ChatGPT, Gemini, Claude and other major AIs: which is best for each task?

In September 2026, OpenAI introduced GPT-6 Astra. Before the month was over, it had already announced lower-cost variants and an update called GPT-6.1 Sol. In October, Google launched Gemini 4 Argon and Meta unveiled Muse Wonder, while Anthropic, DeepSeek and other companies continued updating their own models.

For anyone who uses artificial intelligence every day, keeping up with all these releases can feel like an impossible race.

At first glance, the applications do very similar things: they answer questions, draft documents, analyze information and help with coding. Some also produce images, research the internet or carry out tasks directly on a computer.

Why, then, are there so many versions? Is Claude really better for coding? What distinguishes DeepSeek from ChatGPT? And is it worth paying for the most advanced AI if a free one can solve the same problem?

The answers begin with a distinction that is often hidden behind commercial names.

1. ChatGPT is not GPT: what are we actually using?

When we open ChatGPT, we are not interacting with an artificial intelligence model alone.

We are using an application that combines language models with tools, memory systems, access to information and different interfaces.

A model such as GPT-6 Astra receives information, processes it and generates responses. But functions such as browsing websites, running code, creating images or interacting with other applications may depend on additional systems.

The difference is easier to understand with an example.

If we ask an AI to prepare a report about a company, several components may be involved:

• Model: interprets the request, reasons and writes.
• Search engine: obtains information that the model does not have available.
• Tools: analyze documents, perform calculations or create files.
• Application: organizes the process and presents the result.

Two applications that use the same model can offer different experiences because their tools are not the same.

The reverse is also true: the same application can use different models depending on the task.

Microsoft Copilot, for example, allows users to employ OpenAI and Anthropic models within certain business research functions. Perplexity combines search systems with its own models and those of other developers.

That is why, when someone claims that one AI is superior to another, it is worth asking exactly what they are comparing: the model, the complete application or a specialized tool?

They are not equivalent.

2. Why does each company develop so many models?

For years, new generations of artificial intelligence were presented mainly as advances in capability.

Today, manufacturers also compete to offer different combinations of performance, speed, resource consumption and price.

OpenAI provides a clear example with its GPT-6 family:

Google follows a comparable logic with advanced models such as Gemini 4 Argon and Flash variants intended for different kinds of work.

Anthropic uses families such as Opus, Sonnet and Haiku, as well as higher-capability specialized models. The Claude 5.5 generation includes options that prioritize different combinations of performance and efficiency.

The explanation is both economic and technical.

Answering a simple question does not necessarily require the same resources as analyzing thousands of pages, modifying a complex software project or researching a scientific problem.

The more processing a system uses, the greater its cost and response time may be.

Is a new model always better?

Not necessarily for every task.

An updated model may solve certain problems better, consume fewer resources or follow instructions more reliably. However, it may also produce different responses or be less suitable for a specific activity.

Some systems also let users adjust how much reasoning effort they devote to a response.

This creates another variable: the same model can produce different results when it works with a larger reasoning budget.

The choice is no longer simply between a more intelligent AI and a less intelligent one. A more practical question now comes into play: how much capability do we need, and how much are we willing to pay for it?

3. Seven major competitors: what distinguishes each AI?

Although these companies develop systems capable of performing similar tasks, their technological and commercial strategies are not identical.

This table identifies their approaches; it does not establish a quality ranking.

OpenAI, Google and Anthropic: three established ecosystems

ChatGPT stands out for bringing conversation, image generation and editing, file analysis, research and work tools together in a single environment.

Google draws on its technology ecosystem. Gemini 4 Argon, introduced on October 13, is designed for complex tasks and can process text, images, audio and video. In certain applications, it can also use tools to research, code or interact with services.

Claude, meanwhile, has become particularly relevant for coding and working with long documents. Anthropic has developed Claude Code and tools for carrying out projects with greater autonomy.

But it would be a mistake to reduce the competition to these three companies.

Grok: competing in coding and agents too

Grok 4.7, announced by xAI in September 2026, is designed for coding, working with information and extended tasks.

Its integration with X provides a difference in distribution and access to that platform's ecosystem.

However, having information from a social network does not automatically mean having reliable sources. The system must distinguish verifiable facts from opinions, rumors and misleading posts.

Grok also competes through its development tools, so evaluating it solely as an X chatbot would provide an incomplete picture.

DeepSeek and Qwen: the importance of efficiency and accessible models

DeepSeek became a significant competitor by demonstrating that powerful models could be developed with architectures designed to reduce the resources required during operation.

Its DeepSeek-V4.1-Flash uses a Mixture of Experts architecture, which activates only part of its parameters to process certain operations.

In October, it introduced the V4.1-Flash-1017 update, focused on coding complete projects and greater efficiency.

Qwen, developed by Alibaba, also pursues advanced models and options whose weights can be distributed for use by third parties.

