OpenAI's adaptive flagship that spends fewer reasoning tokens on easy tasks and thinks harder on complex coding and agentic work.
Output Speed
Intelligence Index
Context Window
Input price
Output price
Performance
How Adaptive Reasoning Changes GPT-5.1's Cost and Latency Profile
Benchmarks
GPT-5.1 Coding and Reasoning Benchmarks vs GPT-5
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-5.1",
"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-5.1', 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-5.1",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}
'
Comparison
GPT-5.1 vs GPT-5: What Actually Changes for Developers
GPT-5.1 is the direct successor to GPT-5 and shares its core positioning as OpenAI's flagship for coding and agentic tasks. Both expose a 400,000-token context window and configurable reasoning, and GPT-5.1 keeps GPT-5's pricing tiers unchanged. The practical decision is not about raw price. GPT-5.1 adds adaptive reasoning (with a default no-reasoning mode), 24-hour extended prompt caching, and native apply_patch and shell tools, and it edges GPT-5 on independent coding benchmarks. For most teams, migration is often as simple as changing the model name.
Choose GPT-5.1 when you want faster, more token-efficient responses on mixed workloads, stronger real-world coding, diff-based code editing via apply_patch, or 24-hour prompt caching for follow-up-heavy sessions. Choose GPT-5 when you already have production prompts tuned and validated against it and don't need the new tooling — OpenAI has stated it does not currently plan to deprecate GPT-5. If you migrate, test system prompts on staging first, since behavior and reasoning defaults differ.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Agentic coding and repository automation
GPT-5.1 is purpose-tuned for agentic coding, with native apply_patch for diff-based edits and a shell tool for running commands. This lets agents iteratively create, modify, and delete files and validate changes rather than emitting loose text suggestions. Independent SWE-bench Verified results (~76%) support its use in autonomous issue-resolution and refactoring pipelines. Run tool access in sandboxes with approval gates for effectful commands.
Latency-sensitive assistants with variable complexity
Adaptive reasoning makes GPT-5.1 a strong fit for chat and product experiences where most prompts are simple but some need real thought. With effort set to none, it answers quickly and cheaply on routine queries, then can be escalated to higher effort only when needed. This avoids paying full reasoning cost on trivial prompts while keeping frontier accuracy available for harder turns.
Follow-up-heavy sessions using prompt caching
Extended prompt caching with up to 24-hour retention makes GPT-5.1 economical for workflows that repeatedly reference the same large context — long documents, codebases, or system prompts — across many follow-up questions. Cached input is billed at a steep discount, so multi-turn document analysis and iterative coding sessions see faster responses at lower cost when the shared prefix stays stable.
Long-context document and code analysis
The 400,000-token context window supports large documents, extended histories, and sizable codebases in a single request. Combined with structured JSON outputs and improved instruction following, GPT-5.1 fits multi-file summarization and code-review pipelines that must reason across a wide span of input. Note that reasoning tokens share the 128,000-token output ceiling, so plan output length accordingly on high-effort runs.