Elite STEM Reasoning
o3 dominates math and science benchmarks, scoring 90.2% on the AIME 2024 and outperforming human experts in complex logic.
O3
o3 is OpenAI’s premier reasoning model, built for elite STEM tasks and advanced coding. With 200k context and high-effort logical thinking, o3 sets new benchmarks in math and complex problem-solving for developers on GPTProto.com.
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": "o3",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'Prezzi O3
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 10% in meno rispetto alle tariffe ufficiali.
| Scenario | Listino ufficiale OpenAI | OpenRouter | GPTProto | Risparmi al mese |
|---|---|---|---|---|
| Personale10M token/mese (4.8M nella cache) | $24.80 | $26.16 | $22.32 | −$2.48≈ $29.76 / anno |
| Team100M token/mese (48M nella cache) | $248.00 | $261.64 | $223.20 | −$24.80≈ $297.60 / anno |
| Aziendale500M token/mese (240M nella cache) | $1240.00 | $1308.20 | $1116.00 | −$124.00≈ $1488.00 / anno |
Risparmia $0.00 / mese
Risparmio annuale ≈ $0.00 · pagamento a consumo
Modelli correlati
Tutti i modelli
O3Attuale
OpenAI · 200K context · $1.8/M input · $7.2/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
O3Attuale
OpenAI
$1.80 / $7.20per 1M di token
-10%
-15%
200K
o3 Standout Capabilities
Explore the core strengths that make o3 the most advanced reasoning model for STEM, coding, and logical planning on GPTProto.com.
Grandmaster Coding Power
Rated at ~2727 ELO on Codeforces, o3 is more capable than 99% of human programmers for refactoring and testing applications.
Configurable Logic Effort
Balance speed and depth with the reasoning_effort parameter, tailoring o3 to your specific latency and cost needs.
Agentic Planning Precision
o3 offers high reliability in multi-step tool orchestration, significantly reducing failures in complex software environments.
OpenAI o3 Frequently Asked Questions
Find answers to technical questions about o3, including API integration, reasoning effort settings, and performance benchmarks for OpenAI's advanced model.
What makes o3 different from the o1 series?
o3 is a generational step up in speed and cognitive depth. While o1 was a breakthrough in reasoning, o3 achieves significantly higher benchmarks, such as 90.2% on AIME 2024. It provides deeper logical planning and faster execution for complex STEM tasks, making it the preferred choice for frontier-level engineering and scientific simulations.
How do I control o3 reasoning time and cost?
You can use the reasoning_effort parameter with values of low, medium, or high. This allows you to balance latency against logical depth. High effort is best for difficult math or competitive coding, while low effort reduces the model's thinking time and overall costs for simpler logical flows that still require o3's architectural precision.
Can o3 handle images and tool calling?
Yes, o3 is a multimodal model. It can process image inputs and generate text or code outputs based on visual data. It also supports function calling and structured JSON mode, making it an excellent choice for agentic workflows that require precise tool orchestration and multi-step planning without the high failure rates seen in earlier models.
What are the token limits for o3 on GPTProto.com?
o3 features a massive 200,000-token context window. It can generate up to 100,000 output tokens, which includes the internal, hidden reasoning tokens used for its chain-of-thought process. This large capacity allows for processing very large datasets or complex codebases in a single request without losing track of intricate logical threads.
Is data sent to o3 used for training?
No. When you access o3 through GPTProto.com's API aggregation platform, your data is sent to enterprise endpoints. Neither we nor OpenAI use this data to train foundation models, ensuring your proprietary code, research data, and sensitive financial logs remain secure and private according to enterprise-grade compliance standards.
Why did my o3 request return a 400 error?
A 400 error often occurs if the max_completion_tokens parameter is set too low. Since o3 requires a significant portion of its output tokens for its internal reasoning process (hidden chain-of-thought), you must provide a large enough budget for both the intensive thinking phase and the final response to avoid premature termination of the request.
Further Reading
Guide, confronti e aggiornamenti relativi a questo modello.
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