OpenAI's highest-compute o-series reasoning model, tuned for reliability on hard math, science, and coding problems.
Modality:
Text
Image
model ID
openai/o3-pro
Output Speed *
N/A
tok/s
Intelligence Index *
33.3
/ 100
Context Window *
200000
tokens
Input price
120
Anytoken
Output price
480
Anytoken
o3 Pro: Maximum-Compute Reasoning When Getting the Answer Right Matters More Than Speed
o3 Pro is OpenAI's high-compute variant of the o3 reasoning model, built to think longer and return more reliable answers on difficult problems. It shares o3's architecture but spends substantially more test-time compute per query, targeting mathematics, science, and programming where expert reviewers consistently preferred it over o3. It accepts text and image input, returns text, and runs only in the Responses API. o3 Pro fits high-stakes analytical workloads where correctness justifies latency—complex reasoning, technical planning, and verification tasks—rather than interactive, high-volume, or latency-sensitive applications.
Integrate o3 Pro through the AnyAPI.ai unified API and reserve it for your highest-stakes reasoning tasks.
Performance
Reliability Under Repeated Attempts, Not Just Single-Shot Accuracy
o3 Pro's defining strength is consistency on hard problems. OpenAI evaluates it with a "4/4 reliability" standard, counting a question correct only when answered correctly in all four attempts. On this stricter bar o3 Pro reaches 90% on AIME 2024, and reviewers consistently preferred it over o3 across science, coding, and technical categories. Because it allocates far more test-time compute per query, the model reduces variance on problems where a single wrong step invalidates the output. The production consequence: it suits verification and high-stakes analysis where a rerun would be expensive, but responses can take minutes.
Benchmarks
o3 Pro Benchmarks: Math, Science, and Competitive Coding
Reported evaluations place o3 Pro at 93% on AIME 2024 pass@1 and 90% under OpenAI's stricter 4/4 reliability measure, ahead of o3 and o1-pro. On GPQA Diamond PhD-level science it scored roughly 84%, and on Codeforces it reached an Elo of about 2748, above o3's competitive-programming rating. Artificial Analysis places o3 Pro above the average of comparable models on its Intelligence Index but notes very low output speed relative to peers. Treat these as reported and independent figures rather than a single official benchmark table; test conditions and reasoning settings vary across sources.
Output Speed
*
N/A
tok/s
Intelligence Index
*
33.3
/ 100
MMLU *
Broad world knowledge and problem-solving
0
%
GPQA *
PhD-level scientific reasoning across physics, biology, chemistry.
85
%
HLE *
Adherence to multi-step structured instructions.
0
%
LiveCodeBench *
Tool-calling reliability in long agentic loops.
0
%
Technical Specifications
What the model supports
o3 Pro provides a 200,000-token context window and up to 100,000 maximum output tokens, which include reasoning tokens. It accepts text and image input and returns text only—there is no audio, video, or image generation. Two constraints matter most in production: it runs exclusively in the Responses API, so Chat Completions integrations need reworking, and long reasoning can trigger timeouts, which is why OpenAI recommends background mode. The knowledge cutoff is June 2024, so time-sensitive facts require retrieval or tool augmentation.
