For the first time in months, OpenAI and Anthropic have their best models out at the same time. No waiting for one lab to catch up. Both GPT-5.4 and Claude Opus 4.6 are live, head-to-head, right now.

I’ve spent the past week running both through the same agentic workflows: multi-step automation, long-horizon coding tasks, computer use, document analysis, and real-world agent pipelines. This isn’t a benchmark comparison. It’s what actually happened when I made both models do the same work.

Quick Verdict: GPT-5.4 vs Claude Opus 4.6

Aspect	GPT-5.4	Claude Opus 4.6
Best For	Computer use, cost-sensitive agentic pipelines	Coding agents, complex reasoning, MCP workflows
API Pricing	$2.50/1M input	$5.00/1M input
Context Window	1M tokens (API)	1M tokens
Computer Use	75% OSWorld-Verified	72.7% OSWorld
Reasoning (ARC AGI 2)	~54% (GPT-5.2 baseline)	68.8%
Professional Performance	83% vs experts (44 occupations)	Leads frontier coding benchmarks
Web Access	Yes (browsing built-in)	No
Agent Framework	Codex + API	MCP servers + Claude Code agent teams

Bottom line: GPT-5.4 wins on computer use and cost. Claude Opus 4.6 wins on reasoning depth and coding quality. For most agentic workflows, the choice depends on whether you’re building screen-based automation or code-heavy agents.

The Short Version (If You’re in a Hurry)

Use GPT-5.4 when you need:

• Computer use automation that interacts with real UIs, web apps, or desktop software
• Cost-efficient agentic pipelines at scale ($2.50/1M input vs $5.00/1M)
• Web browsing integrated into agent tasks
• GitHub Copilot integration with the same model

Use Claude Opus 4.6 when you need:

• The best code reasoning on complex, multi-file agentic tasks
• MCP server integrations for tool-use workflows
• Deep analysis agents that need to understand why something is broken, not just what
• Agent teams in Claude Code for parallel agentic execution

Computer Use: GPT-5.4’s Headline Feature

This is the capability that makes GPT-5.4 worth evaluating seriously for agentic workflows.

GPT-5.4 achieves a 75% success rate on OSWorld-Verified, a benchmark that measures an AI’s ability to navigate a real desktop using screenshots, mouse clicks, and keyboard commands. The human baseline on the same test is 72.4%. That’s not a gap you can attribute to benchmarks being easy.

Claude Opus 4.6 scores 72.7% on OSWorld, which is functionally human-level performance. Both models are operating in the same range. But GPT-5.4 holds a ~2.3 percentage point advantage on this specific benchmark.

What matters more than the numbers: GPT-5.4’s computer use is built into Codex and the API at the architecture level. It’s not an add-on. For workflows where you need an agent to navigate a web app, fill out forms, operate legacy software without an API, or move data between tools manually, GPT-5.4 is where I’d start.

The honest caveat: 75% success means 1-in-4 attempts fails. You need error handling, retry logic, and human review loops. This is semi-automated territory, not full autonomy.

For more on building agent pipelines with computer use, see our guide to building AI agents in 2026.

Coding Agents: Claude Opus 4.6’s Clearest Advantage

For code-heavy agentic workflows, Claude Opus 4.6 is still the model I reach for first.

The gap shows most on complex, multi-file problems. Opus 4.6’s Terminal-Bench 2.0 score of 65.4% leads all frontier models on real-world coding tasks. More meaningfully, I watched Opus 4.6 trace an intermittent auth failure across four services, identify a race condition in the token refresh logic, and explain exactly why it only appeared under load. GPT-5.4 found related code. Opus found the root cause.

That distinction matters when you’re running coding agents on production systems. An agent that identifies symptoms is useful. An agent that understands causality is genuinely useful.

Agent teams in Claude Code add another dimension here. You can spin up multiple Opus agents that divide a codebase into layers (database, API, frontend) and coordinate autonomously. I’ve run a few of these sessions. When they work, the productivity gain is significant. One session handled a large TypeScript refactor I’d been putting off: three parallel agents, consistent output, no manual coordination.