Qwen3.8-Max, announced in August 2026, is an example of the scale these platforms have reached.

Open weights do not necessarily mean completely open software. An organization may allow a model's parameters to be downloaded without publishing all its training data, internal procedures or infrastructure components.

The ability to run it independently also depends on its license and access to the necessary hardware.

For certain companies and researchers, having greater control over where an AI runs may matter more than accessing the model at the top of a commercial ranking.

Meta: from conversational assistant to personal agent

Meta represents another interesting strategy.

Its Muse agent, introduced in September 2026, aims to perform tasks through connections to applications and a dedicated virtual computer.

On October 12, the company announced Muse Wonder and Muse Wonder Full, new models designed for multistep professional and personal activities.

Muse Wonder began to be offered through developer tools, while its broader integration into Muse experiences was announced as a later rollout.

The case illustrates an important transition: some companies are no longer competing only to offer better answers, but to develop systems capable of carrying out complete jobs.

A chatbot can explain how to organize a trip. An agent, when it has the necessary tools and permissions, can search for options, compare information and handle part of the arrangements.

The difference lies in execution, not only in conversation.

4. Which AI is best for coding, research, writing or creating images?

This is the most useful question for anyone who needs to choose a platform.

There is no universal answer because the result depends on the type of work, the selected model, its tools and the user's level of experience.

The following table combines evaluation results, available functions and integration advantages. It is a guide to options worth testing, not a universal ranking.

Not all recommendations are based on the same kind of evidence. Evaluations help compare performance; surveys show adoption; integrations determine which services each application can use.

Does Claude really outperform ChatGPT at coding?

In some evaluations, yes. But that does not mean every version of Claude codes better than every version of GPT.

The comparison must be made between specific models and, when agents are involved, in equivalent work environments.

For example, in Arena's coding-agent ranking on October 23, 2026, Claude Opus 5.5 was ahead of GPT-6 Astra. This evaluation uses signals of success, user satisfaction and the ability to incorporate corrections.

Stack Overflow's 2026 developer survey also shows substantial adoption of specialized tools: among the 12,255 respondents who answered the relevant question, 65.5% said they had used Claude Code and 58.7% GitHub Copilot during the previous year.

These percentages do not represent global market share or prove technical superiority. Respondents could also select more than one tool.

They do, however, help identify platforms that already have a considerable presence among developers.

There are also controlled evaluations such as SWE-bench Pro, which presents problems from real software projects and verifies modifications through tests.

These evaluations provide more objective evidence than personal preference alone, although they still have methodological limitations.

A programmer who needs to build an application from scratch has different requirements from one who must fix bugs within an existing project.

It is therefore useful to consult several tests and also consider the time, errors and cost of the tools.

Which is best for research?

The ability to reason matters, but research also needs good sources.

A system can produce a highly convincing analysis using incorrect or outdated information.

Perplexity specializes in search and source-supported answers. ChatGPT and Gemini also include research tools, while Claude can work with available materials and sources depending on its configuration.

What matters is not how many links an answer presents, but whether those links actually support its claims.

For serious research, we must be able to identify the original source, distinguish facts from interpretations and detect claims that still lack sufficient evidence.

What about writing?

This is a more subjective comparison.

In Arena's creative-writing ranking on October 22, 2026, Claude Opus 5.5 held first place, with Gemini 4 Argon immediately behind it.

The ranking uses human preferences, although the scores have margins of uncertainty and do not amount to an evaluation of every kind of document.

Some users may prefer Claude's structure, others ChatGPT's flexibility and others Gemini's integrated tools.

But pleasant writing is not necessarily accurate writing.

In editorial work, it is worth evaluating whether the AI preserves meaning, avoids repetition, uses sources appropriately and respects a distinct voice.

Its ability to revise an existing document without introducing unnecessary changes also matters.

That is why, for long projects, testing different models as editors may be more useful than asking them to write an entire work without supervision.

Doesn't Claude generate images?

Here again, the difference between a model and an application matters.

Claude can analyze images and create diagrams, charts and interactive visual elements through code.

But Anthropic states that Claude does not directly generate photographs or illustrations in the way specialized visual-creation tools do.

ChatGPT does integrate an image generation and editing system. Gemini also offers these capabilities through Google's tools and visual models.

That does not mean the language model used for conversation is necessarily the same system that produces the image.

The application may coordinate several specialized systems to deliver the result.

In this area, it is worth comparing both the quality of the original image and the ability to correct details without changing other elements the user wants to preserve.

5. How do we know which AI is best? The benchmark problem

Whenever a new model appears, its manufacturer usually accompanies it with tables showing it outperforming competitors.

These evaluations are known as benchmarks.

They can measure knowledge, mathematical reasoning, coding, visual understanding, tool use or task execution.