Verified Specifications —
o3-pro
*
Input modalities
Text
Image
output modalities
Text
Context window
200000
tokens
Maximum output tokens
100000
Reasoning
Yes
Knowledge cutoff
June 2025
Pricing (standard)
120
AnyTokens in
/
480
AnyTokens out
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"stream": False,
"tool_choice": "auto",
"logprobs": False,
"model": "o3-pro",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.json())import requests url = "https://api.anyapi.ai/v1/chat/completions" payload = { "stream": False, "tool_choice": "auto", "logprobs": False, "model": "o3-pro", "messages": [ { "content": [ { "type": "text", "text": "Hello" }, { "image_url": { "detail": "auto", "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" }, "type": "image_url" } ], "role": "user" } ]
}
headers = { "Authorization": "Bearer AnyAPI_API_KEY", "Content-Type": "application/json"
} response = requests.post(url, json=payload, headers=headers) print(response.json())
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const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
method: 'POST',
headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"o3-pro","messages":[{"content":[{"type":"text","text":"Hello"},{"image_url":{"detail":"auto","url":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"},"type":"image_url"}],"role":"user"}]}'
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = { method: 'POST', headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'}, body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"o3-pro","messages":[{"content":[{"type":"text","text":"Hello"},{"image_url":{"detail":"auto","url":"https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"},"type":"image_url"}],"role":"user"}]}'
}; try { const response = await fetch(url, options); const data = await response.json(); console.log(data);
} catch (error) { console.error(error);
}
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Code is copied
curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"stream": false,
"tool_choice": "auto",
"logprobs": false,
"model": "o3-pro",
"messages": [
{
"content": [
{
"type": "text",
"text": "Hello"
},
{
"image_url": {
"detail": "auto",
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
},
"type": "image_url"
}
],
"role": "user"
}
]
}'curl --request POST \ --url https://api.anyapi.ai/v1/chat/completions \ --header 'Authorization: Bearer AnyAPI_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "stream": false, "tool_choice": "auto", "logprobs": false, "model": "o3-pro", "messages": [ { "content": [ { "type": "text", "text": "Hello" }, { "image_url": { "detail": "auto", "url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" }, "type": "image_url" } ], "role": "user" } ] }'
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Limitations & Trade-offs
1
High latency by design. o3 Pro allocates substantial test-time compute, and some requests take several minutes; OpenAI recommends background mode specifically to avoid timeouts. This rules it out for chat, autocomplete, or any interactive path with a user waiting. Use o3 or a smaller model when responsiveness matters, and reserve o3 Pro for asynchronous, high-stakes jobs.
2
Expensive outputs and reasoning overhead. Output pricing sits at the top of OpenAI's range, and because reasoning tokens count toward the 100,000-token output budget, a single hard query can consume many billed tokens before the visible answer appears. This makes o3 Pro poorly suited to high-volume generation. For cost-sensitive throughput, o3 or a mini model is the better economic fit.
3
Responses API only. o3 Pro is available exclusively through the Responses API, not Chat Completions. Existing Chat Completions integrations require rework, and tooling built around that endpoint will not drop in unchanged. Teams should confirm their stack targets the Responses API before committing o3 Pro to production.
4
No live retrieval and a mid-2024 knowledge cutoff. o3 Pro does not query live search in the API and has a June 2024 knowledge cutoff, so it can over-analyze when context is thin and will miss recent facts. Time-sensitive or fast-moving domains require you to supply retrieval, documents, or tool results rather than rely on the model's parametric knowledge.
Best-Fit Workloads
Where this model earns its place
01
High-stakes analytical verification
o3 Pro's 4/4 reliability design targets problems where a single wrong step invalidates the result. Its reported 90% 4/4 AIME score and reviewer preference over o3 make it well suited to checking proofs, validating complex derivations, and reviewing critical technical decisions asynchronously. The trade-off is latency—responses can take minutes—so run these as background jobs rather than interactive requests.
02
Deep technical planning from large context
The model performs best when supplied with full background—long transcripts, roadmaps, or complete specifications—within its 200,000-token window, then asked to produce a structured plan. The intelligence surfaces when context is rich rather than thin, making it a strong fit for architecture reviews and multi-step planning where a shallow prompt would underuse it.
03
Hard competitive-style coding and algorithms
With a reported Codeforces Elo around 2748, o3 Pro is strong on difficult algorithmic problem-solving and code that demands careful multi-step reasoning. It fits offline generation of complex solutions or refactors where correctness matters more than iteration speed. For fast, interactive coding loops, a lower-latency model remains the better choice.
04
Scientific and quantitative Q&A
A reported ~84% on GPQA Diamond makes o3 Pro suitable for PhD-level science and quantitative reasoning tasks where reliability is paramount. Combined with image input, it can reason over diagrams and figures. Pair it with retrieval for anything past its June 2024 knowledge cutoff, and treat outputs in regulated domains as requiring expert review.
Integration
01
One API integration
02
Easy model switching
03
Flexible for production
04