Trying to Understand the AI Model Zoo: OpenAI, Anthropic, Google, DeepSeek, Mistral, and Cohere

Just two years ago, the decision was simple: use ChatGPT or don’t use ChatGPT.

Today, the situation is very different.

If you start exploring tools for automation, agents, content generation, AI-assisted coding, or building AI-powered products, you’ll quickly encounter names like GPT-5, Claude Sonnet, Gemini, DeepSeek, Mistral, Cohere, and an ever-growing list of others.

For someone who doesn’t work in software, this can be surprisingly confusing.

Even for those of us who have spent years in technology, keeping up with the pace of new models requires some effort.

A few days ago, I found myself thinking about this while evaluating alternatives for several agents I’m building for SparkIO and other projects. My goal was simple: understand which models are worth using for which tasks, and when paying more actually makes sense.

The first thing I realized is that we’re often comparing very similar things with completely different names.

And behind those names are very different companies.

OpenAI popularized AI for the general public through ChatGPT. Anthropic was founded by former OpenAI researchers who wanted to focus on AI safety and alignment. Google had been researching AI for years before launching Gemini. Mistral emerged in France with the ambition of building a European alternative. DeepSeek surprised the industry from China by showing that competitive models could be built at significantly lower costs. And Cohere chose a quieter path, focusing primarily on enterprise customers.

Once you understand who’s behind each model, some of the differences start to make more sense.

Anthropic Won the Naming Game

I have to admit that Anthropic did something very clever with its naming strategy.

Their models are called:

  • Haiku
  • Sonnet
  • Opus

You don’t need to read any documentation to understand the hierarchy.

Haiku is fast and inexpensive.

Sonnet is the balanced middle ground.

Opus is the most powerful model.

Even if tomorrow they release Haiku 5, Sonnet 5, and Opus 5, most people would immediately understand where each one fits.

OpenAI took a different path.

We have GPT-4, GPT-4o, GPT-4.1, GPT-4.1 Mini, GPT-5, GPT-5 Thinking, GPT-5 Instant, o4-mini, and a growing collection of names that appear with each generation.

From a technical perspective, it makes sense.

From a human perspective, it’s much harder to remember.

Maybe that’s not an accident.

Anthropic was founded by researchers coming from academic and AI research backgrounds. It feels like someone inside the company understood that most users don’t want to memorize product codes. They want concepts they can remember.

If someone asks me six months from now which Anthropic model sits in the middle of the lineup, I’ll probably remember Sonnet.

If they ask me the difference between GPT-4.1, GPT-4o, and GPT-4.1 Mini, I’ll probably need a few seconds to think about it.

After months of using both platforms, I still remember exactly what Sonnet is supposed to do. I occasionally need to stop and think when I see some GPT variants.

A Rough Mental Mapping

There is no perfect equivalence across providers, but for someone just getting started, this mental model is often useful.

Anthropic OpenAI Typical Use Case Haiku GPT Mini / Budget Models High-volume automation, classification, extraction Sonnet Standard GPT Models Coding, agents, analysis, everyday work Opus Advanced Reasoning GPT Models Complex problems, research, architecture

In practice, Sonnet has become a sweet spot for many developers.

Anthropic has gradually pushed Sonnet closer and closer to Opus-level capabilities while maintaining significantly lower costs.

OpenAI still has one advantage that’s difficult to ignore: it was the company that transformed AI from a research topic into a mainstream tool.

Many of the people using Claude, Gemini, or DeepSeek today started with ChatGPT.

It’s somewhat similar to Google’s role in the early days of the web. It may no longer be the only option, but it was the gateway for a large part of the market.

The Problem with Cheap Models

There’s something I’ve noticed while doing AI-assisted development.

Budget models often work extremely well… until they don’t.

I’ve seen this happen several times.

I present a programming problem.

The model attempts a solution.

Then tries again.

Then generates another variation.

Then another.

And another.

After several rounds, it’s still circling around the problem without identifying the root cause.

Then I copy the exact same context into Sonnet, and it finds the issue almost immediately.

The difference isn’t that the smaller model doesn’t know how to code.

The difference is reasoning capability.

This pattern shows up repeatedly.

Budget models are often excellent at repetitive, well-defined tasks.

Larger models tend to perform better when a problem requires understanding multiple variables, identifying patterns, or finding subtle errors.