GPT-5.4 via Codex is strong for coding too. It’s faster, and on SWE-Bench Pro, the results are competitive. But for architecture-level reasoning on complex systems, Opus 4.6 is ahead.

For a deeper coding-specific comparison, our GPT Codex vs Claude Opus agentic coding breakdown covers this in more detail.

Reasoning Quality: Where the Gap Is Real

GPT-5.4 claims professional-grade performance: matching or exceeding industry professionals in 83% of comparisons across 44 occupations on OpenAI’s GDPval benchmark. That’s a meaningful result for professional task automation.

But on raw reasoning benchmarks, Claude Opus 4.6 operates at a different level.

ARC AGI 2 is the benchmark designed to resist pattern-matching by testing genuine reasoning on novel problems. Claude Opus 4.6 scores 68.8%. GPT-5.2 (the reference point we have for OpenAI on this benchmark) scores 54.2%.

That 14-point gap on a benchmark built to test actual reasoning matters for agent workflows where the model needs to handle unexpected situations, ambiguous instructions, or novel edge cases. A scripted agent following a known sequence is one thing. An agent that needs to adapt when something breaks is another.

Claude Opus 4.6 also leads every frontier model on Humanity’s Last Exam, the multidisciplinary reasoning benchmark meant to be exceptionally hard. For agentic tasks that require planning, adaptive decision-making, or handling ambiguity, that reasoning advantage has real-world implications.

For more context on how these models stack up on reasoning, see our AI models compared guide.

Context Windows: Both Are Now at 1M Tokens

Six months ago, context window size was a meaningful differentiator. It’s less of one now.

GPT-5.4 offers 1M tokens via API, its largest context ever, up from 400K on GPT-5.3 Instant. There’s a pricing catch: input tokens past the 272K threshold double from $2.50 to $5.00/1M. For long-horizon agent sessions that push the full window, that adds up.

Claude Opus 4.6 also offers 1M tokens, up from 200K in Opus 4.5. The pricing doesn’t tier at a lower threshold, but the base rate ($5.00/1M input) is already higher than GPT-5.4’s standard rate.

In practice, both models handle the full window well. I’ve fed entire monorepos (400-600K tokens) to both and gotten coherent analysis. For most agentic workflows, you’re not pushing 1M tokens regularly anyway. The context window is a ceiling, not a daily consumption figure.

Tool Use and Agent Frameworks

This is where the ecosystem differences matter most.

Claude Opus 4.6 has the more mature agent framework through Model Context Protocol (MCP). Hundreds of MCP servers exist for connecting Claude to external tools: databases, APIs, file systems, web browsers, code execution environments. The integration ecosystem for Claude agents is genuinely rich, and Anthropic has been shipping features like agent teams explicitly designed for multi-step agentic work.

GPT-5.4 ships with native computer use baked in, plus strong integration with GitHub Copilot and the broader OpenAI ecosystem. For teams already using Azure or GitHub, the GPT-5.4 path is lower friction. The Codex integration specifically is built for agentic coding workflows.

Neither is universally better here. If your workflow requires MCP integrations or Claude Code agent teams, Opus 4.6 is the clearer path. If you’re building on top of OpenAI’s platform or need Copilot access, GPT-5.4 is more practical.

Pricing: GPT-5.4 Has a Real Advantage

Access Method	GPT-5.4	Claude Opus 4.6
API input (standard)	$2.50/1M tokens	$5.00/1M tokens
API output	$15.00/1M tokens	$25.00/1M tokens
Consumer plan	$20/mo (Thinking, 80 msgs/3hr)	$20/mo (Pro, rate-limited)
Pro plan	$200/mo (unlimited, dedicated GPU)	—
Context pricing	Doubles >272K input	Consistent rate
Batch pricing	Not announced	$2.50/$12.50 per 1M

For high-volume agentic pipelines, GPT-5.4’s pricing advantage is substantial. At 2x lower input cost per token, a pipeline running 10M input tokens per day saves $25,000/month compared to the same volume on Opus 4.6.

