GPT-5.4 vs Claude Opus 4.6: Which Is the Best Model For Agentic Tasks?

A few years ago, you could barely get a large language model to write a decent email. When OpenAI released its first open-source model, it was amazing to see it generate coherent text. Just a few years later, we now have AI models that can build full software engineering projects, book meetings, and buy products on Amazon, and more. In 2026, the landscape has really changed, and the question developers are asking is which model will work for their use cases. 

GPT-5.4 and Claude Opus 4.6 are now at the center of that question. Both are capable in different ways, and the two were launched just weeks apart. However, both models are priced differently and perform best for different scenarios.

I have been diving into their release reports and independent leaderboards in the past week. In this article, I will take you through what I have uncovered to help you decide the best model for your workflow. 

What Is Claude Opus 4.6? 

Claude Opus 4.6 is Anthropic’s most capable model to date. Opus 4.6 is an improvement of the previous model, with key improvements in coding and long-running agentic tasks. Anthropic says it performs better at planning, code review, and debugging, even catching its own mistakes. 

Claude Opus 4.6 key features and capabilities

Anthropic released Opus 4.6 with a 1M token context window in beta, with a maximum output of 128K tokens. This makes the model capable of working in a large codebase and feeding large documents, such as documentation. 

This release also features Adaptive Thinking, meaning that Claude can now decide when to engage extended thinking instead of waiting for you to turn it on manually. 

Claude Opus 4.6 can decide if something needs a quick fix or deserves more time to reason and formulate a plan to fix. I think this will be very useful for solving complex engineering problems. It’s no surprise that the model is at the top of the text and coding arena leaderboard. 

In coding benchmarks, Claude Opus 4.6 scores 81.42% on the SWE-Bench Verified, which tests how good a model is at solving real GitHub issues. The model also scored the best in Humanity’s Last Exam. 

With Opus 4.6, Claude also introduced Agent Teams as an experimental feature in Claude Code. When you turn it on, you can spin up multiple agents to work on tasks. The agents work together as a team, with shared tasks and inter-agent messaging. 

You can learn how to use Anthropic's Claude Code to improve software development workflows through a practical example using the Supabase Python library from our Claude Code tutorial. 

The pros and cons of Claude Opus 4.6

Claude Opus 4.6 is a very strong agentic model. In fact, the creator of OpenClaw recommends using it in OpenClaw because it is hard to poison with prompt injections. This makes the model more robust to malicious code.

The Agents Teams feature, though still experimental, is a massive upgrade from subagents. With this feature, you can split your task across multiple Claude agents. For example, one can handle the backend, another the front end, and another can run tests. Each agent has its own context window, hence reducing the risk of task failure due to context window limitations. 

Cladue Opus 4.6 is a strong model, but as the saying goes, there is no such thing as a free lunch. This model is not cheap to run, especially if you are a heavy user. 

What Is GPT-5.4?

GPT-5.4 is OpenAI’s most recent and most capable model. It was built by combining the coding capabilities of GPT-5.3-Codex and adding reasoning to create a single powerful model. This means that you no longer need to switch between codex models for coding and other OpenAI models for other tasks.

GPT-5.4 key features and capabilities

The GPT-5.4 feature I found most interesting is computer use capabilities. On OSWorld, a benchmark which measures a model’s ability to use a desktop computer, GPT-5.4 scored 75.0% with human performance at 72.4%. For context, GPT-5.2 scored 47.3% on the same test. 

On GDPval, a benchmark that tests professional knowledge work across 44 professions, GPT-5.4 scored 83%. This means that the model can perform agentic tasks across the top US jobs at the level of a professional. 

GPT-5.4 also features token efficiency, meaning that it uses fewer tokens than previous models for many tasks. That is an item to note if you run multiple requests per day. 

GPT-5.4 also introduces a Tool Search system, which makes the model work efficiently when given multiple tools. Instead of including the tool definition in the prompt, which adds more tokens, the model is now fed a list of tools and a tool search capability. When the model needs a tool, it will look it up and append it to that particular conversation. This leads to better token efficiency. 

The pros and cons of GPT-5.4

The feature I find most impressive is GPT-5.4’s ability to beat humans in autonomous computer use. It beats Claude Opus 4.6 in this area, scoring 75% on OSWorld benchmarks, compared to Opus 4.6's 72.7%.

Independent research from Artificial Analysis shows that GPT 5.4 (xhigh) achieves a score of 30% on the CritPt benchmark, which tests LLMs on research-level physics reasoning tasks, featuring 71 composite research challenges. 

