OpenAI's reasoning flagship for complex professional work, long-running agents, and cross-language software engineering.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where GPT-5.2 Pulls Ahead: Abstract Reasoning and Cross-Language Coding
Benchmarks
GPT-5.2 Benchmarks: Reasoning, Math, and Software Engineering
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"model": "openai/gpt-5.2",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
headers = {
"Authorization": "Bearer AnyAPI_API_KEY",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)
const options = {
method: 'POST',
headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: JSON.stringify({model: 'openai/gpt-5.2', messages: [{role: 'user', content: 'Hello'}]})
};
fetch('https://api.anyapi.ai/v1/chat/completions', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '
{
"model": "openai/gpt-5.2",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
'
Comparison
GPT-5.2 vs GPT-5.1: What Actually Changes for Developers
GPT-5.2 is the direct successor to GPT-5.1 within OpenAI's GPT-5 family, sharing the same reasoning-model design and API surface. Both expose graded reasoning effort, tool calling, and structured outputs. The practical decision is whether the measured capability jump justifies higher per-token pricing. Independent reporting puts GPT-5.2 Thinking at 92.4% on GPQA Diamond, a 4.3-point gain over GPT-5.1, and around 80% on SWE-Bench Verified versus roughly 76% for GPT-5.1. GPT-5.2 also improves vision accuracy on charts and interfaces, cutting reported error rates substantially on those tasks.
Choose GPT-5.2 when you need the strongest reasoning, cross-language software engineering, or improved chart and interface vision, and when higher answer quality justifies a higher per-token price. Its gains are clearest on hard math, abstract reasoning, and multi-file coding. Choose GPT-5.1 when your workloads are simpler, cost sensitivity is higher, and the extra reasoning depth of GPT-5.2 would not measurably change output quality. For high-volume, latency-tight production paths where GPT-5.1 already meets accuracy targets, the upgrade may not pay off per task.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Autonomous coding agents
GPT-5.2 delivers state-of-the-art agentic coding performance according to partners like Cognition, Warp, and JetBrains, with a reported 55.6% on SWE-Bench Pro across four languages and near-80% on SWE-Bench Verified. Its 400K context and /compact endpoint support long-horizon work like refactors and migrations. Best suited to agents that plan and execute multi-file changes; expect meaningful reasoning latency per step at higher effort.
Technical debugging and code review
The model's strength in complex debugging and multi-file architecture makes it a fit for reviewing large repositories and diagnosing cross-file issues. Its large context lets it hold entire modules alongside tests and documentation, and cached inputs reduce cost when querying the same codebase repeatedly. Use higher reasoning effort for genuinely hard bugs where an accurate root-cause analysis outweighs response time.
Structured professional knowledge work
GPT-5.2 targets well-specified knowledge-work tasks such as spreadsheets, presentations, and document analysis. OpenAI reports it beats or ties human experts 70.9% of the time on the GDPval benchmark across 44 occupations—an internal metric not yet independently validated, so treat it as directional. It fits analytical deliverables where structured reasoning and quality justify added latency and cost.
Chart and interface vision analysis
GPT-5.2 Thinking is OpenAI's strongest vision model to date, roughly halving reported error rates on chart reasoning and software interface understanding and reaching 88.7% on CharXiv. This suits workflows that extract data from dashboards, product screenshots, technical diagrams, and visual reports in finance, operations, and engineering. Note that input is text and image only—there is no native audio or video understanding at parity with fully multimodal models.