OpenAI's balanced small model: 1M-token context, strong instruction following and tool calling at low latency and cost.
Modality:
Text
Image
PDF
model ID
openai/gpt-4.1-mini
Output Speed *
0.00
tok/s
Intelligence Index *
10.2
/ 100
Context Window *
1047576
tokens
Input price
2.4
Anytoken
Output price
9.6
Anytoken
GPT-4.1 Mini: 1M-Token Context and Reliable Tool Calling Without a Reasoning Tax
GPT-4.1 Mini is OpenAI's mid-tier model in the GPT-4.1 family, sitting between GPT-4.1 and GPT-4.1 Nano. It's a non-reasoning model built around instruction following and tool calling, with a 1M-token context window and low latency. OpenAI positions it as a significant leap in small-model performance that matches or exceeds GPT-4o on many evals while cutting latency roughly in half. It accepts text and image input and returns text. It fits interactive assistants, agent loops, and long-document workflows where predictable formatting and cost efficiency matter more than deep reasoning.
Start building with GPT-4.1 Mini through the AnyAPI.ai unified API.
Performance
Where GPT-4.1 Mini Earns Its Place: Instruction Following at Low Latency
GPT-4.1 Mini's core strength is reliable instruction following and tool calling without a reasoning step, which keeps responses fast. OpenAI reports it matches or exceeds GPT-4o on many intelligence evals while reducing latency by nearly half. Independent testing places its time to first token below the typical median for its price tier, so it reacts quickly to interactive requests. The production consequence: it works well as the default engine for chat assistants and tool-driven agent loops where you need consistent formatting and predictable behavior more than deep chain-of-thought reasoning.
Benchmarks
GPT-4.1 Mini Benchmarks: Intelligence, Instruction Following and Coding
On Artificial Analysis, GPT-4.1 Mini scores 15 on the Intelligence Index, above the median of 12 for non-reasoning models in its price tier, but it generates output at roughly 97 tokens per second, slightly below the tier median. For instruction following, independent evals report around 84.1% on IFEval and 35.8% on MultiChallenge. On coding, it reaches about 31.6% on Aider's polyglot diff benchmark. These indicate a model that follows structured, multi-step prompts reliably and handles routine coding, while genuinely hard reasoning or complex software engineering favors dedicated reasoning models.
Output Speed
*
0.00
tok/s
Intelligence Index
*
10.2
/ 100
MMLU *
Broad world knowledge and problem-solving
78
%
GPQA *
PhD-level scientific reasoning across physics, biology, chemistry.
66
%
HLE *
Adherence to multi-step structured instructions.
5
%
LiveCodeBench *
Tool-calling reliability in long agentic loops.
48
%
Technical Specifications
What the model supports
GPT-4.1 Mini accepts text and image input and returns text only; it is not multimodal on output. Its 1,047,576-token context window is the family's headline feature and dwarfs GPT-4o's 128K, making it viable for long documents and large codebases in a single request. Maximum output is capped at 32,768 tokens, so very long generations must be chunked. It is a non-reasoning model, supports function calling and JSON-schema structured outputs, and uses OpenAI's Chat Completions and Responses APIs. Knowledge cutoff is mid-2024.
Verified Specifications —
GPT-4.1 mini
*
Input modalities
Text
Image
PDF
output modalities
Text
Context window
1047576
tokens
Maximum output tokens
32768
Reasoning
No
Knowledge cutoff
April 2025
Pricing (standard)
2.4
AnyTokens in
/
9.6
AnyTokens out
Quickstart
import requests
url = "https://api.anyapi.ai/v1/chat/completions"
payload = {
"model": "gpt-4.1-mini",
"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())import requests url = "https://api.anyapi.ai/v1/chat/completions" payload = { "model": "gpt-4.1-mini", "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())
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Copy
Code is copied
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-mini","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);
}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-mini","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);
}
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Copy
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 '{
"model": "gpt-4.1-mini",
"messages": [
{
"role": "user",
"content": "Hello"
}
]
}'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-mini", "messages": [ { "role": "user", "content": "Hello" } ] }'
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Code is copied
View docs
Limitations & Trade-offs
1
No reasoning mode. GPT-4.1 Mini is explicitly a non-reasoning model with no extended-thinking step. That keeps latency low but caps performance on genuinely hard math, science, and multi-step logical problems. On complex tasks, OpenAI itself recommends starting with GPT-5 Mini or a dedicated reasoning model instead. If your workload depends on deliberate step-by-step reasoning rather than instruction following, this model is the wrong tool.
2
Not the cheapest mini option. Independent pricing comparisons place GPT-4.1 Mini's per-token cost meaningfully above GPT-4o Mini, and Artificial Analysis notes it sits at the higher end of its price tier for a non-reasoning model. For extremely high-volume, simple workloads such as classification or autocomplete where the larger context and higher intelligence aren't needed, GPT-4.1 Nano or GPT-4o Mini deliver similar results at lower cost.
3
Output length capped at 32,768 tokens. Despite the 1M-token input window, a single response cannot exceed roughly 32K output tokens. Workloads that must generate very long documents, full reports, or large code files in one pass need chunking, continuation logic, or a different model. The asymmetry between input and output limits is easy to overlook when designing long-context pipelines.
4
Below-median output speed. Independent measurement puts throughput around 97 tokens per second, slightly under the median for comparable non-reasoning models in its price tier. Its fast time to first token helps interactive feel, but for streaming very long completions or latency-sensitive high-throughput generation, faster models may complete large outputs sooner.
Best-Fit Workloads
Where this model earns its place
01
Tool-calling agents
GPT-4.1 Mini's reliable function calling and structured-output support make it a solid engine for agent loops that orchestrate external tools and APIs. Its strong instruction-following scores mean it respects tool schemas and multi-step directions consistently, and low time to first token keeps agent turns responsive. For agents that need deep independent reasoning rather than tool orchestration, a reasoning model is a better fit.
02
Long-document and codebase analysis
The 1,047,576-token context window lets GPT-4.1 Mini ingest large documents, transcripts, or entire codebases in a single request without aggressive chunking. Combined with improved long-context comprehension over GPT-4o, this suits retrieval-light summarization, cross-document extraction, and code review over large repositories. Keep the 32K output cap in mind when the task also requires very long generated output.
03
Interactive assistants and chat
Its low latency and fast time to first token make GPT-4.1 Mini well suited to interactive chat assistants and copilots where responsiveness shapes user experience. It matches or exceeds GPT-4o on many intelligence evals while responding faster, giving a good balance of quality and speed for real-time conversational products that don't require heavy reasoning.
04
Structured data extraction
With JSON-schema structured outputs and strong instruction adherence (around 84% on IFEval), GPT-4.1 Mini reliably returns schema-constrained data from unstructured text or images. This fits document parsing, field extraction, and content classification pipelines where downstream systems require predictable, well-formed JSON at production volume.
Pricing in anytokens via AnyAPI
Input
2.4
₳
Output
9.6
₳
Cache write
—
₳
Cache read
0.6
₳
Integration
01
One API integration
02
Easy model switching
03
Flexible for production
04