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
GPT 5 Mini compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| GPT 5 MiniOpenAIThis model | GPT 6 LunaOpenAIRemove | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Discovery tier | Efficient | Efficient |
| Added | Aug 7, 2025 | Sep 22, 2026 |
| Limits | ||
| Context window | 400K | 1.05MBest |
| Max output | 128K | 128K |
| Average output | 891 tokens | 120 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.25 | $0.10Best |
| Output | $2.00 | $0.50Best |
| Cache read | $0.025 | $0.01Best |
| Cache write | — | $0.13 |
| Performance | ||
| Throughput | — | 31.2 tok/s |
| Time to first token | — | 7.5 s |
| Request success (24h) | 100.0%Best | 99.8% |
| Benchmark indexes | ||
| Intelligence Index | 16.8 | 29.9Best |
| Coding Index | 15.6 | — |
| Math Index | 90.7 | — |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | No | Yes |
| Vision | Yes | Yes |
| Structured output | No | Yes |
| PDF input | No | Yes |
| Reasoning effort | minimal, low, medium, high | none, low, medium, high, xhigh, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- GPT 5 Mini
- GPT 6 Luna
Intelligence Index
- GPT 5 Mini16.8
- GPT 6 Luna29.9
GPT 5 Mini tested at high reasoning effort
GPT 5 Mini: Better than 45% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- GPT 5 Mini: 15.6
- GPT 6 Luna: —
Math Index
Composite of the math evaluations
- GPT 5 Mini: 90.7
- GPT 6 Luna: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- GPT 5 Mini: 6.5%
- GPT 6 Luna: 40.5%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- GPT 5 Mini: 0.9%
- GPT 6 Luna: 10.2%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- GPT 5 Mini: 428 Elo
- GPT 6 Luna: 1,097 EloTop
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- GPT 5 Mini: 766 Elo
- GPT 6 Luna: 1,263 EloTop
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- GPT 5 Mini: 8.4%
- GPT 6 Luna: 13.8%Top
AA-LCRv1.1
Reasoning across long documents
- GPT 5 Mini: 72.3%
- GPT 6 Luna: 78.3%Top
MLCR-AA
Long-context reasoning over medical records
- GPT 5 Mini: 8.9%
- GPT 6 Luna: 18.3%Top
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- GPT 5 Mini: 21.5%
- GPT 6 Luna: 28.3%Top
GPQA Diamond
Graduate-level science questions
- GPT 5 Mini: 82.8%
- GPT 6 Luna: —
MMLU-Pro
Multi-subject knowledge and reasoning
- GPT 5 Mini: 83.7%
- GPT 6 Luna: —
IFBench
Following precise output instructions
- GPT 5 Mini: 75.4%
- GPT 6 Luna: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- GPT 5 Mini: 0.0%
- GPT 6 Luna: 2.5%Top
SciCode
Scientific computing in Python
- GPT 5 Mini: 39.0%
- GPT 6 Luna: 50.9%Top
LiveCodeBench
Recent competitive programming problems
- GPT 5 Mini: 83.8%
- GPT 6 Luna: —
Math
AIME 2025
Competition mathematics, 2025 problems
- GPT 5 Mini: 90.7%
- GPT 6 Luna: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- GPT 5 Mini: 33.3%
- GPT 6 Luna: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- GPT 5 Mini: 68.4%
- GPT 6 Luna: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- GPT 5 Mini: 72.3%
- GPT 6 Luna: 78.3%Top
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
- GPT 5 Mini
- GPT 6 Luna
| Leaderboard | GPT 5 Mini | GPT 6 Luna |
|---|---|---|
| ARC-AGI-3 (Default harness) | Not in top 20 | #18 · 0.2% |
Ranks among Noumen catalog models on ARC Prize leaderboards. Only top-20 placements are listed.ARC Prize
- GPT 5 Mini
- GPT 6 Luna
| Leaderboard | GPT 5 Mini | GPT 6 Luna |
|---|---|---|
| SVG | Not in top 20 | #16 · 1250 |
Ranks among Noumen catalog models on Design Arena leaderboards. Only top-20 placements are listed.Design Arena
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
- 22.1%
- 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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