Multimodal Visual Logic
o4 processes diagrams and screenshots directly within its reasoning chain, reducing visual logic errors by 35% compared to earlier mini models.
O4 Mini
o4-mini is a high-speed, cost-efficient reasoning model on GPTProto.com. It bridges the gap between basic chat and frontier logic, offering native multimodal capabilities, agentic tool-use, and superior STEM performance for complex tasks.
Voorbeelden van API-gebruik
curl --request POST "https://gptproto.com/v1/chat/completions" \
--header "Authorization: Bearer $GPTPROTO_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "o4-mini",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'O4 Mini prijzen
Chatten, codeeragents en documentwerk. Geprijsd per 1 miljoen tokens — invoer, gecachte invoer en uitvoer worden afzonderlijk berekend. GPTProto is 10% goedkoper dan de officiële tarieven.
| Scenario | OpenAI prijslijst | OpenRouter | GPTProto | Je bespaart / mnd |
|---|---|---|---|---|
| Persoonlijk10M tokens / mnd (4.8M gecachet) | $13.64 | $14.39 | $12.28 | −$1.36≈ $16.37 / jr |
| Team100M tokens / mnd (48M gecachet) | $136.40 | $143.90 | $122.76 | −$13.64≈ $163.68 / jr |
| Zakelijk500M tokens / mnd (240M gecachet) | $682.00 | $719.51 | $613.80 | −$68.20≈ $818.40 / jr |
Bespaar $0.00 / maand
Jaarlijkse besparing ≈ $0.00 · betalen naar gebruik
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Model
Prijs: GPTProto
vs. officieel
vs. OpenRouter
Context
Stabiliteit
O4 MiniHuidig
OpenAI
$0.99 / $3.96per 1 miljoen tokens
-10%
-15%
200K
Core o4 Features and Capabilities
Explore the standout capabilities of o4, from native visual reasoning to agentic tool-use. These features make it the most powerful and cost-effective reasoning model in its class.
Agentic Autonomy
As the first mini model with multi-step tool use, o4 can independently browse, execute Python code, and perform recursive file analysis for complex workflows.
Elite STEM Math
With a 94% AIME score and 98.9% on MATH-500, o4 provides frontier-level logic for scientific and mathematical applications at a fraction of the cost.
Variable Reasoning Depth
Developers can use the reasoning effort parameter to adjust o4's logic depth, balancing speed and accuracy for everything from simple chat to complex math.
o4 Reasoning Model FAQ
Common questions about o4-mini's multimodal reasoning, performance benchmarks, and API integration on GPTProto.com. Learn how to optimize logic depth and costs.
What makes the o4 reasoning model unique?
The o4 model is the first in the 'mini' class to offer native multimodal reasoning and agentic autonomy. While previous versions treated images as separate inputs, o4 processes visual logic within its chain-of-thought. This results in far higher accuracy for complex STEM problems and coding tasks, all while remaining 10x more cost-effective than frontier models like o3. It effectively bridges the gap between chat and deep logic.
How does o4 handle visual logic and images?
o4 uses an integrated multimodal approach. It can analyze UI screenshots, whiteboard sketches, or architectural diagrams directly within its reasoning process. This internal visual logic reduces errors by 35% compared to models like o1-mini. It is specifically optimized for technical visual data, making it a powerhouse for visual debugging and semi-structured data extraction from complex technical manuals or invoices.
Can o4 be used for autonomous coding tasks?
Yes. o4 is highly optimized for agentic coding. It supports the Responses API, which allows it to maintain state during long-form engineering tasks. With a score of 85.9% on LiveCodeBench, o4 is capable of multi-step tool use, including executing Python code in sandboxed environments and recursive file analysis. It is ideal for building CLI tools or IDE extensions that require reasoning through entire project directories.
What is the pricing for o4 on GPTProto.com?
o4 is designed for cost-efficiency at $1.10 per 1M input tokens and $4.40 per 1M output tokens. To further reduce costs, GPTProto.com supports cache discounts of 50% for reused input tokens. Additionally, o4 tasks processed via the Batch API receive a 50% discount. This makes o4 roughly 10x cheaper than the full o3 model, providing reasoning capabilities that were previously too expensive for high-volume production.
Does o4 support structured JSON outputs?
Yes, o4 fully supports Structured Outputs via the response_format parameter. This is essential for agentic workflows where o4 must parse visual or text data into reliable JSON objects. By ensuring the model adheres to a specific schema, developers can use o4 for complex data extraction tasks, such as turning handwritten invoices or technical diagrams into structured data for downstream databases without formatting errors.
How do I migrate from o3-mini to o4?
Migrating to o4 is straightforward. You simply need to update your model parameter to 'o4-mini'. Keep in mind that o4 offers an expanded context window of 200k tokens and adds native multimodal support. If you were using o3-mini for logic, you will likely see improved STEM performance and better instruction following. You can also begin utilizing the reasoning_effort parameter to control latency and depth in your o4 requests.
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