GPT-5.1 vs Gemini 3 Pro vs Claude Opus 4.5

The closing months of 2025 have turned into a strange kind of festival in the world of artificial intelligence. Three of the most powerful models ever built arrived almost back-to-back. OpenAI’s GPT-5.1 came first, followed by Gemini 3 Pro by Google DeepMind. Finally, Claude Opus 4.5 from Anthropic rounded off the month. 

The most striking shift this season among these models is a simple idea. They do not think in a straight line anymore. In older systems, the model would read a prompt and fire back a response at a fixed pace. In the new systems, the model slows down when the task demands it. It walks through a chain of ideas, checks mistakes and plans.

Thinking Styles

Each company has taken its own approach to this new behaviour. GPT-5.1 decides for itself whether to think deeply or speed through an easy task. There’s no switch for the user to flip. The model reads the room. 

Gemini 3 Pro offers a clear choice through a Deep Think mode. A researcher can switch it on for complex problems. 

Claude Opus 4.5 offers the most control. Its Effort setting lets users control the number of tokens used for a task. It’s almost like adjusting the brightness, but for intelligence.

Together, these choices set the tone for how the three companies envision the future. OpenAI seems to want speed and scale, Google wants mastery over media, and Anthropic wants reliability in long stretches of complex tasks. 

The Coding Test

GPT-5.1 has a second trick that matters. Codex-Max, its specialised version for software work, uses a process called compaction. It keeps long coding sessions clean by turning old logs and errors into a compressed memory that preserves the essence of the work. 

This solves a problem that slowed previous systems. Long loops often drowned the model in clutter. Codex-Max stays alert throughout an entire day of debugging without losing context. The result is not a longer memory but a sharper one.

Gemini 3 Pro takes a different path. Google built it to treat text, images, audio and video as part of a single stream. It does not bolt separate vision or audio modules on top of a language core. Everything is processed in a unified space. 

This gives it an unusual sense of flow. It understands tone in audio, grasps cause and effect in long videos, and reads documents that stretch across a million tokens without breaking them apart.

Claude Opus 4.5 tries to solve a quieter but stubborn problem. In long chains of coding or research, older models often forgot why they made a choice a few turns earlier. Opus 4.5 keeps its own thinking blocks intact from one step to the next. This stops it from repeating the same failed ideas. 

It behaves like someone who remembers previous attempts with clarity. It also brings a fresh skill. The model can zoom into small portions of a screen at full resolution. It uses this to catch minute details in documents or interfaces that other models miss.

A developer compared GPT-5.1, Gemini 3.0 and Opus 4.5 across three coding tasks to see how they behave in real work. The idea was simple. Put the models through the same problems and watch how they deal with instructions, messy legacy code and incomplete systems.

The first test checked prompt discipline. The models had to build a Python rate limiter with 10 strict requirements. Gemini stuck to the script in a very literal way. Opus 4.5 stayed close to the spec and produced clearer notes. GPT-5.1 added checks and safety logic that hadn’t been requested.

The second test threw a broken TypeScript API at the models, asking them to clean it up and fix underlying design flaws. Opus 4.5 completed all 10 improvements. GPT-5.1 achieved nine and flagged security gaps like missing auth and unsafe database calls. Gemini managed eight and wrote quick code, but overlooked structural issues.

The third test assessed how well they understand a system before extending it. Given a half-built notification module, the models had to first explain the layout, then add email support. Opus 4.5 delivered the most complete answer and offered templates for every event. 

GPT-5.1 spent more time reading the system, pointed out bugs, drew diagrams and then added richer features like CC, BCC and attachments. Gemini understood the brief but kept its answer short.

Software work shows a very different ranking. 

Claude Opus 4.5 takes the crown for real engineering tasks. In a benchmark that tests fixes for real GitHub issues, it edges past GPT-5.1 Codex-Max. Both outperform Gemini in this space. While Claude handles ambiguity with grace, Codex-Max survives the longest sessions. Gemini performs best on pure algorithmic puzzles but loses its calm inside messy repositories.

On Benchmarks

The three models reveal their distinct personalities on benchmarks. Gemini 3 Pro leads in scientific reasoning with a strong grasp of physics, chemistry and biology. It also performs best on the toughest new test called Humanity’s Last Exam. 

The score suggests an ability to generate answers in areas where human knowledge is still patchy and unclear. GPT-5.1 trails in this category, while Claude Opus 4.5 sits between the two. It is competent in science but not dominant.

In mathematics, Gemini reaches perfection when allowed to call external tools. GPT-5.1 follows close behind. Claude remains reliable but less spectacular. The surprise comes in visual reasoning. Gemini and Claude show agility on puzzles that need flexible thinking, while GPT-5.1 struggles to keep pace.

The Bottomline: Pricing

Price changes the story yet again. OpenAI has pushed costs down to a point that feels almost strategic. GPT-5.1 is inexpensive to run at scale. This makes it ideal for high-volume workloads across companies and startups. Gemini is expensive per token but becomes good value for very long documents due to its huge context window. Claude costs the most but gives fine control through its Effort setting. 

The human experience around these models brings its own colour. Developers on community forums like Hacker News, Cursor and Reddit describe Claude Opus 4.5 as the one that understands intent with little friction. It behaves like a careful senior engineer. 

Gemini feels clever and thoughtful. It excels at planning large systems but can turn overly literal during execution. 

GPT-5.1 is described as fast and easy to work with. It solves small tasks quickly, but sometimes gives quick answers to slow problems.

Most users will not choose a single model. They will combine them: GPT-5.1 as the dependable worker that handles the load, Gemini as the deep reader and Claude as the careful executor. The idea of one model ruling the field is fading. The field now looks more like a team sport.

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