OpenAI · Efficient
GPT-4o mini
OpenAI's most cost-efficient small model. Cheaper and smarter than GPT-3.5 (the original ChatGPT), but less performant than gpt-4o.
- Vision
- Input
- Text, Images
- Output
- Text
- Discovery tier
- Efficient
Pricing
USD per 1M tokens- Input
- $0.15
- Output
- $0.60
- Cache read
- $0.075
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 16.4K
- Average output (7 days)
- 29 tokens
- Longer than 1% of 465 models
Side by side
GPT-4o mini compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| GPT-4o miniOpenAIThis model | Mistral Small 3 24B (2501)MistralRemove | |
|---|---|---|
| Provider | OpenAI | Mistral |
| Discovery tier | Efficient | Open weights |
| Added | Jan 1, 2024 | Sep 10, 2026 |
| Limits | ||
| Context window | 128KBest | 32.77K |
| Max output | 16.38KBest | 8.19K |
| Average output | 29 tokens | 403 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.15 | $0.12Best |
| Output | $0.60 | $0.35Best |
| Cache read | $0.075 | — |
| Performance | ||
| Request success (24h) | 100.0% | — |
| Benchmark indexes | ||
| Intelligence Index | 6.7 | 6.7 |
| Coding Index | 11.4 | — |
| Math Index | 14.7Best | 4.3 |
| Capabilities | ||
| Tools | No | Yes |
| Vision | Yes | No |
| Reasoning effort | — | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- GPT-4o mini
- Mistral Small 3 24B (2501)
Intelligence Index
- GPT-4o mini6.7
- Mistral Small 3 24B (2501)6.7
GPT-4o mini: Better than 6% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- GPT-4o mini: 11.4
- Mistral Small 3 24B (2501): —
Math Index
Composite of the math evaluations
- GPT-4o mini: 14.7Top
- Mistral Small 3 24B (2501): 4.3
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- GPT-4o mini: —
- Mistral Small 3 24B (2501): 0.0%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- GPT-4o mini: 4.2%Top
- Mistral Small 3 24B (2501): 3.8%
GPQA Diamond
Graduate-level science questions
- GPT-4o mini: 42.6%
- Mistral Small 3 24B (2501): 46.2%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- GPT-4o mini: 64.8%
- Mistral Small 3 24B (2501): 65.2%Top
IFBench
Following precise output instructions
- GPT-4o mini: 31.0%Top
- Mistral Small 3 24B (2501): 26.4%
Coding
LiveCodeBench
Recent competitive programming problems
- GPT-4o mini: 23.4%
- Mistral Small 3 24B (2501): 25.2%Top
Math
AIME 2025
Competition mathematics, 2025 problems
- GPT-4o mini: 14.7%Top
- Mistral Small 3 24B (2501): 4.3%
AIME
Competition mathematics
- GPT-4o mini: 11.7%Top
- Mistral Small 3 24B (2501): 8.0%
MATH-500
Competition math problems
- GPT-4o mini: 78.9%Top
- Mistral Small 3 24B (2501): 71.5%
Legacy evaluations
τ²-Bench Telecom
Tool use in a customer-support simulation
- GPT-4o mini: —
- Mistral Small 3 24B (2501): 19.6%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- GPT-4o mini: —
- Mistral Small 3 24B (2501): 0.0%
Scores from Artificial Analysis, updated Oct 26, 2026. 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
- 0.0%
- About $0.15 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-4o-mini",
"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-4o-mini",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)OpenAI
Related models
Other OpenAI models, starting with the same family.