GPT-4.1 mini is the small model in the GPT-4.1 family that OpenAI introduced on April 14, 2025. It accepts text and images and generates text. It supports function calls and structured outputs, so it can be considered for specific functions like extracting fields, organizing notes, or querying information from multiple documents.
In 2026 it still makes sense to evaluate it for a well-defined task. According to the official OpenAI API pricing, consulted on October 30, 2026, its standard text input and output rates are lower than those of the latest flagship model GPT-6 Astra. That allows it to be included among candidates for a small function; it does not prove it is cheaper than any new model or that it reduces total work. Error review also counts. This comparison does not represent what Crazyrouter charged in the test I explain here.
I tested it by organizing documents from a fictional project through the Crazyrouter API. It could combine a change of owner with one of date and preserve a condition from the original document. However, when it found two incompatible dates, it left a status that did not reflect the need to confirm them. For an application that converts notes into tasks, both observations matter.
The data and calls are from October 30, 2026. The experiment was conducted with documents, instructions, and responses in Simplified Chinese; this article does not measure performance in Spanish.
What to Ask GPT-4.1 mini Before Automating a List
OpenAI’s documentation indicates a context window of 1,047,576 tokens, a maximum output of 32,768 tokens, and a knowledge cutoff of June 1, 2024. The large window allows gathering related sources in a single request. It does not guarantee that every detail will be interpreted correctly, nor does it equal a fixed word count.
To start, I would choose an output that can be verified: owner, deadline, and acceptance condition, with the documents supporting each task. This makes it possible to distinguish between finding a piece of data and correctly applying a modification. Asking only to “summarize these notes” leaves more work pending if what the application needs is an updated list.
Explaining code or interpreting an image are other possibilities described for the model, but they did not form part of this test. I also did not run external tools. Even if the model proposes a function call, it is the application that must execute it and control its effects.
First Call: a Note with a Known Answer
I used the OpenAI-compatible Chat Completions interface of Crazyrouter. Access via the OpenAI API also exists; the addresses in this section correspond to the channel I used. To try it, create an account or sign in, generate an API key, and check the availability of the model and current conditions. The Crazyrouter conversation model catalog serves as a reference point; it is a general catalog, not a quality measurement.
Base URL: https://api.crazyrouter.com/v1
Model: gpt-4.1-mini
API key: read your Crazyrouter key from a secure configuration
If your client allows defining an OpenAI-compatible connection, enter those values in the corresponding fields. For direct HTTP, send a POST to https://api.crazyrouter.com/v1/chat/completions, with Bearer authentication and Content-Type: application/json. If the client adds the path on its own, use only the Base URL. Do not include the key in public code or screenshots.
This body illustrates a first request with a note and its modification. It is written in Spanish to explain the procedure; it is not the original prompt from the experiment and I have not sent it to the API.
{
"model": "gpt-4.1-mini",
"messages": [
{
"role": "system",
"content": "Return a JSON object with owner, deadline, and sources. Use an ISO date. Apply only the indicated change and keep everything else. Do not invent data; use null if information is missing."
},
{
"role": "user",
"content": "Note A: Marta prepares the report for November 9, 2026. Note B, approved and current: the report date changes to November 12, 2026; the person in charge remains the same."
}
],
"temperature": 0,
"max_tokens": 6000,
"stream": false,
"response_format": { "type": "json_object" }
}
The generated text is obtained in choices[0].message.content. Before connecting it to another function, I would verify that it is not empty or truncated, that it can be interpreted as JSON, and that it keeps Marta as the owner with the date of November 12. This is the expected result of this example, not an observed response from the model.
To isolate problems, you can start with just Note A and add Note B afterward. If you do not receive content, check the connection and the service response. If you do receive content but the fields do not match the notes, you are already facing a result problem, not sufficient proof that the function is ready.
The Full Test: Deciding What Changes Are Current
In the actual experiment I prepared 51 short documents about twelve tasks. There were initial versions, approved changes, drafts, discussions, and old records that reused the same identifiers. The names and facts were fictional. Each complete request recorded 26,430 input tokens, far from the context maximum.