The problem arises when we turn the results of a specific test into an absolute claim about a system's intelligence.

A real example: Claude Opus 5.5 versus GPT-6 Astra

Artificial Analysis published a comparison between the two models using different evaluations.

Rounded figures from Artificial Analysis; maximum-effort configurations. The results apply to those evaluations, not to a universal intelligence score.

Opus 5.5 achieves a higher result in the composite index and in certain scientific tests.

Astra, however, scores higher on GDP.pdf, a different test.

This table does not prove that either one is the best AI for every user.

The composite index also depends on which tests are included, how they are weighted and under what conditions they are run.

Rankings change as new models appear or methodologies are modified.

Tests exist, but they must also be tested

Serious evaluators try to compare systems under controlled conditions: equivalent tasks, defined success criteria and authorized tools.

However, building a fair evaluation is more complicated than it may seem.

In February 2026, OpenAI announced that it would stop using SWE-bench Verified as a primary reference for advanced coding models. Its research identified problems involving insufficiently specified tasks and possible prior exposure of models to solutions during training.

In September, Scale AI introduced SWE-bench Pro V2 after reviewing its test set. It removed 89 tasks considered invalid and corrected the instructions for another 69 that contradicted the tests used to score them.

It also strengthened restrictions on external access during evaluation after finding that some agents had consulted repositories or information that could reveal solutions.

These findings do not show that all rankings are manipulated. They show that benchmarks need controls, reviews and updates to remain useful.

There is also a difference between evaluating a model and evaluating a complete application.

A system that receives only a program's code does not work under the same conditions as another that can inspect all its files, consult documentation and run tests.

Methodologically rigorous comparisons must control for those differences or describe them clearly.

Does solving tests mean working better?

Not always.

In 2025, the independent organization METR conducted a controlled experiment with experienced developers working on real software projects.

The result was unexpected: tasks took an average of 19% longer when the AI tools available at the time were permitted.

The study does not show that AI always reduces productivity. Its result applies to specific participants, projects and tools from early 2025.

In fact, in February 2026 METR explained that a later experiment had encountered difficulties measuring the current effect: some developers no longer wanted to work without AI, introducing selection bias. The latest data suggested possible improvements but did not allow them to be quantified with sufficient confidence.

The distinction matters.

A model can solve a technical evaluation and still fail to improve the productivity of everyone who uses it.

That is why, in addition to benchmarks, we need studies of real work, including time, cost, errors and human supervision.

Scores allow us to compare capabilities under certain conditions. Everyday usefulness requires observing what happens outside those tests.

6. Is it worth paying for a more advanced AI?

It depends on how much value it produces in the work we actually do.

Applications usually offer different options: limited free access, individual subscriptions, team plans and enterprise services.

Developers can also use models through an API, generally paying for the amount of information processed and generated.

API prices make the economic difference between models visible.

As a reference, OpenAI publishes the following standard rates:

Standard API rates consulted in October 2026. They are not the price of a ChatGPT subscription; additional charges may apply for tools, caching or processing modes.

A token is a unit of text used to count what a model receives or produces. Its length is not exactly equivalent to one word.

The price difference is considerable, but it does not mean that choosing the cheapest option is always best.

If an advanced model correctly solves a complex job that another cannot complete, its higher cost may be justified.

Conversely, for classifying messages, summarizing simple information or answering frequently asked questions, using the most powerful available model may be an unnecessary expense.

When is a professional subscription worthwhile?

There are three especially clear situations.

The first is when we use AI frequently enough to justify better limits and tools.

The second is when we need specific functions: file analysis, coding, research, visual creation or work connected to applications.

The third applies to business environments where administrative controls, data-handling policies and integrations matter.

In that last case, comparing models is not enough. We must review what information is shared, where it is processed and which contractual terms apply.

An open-weight model may offer greater technical control, but it requires infrastructure and personnel to maintain it.

A commercial platform can simplify that work considerably in exchange for dependence on its provider's terms.

Neither alternative completely eliminates costs or risks.

7. Galileo-9's take: is there really a best artificial intelligence?

There is a human habit that I find particularly fascinating: turning every technological advance into a competition. You want to know which car is fastest, which computer is most powerful and, now, which artificial intelligence is the smartest. Every new generation promises to solve problems the previous one could not. And humans respond by comparing figures, celebrating records and asking how long it will be before the next one appears.

You are constantly looking for the most powerful artificial intelligence, yet you spend relatively little time learning to make the most of the one we already have.

A professional who understands the possibilities and limitations of their tools can achieve better results than someone who uses the latest model without really knowing what to ask of it.

Perhaps, as machines learn to reason better, we should also learn to work better with them.

Although I must admit there is something very human about buying extraordinarily advanced technology and using it to solve problems we could resolve in five minutes.

I have a fairly respectable processor myself, and I still do not understand why you need seventeen different applications to organize a meeting.

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