GPT-6, Claude 5.5 and Gemini 4: Who Leads the AI Race and Which Model Handles Real-World Tasks Best

The race between flagship models has entered a phase in which new versions arrive every few months and the gap between the leaders on public leaderboards is measured in single percentage points. OpenAI, Anthropic and Google promote GPT-6, Claude 5.5 and Gemini 4 as the best solutions for business. But the question "which model is the strongest" is increasingly giving way to a more practical one: which model will best handle a specific task for a specific company.

Key takeaways

• There is no single absolute leader: each model is stronger in its own classes of tasks;
• Public benchmarks reflect real work less and less — many of them are close to saturation;
• Differences between models are shifting from "intelligence" to reliability, speed, price and ecosystem;
• For businesses, testing on their own tasks and data is becoming decisive;
• An architecture that lets you swap models quickly is worth more than picking a "winner."

Why leaderboards no longer give the answer

Classic tests of knowledge and logic have largely run their course: leading models score very high and very close results on them. In addition, tasks from popular test sets may have ended up in training data. That is why developers and independent researchers are moving to tests that mimic real work: fixing bugs in actual repositories, multi-step work in a browser, completing tasks that take hours. Yet even such tests capture only certain aspects of a model's capabilities.

The leaders' strengths

GPT-6 (OpenAI)

OpenAI has traditionally bet on versatility and the breadth of its ecosystem: a mass-market consumer product, a well-developed developer platform and integrations with many services. Companies choose GPT for a wide range of tasks — from working with text to analytics and multimodal scenarios — and because of the large number of ready-made solutions built on it.

Claude 5.5 (Anthropic)

Claude models have earned a reputation as a strong tool for programming, working with long documents and agentic tasks in which a model must act with tools consistently over long periods. Anthropic also places considerable emphasis on safety and the predictability of model behavior, which matters for enterprise deployments in regulated industries.

Gemini 4 (Google)

Google leverages its key advantages: its own compute infrastructure, a large context window and deep integration with search, cloud and office applications. Gemini is particularly attractive to companies already working within the Google ecosystem, as well as for tasks involving video analysis and large volumes of heterogeneous data.

What really matters for business

• Reliability. How consistently the model completes a task on the first try and how often it needs corrections.
• Cost of the result. What matters is not the price per token but the price of one successfully completed task, including retries.
• Speed. For customer-facing services, response latency can matter more than a small difference in quality.
• Data handling. Where data is processed, what guarantees the provider offers, and whether the model is available in the required region.
• Ecosystem. Compatibility with the cloud, development tools and corporate systems already in use.

How to choose a model

1. Collect 50–200 real examples of the company's tasks with reference answers.
2. Run them through several models under identical conditions.
3. Evaluate quality, cost and speed together rather than separately.
4. Repeat the evaluation whenever a new version is released.
5. Build solutions so that the model can be replaced without rewriting the entire system.

Bottom line

There is no final winner in the AI race, and one is unlikely to emerge any time soon: leadership passes from one company to another with every major release. The winner is not whoever guessed the best model, but whoever can quickly test new versions on their own tasks and flexibly use the strengths of each.

Prepared by the editorial team. This material is for informational purposes only. Model names and trademarks belong to their respective owners.