored models
SciCode
39.0%Scientific computing in Python
Better than 19% of 174 scored models
LiveCodeBench
83.8%Recent competitive programming problems
Better than 90% of 129 scored models
Math
AIME 2025
90.7%Competition mathematics, 2025 problems
Better than 88% of 118 scored models
Legacy evaluations
Terminal-Bench Hard
33.3%Earlier terminal task suite
Better than 64% of 212 scored models
τ²-Bench Telecom
68.4%Tool use in a customer-support simulation
Better than 53% of 214 scored models
AA-LCR (unversioned)
72.3%Long-context reasoning, earlier grading
Better than 54% of 309 scored models
Scores from Artificial Analysis, updated Oct 26, 2026. GPT 5 Mini was tested at high reasoning effort; compare scores measured at the same effort.Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Prompt cache hits
- 21.4%
- About $0.20 per 1M input tokens with caching
- Request success (24h)
- 100.0%
- Reliable
Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-5-mini",
"reasoning_effort": "medium",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="openai/gpt-5-mini",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)OpenAI
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