High-Performance Reasoning
The ai gpt 4o mini scores 82.0% on MMLU, outperforming Claude 3 Haiku and GPT-3.5 Turbo in reasoning and math.
GPT 4o Mini
OpenAI's ai gpt 4o mini is a small, high-performance model built for high-volume tasks. It offers 128k context, 16k output tokens, and native vision, providing top-tier intelligence at 60% less cost than GPT-3.5 Turbo for developers everywhere.
Esempi di utilizzo dell’API
curl --request POST "https://gptproto.com/v1/chat/completions" \
--header "Authorization: Bearer $GPTPROTO_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "gpt-4o-mini",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'Prezzi GPT 4o Mini
Chat, agenti di coding e lavoro sui documenti. Prezzi per 1M di token: input, input memorizzato nella cache e output vengono fatturati separatamente. GPTProto costa il 30% in meno rispetto alle tariffe ufficiali.
| Scenario | Listino ufficiale OpenAI | OpenRouter | GPTProto | Risparmi al mese |
|---|---|---|---|---|
| Personale10M token/mese (4.8M nella cache) | $2.04 | $2.15 | $1.43 | −$0.61≈ $7.34 / anno |
| Team100M token/mese (48M nella cache) | $20.40 | $21.52 | $14.28 | −$6.12≈ $73.44 / anno |
| Aziendale500M token/mese (240M nella cache) | $102.00 | $107.61 | $71.40 | −$30.60≈ $367.20 / anno |
Risparmia $0.00 / mese
Risparmio annuale ≈ $0.00 · pagamento a consumo
Modelli correlati
Tutti i modelli
GPT 4o MiniAttuale
OpenAI · 128K context · $0.105/M input · $0.42/M output
Bytedance · 262K context · $0.0971/M input · $0.9714/M output
Bytedance · 262K context · $0.0971/M input · $0.9714/M output
Modello
Prezzo: GPTProto
vs ufficiale
vs OpenRouter
Contesto
Stabilità
GPT 4o MiniAttuale
OpenAI
$0.10 / $0.42per 1M di token
-30%
-34%
128K
Core ai gpt 4o mini Features
The ai gpt 4o mini packs high-end capabilities into a low-latency, affordable package.
Cost-Effective Scaling
At just $0.15 per 1M input tokens, this gpt 4o mini allows for high-volume processing at a fraction of the usual cost.
Native Vision Support
This mini model isn't just for text; it understands images too. Use it for OCR and visual analysis in real-time.
Structured JSON Outputs
Use the 4o mini for 100% reliable JSON schema adherence. Perfect for building stable, agentic AI workflows.
ai gpt 4o mini Common Questions
Find expert answers about the ai gpt 4o mini model and its practical applications.
How does this ai gpt 4o mini compare to GPT-3.5 Turbo?
The ai gpt 4o mini is 60% cheaper than GPT-3.5 Turbo and significantly smarter. It scores 82.0% on MMLU compared to 70.0% for 3.5. It also adds vision support and a much larger 128k context window, making it the superior choice for modern ai applications.
Can I use this gpt 4o mini for vision-based tasks?
Yes, the gpt 4o mini natively supports image understanding. It can handle OCR, object detection, and captioning. This makes the mini model ideal for simple vision tasks like extracting data from receipts or describing user photos without needing a separate multimodal model.
What are the structured output capabilities?
This model supports OpenAI’s 'Strict' mode for JSON schemas. This ensures that the gpt 4o mini outputs match your provided schema with 100% reliability, which is critical for developers building complex API chains and automated workflows.
Is my data used to train the ai gpt 4o mini?
No. When you access the ai gpt 4o mini via our API, your data is processed under enterprise-grade privacy terms. OpenAI does not use data submitted through their API to train their underlying models, ensuring your proprietary information remains secure.
What is the context window for this 4o mini model?
The 4o mini model features a 128,000 token context window. Additionally, it supports up to 16,384 output tokens, which is four times higher than the standard GPT-4 models. This allows for processing long documents and generating extensive responses in a single pass.
How do I migrate my existing gpt apps to mini?
Migration is seamless. Simply update your API request's model parameter to 'gpt-4o-mini'. Since it uses the same chat completions schema as previous gpt models, no major code refactoring is necessary to take advantage of the lower costs and higher speed.
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