Where Does DeepSeek Fit?

DeepSeek is probably the most interesting player to emerge over the past year.

What’s interesting about DeepSeek isn’t just the model itself.

It’s the effect it had on the industry.

For years, there was a widespread assumption that competing in AI required nearly unlimited resources.

DeepSeek forced many people to reconsider that assumption.

Suddenly there was a competitor producing surprisingly good results with a very different cost structure.

Its value proposition is simple.

Get as close as possible to premium model performance at a fraction of the cost.

And for many use cases, it does that remarkably well.

Particularly for software development.

I wouldn’t be surprised if a large percentage of lead-generation agents, automation workflows, classification systems, and data-enrichment pipelines eventually run on DeepSeek while reserving premium models for final output generation.

In fact, I’m increasingly seeing architectures like:

  • DeepSeek for data collection.
  • DeepSeek for classification.
  • DeepSeek for enrichment.
  • Sonnet or GPT for final report generation.

From an economic perspective, that makes a lot of sense.

Costs Are Converging

A while ago, the pricing differences between providers were dramatic.

Today, things are becoming much more competitive.

Anthropic’s API pricing roughly looks like this:

  • Haiku: around $1 per million input tokens.
  • Sonnet: around $3 per million input tokens.
  • Opus: around $5 per million input tokens.

OpenAI offers similar tiers:

  • GPT Mini: roughly $0.40 per million input tokens.
  • Standard GPT models: around $2 per million input tokens.
  • Premium GPT models: approximately $2.50 to $5 per million input tokens.

When you look strictly at token pricing, the differences are no longer as dramatic as they were a couple of years ago.

The real difference often comes from productivity.

If a more expensive model solves a problem in one interaction while a cheaper model requires ten attempts, the effective cost becomes very different.

The Other Players

Beyond OpenAI and Anthropic, there are three companies worth paying attention to.

Google is playing a different game.

It doesn’t need to prove it can build powerful models. It needs to figure out how to integrate those capabilities into an ecosystem already used by billions of people every day.

Gemini may ultimately benefit more than any other model from being connected to email, documents, calendars, maps, search, and other services people already use.

Mistral represents something different.

It is probably Europe’s most serious attempt at building foundation models and has quickly earned respect among developers for the quality of its open models.

Many teams are adopting Mistral when they want to reduce dependence on major U.S. providers or when they need greater deployment flexibility.

Cohere, on the other hand, is often invisible to everyday AI users.

Yet it appears regularly in enterprise discussions where the focus is document search, knowledge management, and large-scale organizational deployments.

It doesn’t have the visibility of OpenAI or Anthropic, but it remains a company that many large organizations follow closely.

So Which One Should You Use?

After trying quite a few of them, my practical recommendation would look something like this.

If you’re just getting started:

  • ChatGPT or Claude.
  • Don’t overcomplicate it.

If you’re doing a lot of coding:

  • Claude Sonnet remains extremely difficult to beat.
  • GPT is very close depending on the type of work.

If you’re building high-volume agents:

  • DeepSeek deserves serious consideration.
  • The savings can be substantial.

If you want to run models locally:

  • Mistral.
  • Gemma.
  • Qwen.

At that point, however, you’re exchanging token costs for operational costs, hardware management, maintenance, and availability concerns.

That’s a trade-off people often underestimate.

Running a mini PC 24 hours a day also has a cost.

It’s not always financial.

Sometimes it’s mental.

My Current Conclusion

The industry seems to be entering an interesting phase.

There is no longer a single obvious winner.

There is no perfect model for every task.

What I’m seeing more and more is a multi-model architecture.

One inexpensive model for processing volume.

One mid-tier model for most day-to-day work.

One premium model reserved for situations where it genuinely adds value.

Interestingly, that’s very similar to how we’ve designed software systems for years.

We don’t use the most powerful database for every operation.

We don’t run every service on the largest server available.

We optimize each component according to its purpose.

Perhaps the next important skill for people building AI products won’t be learning a specific model.

Perhaps it will be learning how to combine several of them effectively.

Just two years ago, the conversation was:

“Do you use ChatGPT?”

Today the question is becoming something else:

“Which model are you using for this task?”

The answer is increasingly not just one.

And that’s probably a sign that the market is beginning to mature.

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