When in the AI platform catalog there are several text models, it's easy to start with the question 'which one is stronger'. For practical work, such a question rarely helps. It is more important for the user to understand which model fits a specific task type: quick draft, search for up-to-date information, reasoning, idea validation, working with visual context, or deep analysis. This article provides a task map based on the confirmed current Neiron AI catalog without ratings or external comparisons.

Selection Principle: Task First, Then Model

Start with the result format. If you need a short answer, you don't necessarily need the model with the most complex mode. If you need a search for fresh information, web access and source verification are more important. If you need a detailed reasoning, look at models with reasoning mode. If the task involves an image in the query, consider the model's visual capabilities. This approach helps avoid arguments about names and prevents turning the catalog into a list of advertising promises.

For Neiron AI, the confirmed text models and modes from web/lib/ai/models.ts and web/lib/subscription.ts are: Gemini 3.1 Flash, Gemini 3.1 Flash with web access, Grok 4 Fast with web access, Grok 4 Fast reasoning mode, DeepSeek V4 reasoning mode, DeepSeek V4 PRO reasoning mode, GPT-5.4, GPT-5.4 with web access, Perplexity with web access, Gemini 3 Pro with web access, and Deep Research. Access conditions and limits should be checked at /pricing and in the current interface.

Quick Everyday Tasks

For short tasks, speed, clarity, and lack of unnecessary complexity are important. This could be rephrasing a paragraph, a list of ideas, a draft email, explaining a term, a plan for a short note, or checking wording. For such tasks, fast models like Gemini 3.1 Flash or Grok 4 Fast are suitable, if available to the user in the current interface.

A good query for a quick task is short but specific: 'Shorten this paragraph to three sentences', 'Give five headline options without loud evaluative claims', 'Explain the term in simple words for a beginner user'. Do not ask the model to 'make it beautiful' without criteria. The clearer the response format, the fewer unnecessary clarifications.

Tasks with Up-to-Date Information

If the task requires fresh data, use models or modes with web access. The confirmed catalog includes Gemini 3.1 Flash with web access, GPT-5.4 with web access, Grok 4 Fast with web access, Perplexity with web access, and Gemini 3 Pro with web access. Such modes are useful for checking news, preparing a brief, searching for sources, and fact-checking before publication.

Web access does not replace manual verification. If the response contains a link, open the source and ensure that it actually says what the model summarized. If an article is being prepared for /news/articles, record the verification date and do not include conclusions in the text that are not confirmed by the original source.

Tasks with Reasoning

For tasks that require analyzing causes, comparing options, building arguments, or testing hypotheses, reasoning modes are useful. The confirmed catalog includes Grok 4 Fast reasoning mode, DeepSeek V4 reasoning mode, DeepSeek V4 PRO reasoning mode, Gemini 3 Pro with web access, and Deep Research. They can help break down a complex topic into steps, find weak points in reasoning, and suggest a verification plan.

It is important not to confuse reasoning with proof. The model can logically explain an erroneous conclusion if the initial data is incomplete. Therefore, in the query, specify which facts are confirmed, which are assumptions, and which conclusions cannot be drawn without sources. For important decisions, save not only the response but also a list of checks that need to be performed manually.

Deep Research for Complex Materials

Deep Research should be used when the topic requires a longer research framework: article plan, overview of approaches, list of questions for an expert, preparation of the structure of analytical material. This is not a substitute for full research and not legal or financial expertise. The result should be viewed as a draft map, which is then verified against sources.

A good query for Deep Research sets boundaries: topic, audience, what is considered a source, which claims not to use, what output format is needed. For example: 'Prepare a structure for a review on the topic of AI video generation, do not use ratings, indicate which facts need to be checked separately.' Such a query helps obtain a useful plan without unconfirmed promises.

Visual Context in Text Tasks

Some text models support visual context. This is reflected in the catalog through capabilities. This mode is useful if you need to describe an image, analyze a screenshot, explain a diagram, or check whether the visual material matches the text description. But the result from an image also needs to be verified, especially if there are numbers, small text, interface elements, or legally significant data.

If the task is not about analyzing an image but about creating an image, go to /images. For video scenarios, use /videos. Do not mix text models with media generation: these are different surfaces and different types of queries.

How to Consider Tariffs and Limits

Model selection is not only about the task but also about access. In web/lib/subscription.ts, it is confirmed that access to models depends on the plan, and limits include text queries, images, videos, and Deep Research. Therefore, before regular work, check /pricing and the current account status. Do not base an article on the assumption that a specific model is always available to all users.

For regular tasks, it is useful to maintain a simple map: task, model, result, what to check, how many attempts were needed. After a few days, it will become clear which models truly suit your workflow. This map is more useful than someone else's rating because it reflects your tasks and your query style.

Common Mistakes in Model Selection

The first mistake is switching models after one unsuccessful response. Often the problem is in the query: no context, no format, no constraints, or no example. First improve the instruction for the AI, and only then try another mode.

The second mistake is using web access where the data is already with you. If you need to rework your own document, give the model the relevant fragment and ask a question. Web search is not needed if the task does not require external timeliness.

The third mistake is using reasoning mode for simple tasks. If you need to come up with several options for a short headline, complex reasoning may be excessive. Choose the tool according to the task load.

Model Selection Checklist

Before a query, answer five questions. Do you need up-to-date information? Do you need a detailed analysis? Is there visual material? Is a short draft sufficient? Do you need to check the result against sources? After that, choose the model or mode, formulate the query, get a draft, and manually verify it.

If the task repeats, save the successful query as a template. If the task is one-time, do not complicate the process. If the question concerns access, payment, limits, or platform operation, use /support and /pricing, not guesswork.

Summary

The Neiron AI text model map is not for ratings but for navigating tasks. Fast models help with short drafts, web access with up-to-date information, reasoning modes with analysis, Deep Research with the research framework. The final result always requires manual verification, especially before publication, delivery to a client, or use in work documents.

Neiron AI Text Model Map by Task Type Without Ratings