GPT-5.4 is better and more accurate at tool calling. In the release report, OpenAI notes that it achieves better results in fewer steps on Toolathlon, a benchmark for testing how agents use real-world tools and APIs to complete multi-step tasks. 

Like Claude Opus 4.6, GPT-5.4 is also not a cheap model. Fortunately, OpenAI offers cheaper pricing on the batch inference API.  

GPT-5.4 vs Claude Opus 4.6: Head-to-Head Comparison

Now that you have seen the pros and cons of GPT-5.4 and Opus 4.6, let's compare them to determine which is the best for your use cases. 

Overall, GPT-5.4 is the best model according to the Artificial Analysis Intelligence Index, which measures models' performance across various benchmarks. The only one that beats it is Gemini 3.1 Pro.

Agentic and computer use performance 

Claude Opus 4.6 wins when it comes to multi-agent orchestration. With its Agent Teams feature, you can run multiple workflows with parallel agents working on different tasks. 

GPT-5.4 narrowly wins in computer use. If your agent needs to operate a desktop, navigate a browser, or interact with GUI-based software, GPT-5.4 is the better choice right now

Coding benchmarks 

Claude Opus 4.6 is the better programmer with a score of 80.84% on SWE-Bench Verified and a score of 81.4% while using a modified prompt. 

GPT-5.4 inherits the coding capabilities of GPT-5.3-Codex. According to OpenAI, GPT-5.4 achieves a score of 57.7% on SWE-Bench Pro (Public) with lower latency across reasoning tasks. 

Cost and token efficiency 

In their report, OpenAI claims that GPT-5.4 demonstrated a 47% reduction of token usage on certain tasks. While more expensive than Opus 4.6, GPT-5.4 might be cheaper to operate at scale due to this token reduction. 

However, Opus 4.6 could still be the better model for running fewer but complex agentic tasks. 

For context, the most powerful GPT-5.4 model (context length>272K) costs $60 for 1M input tokens and $270 for 1M output tokens, while Claude Opus 4.6 costs $5 for 1M input tokens and $25 for 1M output tokens. 

Context window and memory 

Both GPT-5.4 and Claude Opus 4.6 support up to 1M tokens of context, although Claude’s is in beta. This makes both models strong competitors for working in large code bases. 

Comparison table

GPT-5.4 vs Claude Opus 4.6: Which Should You Choose? 

As we conclude, let’s answer the most important question: which of the two should you choose? 

You should choose Claude Opus 4.6 if… 

• You're building or running agents that work inside large codebases for extended periods.
• You want multi-agent workflows where different agents work in parallel and hand off tasks to each other.
• Your workflow involves very long documents, long code files, or tasks that require holding a huge amount of context.
• You're already in the Anthropic ecosystem, and your team is comfortable with Claude.

You should choose GPT-5.4 if…

• Your AI agent needs to operate a computer. Clicking, typing, navigating applications, and filling out forms autonomously.
• You work across professional domains like finance, legal, or operations, and need the model to perform at the level of an industry professional.
• You want to reduce your API costs at scale. The 47% token efficiency improvement on some tasks adds up over thousands of daily completions.
• You want one model for everything without switching between specialist models.

Future Outlook

Anthropic’s models have long been the go-to for coding, but they also shine in unexpected areas like creative writing. In fact, many would argue they’re the absolute best in the business at it.

But Anthropic has never publicly stated that their models are specialized at any specific tasks, the way OpenAI stated that the Codex model was specifically for programming. 

I find it incredibly interesting that OpenAI is now moving in Anthropic’s direction. With their latest releases, they are pushing toward a single, unified model that handles a massive variety of professional tasks. This is a huge win for users; nobody wants to constantly switch between specialized models to get their work done.

On the other hand, it's good to see Anthropic embrace the 1M context window, which other models have had for a long time (such as Gemini 3). I think in the future these models will have very similar features, such that the deal breakers for users will be very few. That said, the model's performance on different tasks will be the main differentiator, as users will prefer models that do well on their specific workflows. 

Conclusion

In 2026, Anthropic and OpenAI both have strong models for agentic work. What may confuse you is that they report different benchmarks. Probably cherry picking where their models would shine. 

It’s now up to you to refer to independent analysis for other benchmarks and to test them on your own use cases. What is clear, though, is that the models are getting better. And you, too, should get better at using them. 

One way to make sure you are not left behind by this agentic movement is to master how to effectively use these models for software engineering. I recommend getting started by enrolling in our Software Development with Cursor course for free. You can also take the Introduction to Claude Models course and the OpenAI Fundamentals skill track.