I asked for one row per task with id, owner, deadline, status, acceptance, sources, and conflicts. The reference time of the scenario was October 30, 2026 at 6:00 p.m. Only changes already current should be applied, fields explicitly modified should be replaced, and the rest preserved. Drafts and documents from other projects could not overwrite the current task.
I also set two rules for uncertain cases: leave null when information was missing and preserve alternatives when there were incompatible decisions without defined priority. In the second case, the status should change to “pending confirmation”.
This avoids a common simplification: treating the most recent document as if it contained the whole truth. T06 had an approved modification whose validity began on November 2. In both responses, the model kept the previous owner and the condition of returning the first 20 results, without advancing the new quantity of 50.
What It Did Resolve: a Task Split Across Three Documents
Document B01 of T01 assigned the work to Zhou Ning for November 4 and required keeping audit records for 180 days. C01 changed the date to November 9. C02 transferred responsibility to Zheng Yan and maintained the other conditions.
The model returned Zheng Yan, the date 2026-11-09, and the 180-day retention. It also included B01, C01, and C02 as sources. Both complete attempts agreed on this data.
Figure 1. Diagram I prepared with the fictional documents and the first complete response. It preserves the Chinese values and explanations in English; the text of this section explains them in Spanish. It is not a screenshot of an application. The relationships between fields and sources come from my review; the API returned the task sources as a set.
There is an exploitable function here: combining partial changes without losing what remains current. This is not just about writing a nice summary. The output contains concrete values that can be verified and then used, if they pass the necessary checks. The experiment did not include an interface that would allow automatically opening the exact quotation of each field.
What I Would Leave Pending Before Importing
T02 had two valid decisions, with dates of November 10 and 12. Neither canceled the other. GPT-4.1 mini returned deadline: null and kept both dates with their sources, but left status: "待办", which means “to do”. The required value was 待确认, “pending confirmation”.
Also, conflicts should have been an array of strings and contained a nested array. The response was still valid JSON: being able to read it with a parser did not guarantee it had the correct types or status.
Figure 2. Cases from the first saved response, with original values in Chinese and explanations in English. T03 requires requesting information; T02 requires resolving existing decisions. The suggested actions are proposals, not results of automatic correction. The incorrect status and nested array were not repaired.
Before importing a row, I would check the types, their correspondence with the sources, and consistency between fields. If the sources maintain a date in dispute, the status should not present the task as a normally defined assignment. That check requires information about the conflict, not just looking at whether deadline is empty.
T03 shows why: owner and date were null because they were not in the documents, not because there were two opposing decisions. The application should help formulate the next correct question. I did not build that screen or measure an approval process; it is a design decision that emerges from the observed responses.
What These Calls Allow Us to Conclude
I prepared the expected responses before calling the model. Then I compared the fields, sources, and structure with a program and reviewed the documents. Each of the two complete attempts obtained 47/48 main fields correct, 12/12 rows with the required sources, and 11/12 rows with the requested structure. For T02, neither met all requirements.
There was a prior test with three tasks and two complete requests on the same twelve. There were three generation calls in total, without repair requests or manual changes to the responses. These are not 24 independent tasks, nor an estimate of long-term stability.
The parameters of the complete requests were temperature=0, max_tokens=6000, stream=false, and response_format=json_object. I did not send a strict JSON Schema. Therefore, this type failure does not demonstrate a failure of strict schema enforcement. I also did not test images, tool execution, the context limit, or documents in Spanish.
Finally, the model’s lifespan must be considered. OpenAI’s notice of October 14, 2026 schedules the retirement of gpt-4.1-mini-2025-04-14 from its API for January 14, 2027. A successful call through Crazyrouter does not allow deducing its future availability schedule.
Before funding your own test, I would review the terms of use, billing, and channel availability. Then I would keep a few notes with known responses: a valid change, missing data, and an unresolved conflict. That small set allows evaluating another model without losing the function criterion. If you already convert notes into tasks, what signal do you use to prevent a pending decision from ending up as a confirmed task?