OpenAI's coding-focused GPT-4.1 pairs a 1M-token context window with reliable tool calling and low-latency, non-reasoning responses.
Output Speed
Intelligence Index
Context Window
Input price
Output price
Performance
Where GPT-4.1 Earns Its Place: Coding and Long-Context Recall
Benchmarks
GPT-4.1 in Independent Benchmarks: Speed vs. Reasoning Depth
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": "gpt-4.1",
"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.json())
const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
method: 'POST',
headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
body: '{"model":"gpt-4.1","messages":[{"role":"user","content":"Hello"}]}'
};
try {
const response = await fetch(url, options);
const data = await response.json();
console.log(data);
} catch (error) {
console.error(error);
}curl --request POST \
--url https://api.anyapi.ai/v1/chat/completions \
--header 'Authorization: Bearer AnyAPI_API_KEY' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-4.1",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'
Comparison
GPT-4.1 vs GPT-4o: What Actually Changes for Developers
GPT-4.1 is the practical successor to GPT-4o for API workloads, and the two are the most realistic head-to-head choice within OpenAI's non-reasoning lineup. Both accept text and image input and return text, both support function calling and structured outputs, and both respond without a separate reasoning step. The differences are concrete: GPT-4.1 expands the context window from 128K to roughly 1M tokens, refreshes the knowledge cutoff to June 2024, and posts materially higher coding and instruction-following scores, including a large SWE-bench Verified gain. The decision usually comes down to context size and coding reliability versus cost.
Choose GPT-4.1 when you need the 1M-token context window, stronger coding and agentic reliability, precise multi-step instruction following, or a more recent knowledge cutoff—typical of IDE tooling, codebase analysis, and long-document agents. Choose GPT-4o when your inputs comfortably fit in 128K tokens and you want a lower output cost for high-volume general chat and assistant traffic, since GPT-4o's output pricing sits below GPT-4.1's. For hard multi-step reasoning, neither is ideal—look to a dedicated reasoning model instead.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Software Engineering and IDE Tooling
GPT-4.1 is explicitly optimized for real-world coding, scoring 54.6% on SWE-bench Verified and producing more reliable code diffs than GPT-4o. Combined with the 1M-token context window, it can hold large portions of a codebase in a single request for refactoring, code maintenance, and review. Its low time to first token suits interactive assistants embedded in editors. The main constraint is the 32K output cap—large generated files may need chunking.
Long-Document Analysis and RAG
The ~1M-token context window lets GPT-4.1 ingest lengthy contracts, reports, or bundled document sets in one call, and OpenAI improved long-context comprehension over GPT-4o. It also accepts PDF files and images directly. This fits knowledge retrieval, contract review, and analysis of information-dense documents. Because accuracy can decline at extreme input lengths, validate recall at your real document sizes and consider retrieval augmentation for the largest corpora.
Tool-Calling Agents
Improved instruction following and more reliable function calling make GPT-4.1 well suited to agents that independently execute multi-step tasks. OpenAI highlights gains in following complex, multi-request instructions and selecting correct actions from ambiguous inputs. Structured JSON-schema outputs keep tool arguments well-formed. For agent loops that emphasize consistent execution and responsiveness over deep reasoning, GPT-4.1 is a strong default; for planning-heavy agents, pair or substitute a reasoning model.
Long-Context Multimodal Understanding
GPT-4.1 accepts image input alongside text and set a state-of-the-art 72.0% on Video-MME's long, no-subtitles category, a benchmark for multimodal long-context understanding. Combined with the large context window, it can analyze documents containing charts, diagrams, and mixed visual-text content at scale. Note that output is text-only—the model does not generate images—so it fits extraction and analysis rather than visual creation.