Anthropic’s batch pricing partially closes this gap for non-real-time workloads. At $2.50/$12.50 per 1M tokens with 24-hour delivery, Opus 4.6 batch pricing matches GPT-5.4’s standard rate.

For interactive agentic use where latency matters: GPT-5.4 is cheaper in real time. For bulk processing where you can wait: Opus batch pricing is competitive.

For the full pricing context, see our OpenAI Frontier Enterprise Review.

The Stuff Nobody Talks About

GPT-5.4 can browse the web. Claude Opus 4.6 cannot. For agents that need live information, current documentation, or real-time data, this is a hard differentiator. I keep hitting this wall with Opus agents when they need information from after their training cutoff. GPT-5.4 agents can solve this on their own. Opus agents can’t.

GPT-5.4 Thinking has message limits. 80 messages every 3 hours on ChatGPT Plus is tight for extended agent sessions. For running long agentic tasks interactively, you’ll either need the $200/month Pro plan or API access. Opus 4.6 on Claude Pro has rate limits too, but they hit differently in my experience.

Opus 4.6 is better at acknowledging uncertainty. When an Opus agent hits an edge case it can’t handle, it tends to tell you. When GPT-5.4 hits an edge case, it sometimes proceeds confidently in the wrong direction. For agents running in supervised pipelines, this matters. Confident failure is harder to catch than flagged uncertainty.

GPT-5.4 is 33% less likely to make false claims per response compared to GPT-5.2. That’s OpenAI’s figure. I’ve noticed the model is more cautious about making claims it can’t support, which is the right behavior for agents that need to produce trustworthy output. Opus 4.6’s hallucination rate remains low as well.

What I Actually Do

After a week of head-to-head testing, here’s how I split them:

Task	My Choice	Why
Web scraping + data extraction agents	GPT-5.4	Native web access
Computer use automation	GPT-5.4	Higher success rate, built into Codex
Production bug debugging	Opus 4.6	Root cause reasoning, not symptom matching
Large codebase analysis	Opus 4.6	Stronger reasoning at full context
Multi-step research with live data	GPT-5.4	Web browsing essential
Code refactoring agents	Opus 4.6	Agent teams, MCP file system access
High-volume document processing	GPT-5.4	2x cheaper, batch not needed
Strategic planning / analysis	Opus 4.6	Reasoning gap is real on complex problems
GitHub Copilot workflows	GPT-5.4	Already integrated

How to Decide

Choose GPT-5.4 if:

• You’re building agents that need to interact with software that doesn’t have a clean API
• Web browsing is part of your agent’s task requirements
• You need to run high-volume pipelines where input cost matters
• You’re already in the OpenAI ecosystem (GitHub Copilot, Azure, ChatGPT)
• Computer use automation is your primary use case

Choose Claude Opus 4.6 if:

• You’re building coding agents that need to understand complex systems
• Your workflows use MCP servers for tool integrations
• Reasoning quality and handling ambiguity matters more than speed
• You’re running agent teams with parallel workstreams in Claude Code
• You work on codebases where root-cause analysis saves hours of debugging

Get Both if:

• You’re running a serious agentic development environment
• Different workflows genuinely need different strengths
• Budget allows $40-60/month for both consumer plans
• You’re evaluating which to standardize on and need real data first

The Bottom Line

There’s no clean winner here. Both GPT-5.4 and Claude Opus 4.6 are genuinely excellent for agentic workflows, and they’re better at different things.

GPT-5.4 has the edge on: Computer use automation, cost efficiency at scale, web access for agents, and GitHub/Azure ecosystem integration. For the first time, OpenAI has a model that competes seriously with Anthropic on the agentic use cases where Claude has dominated.

Claude Opus 4.6 has the edge on: Reasoning depth, coding quality on complex systems, MCP-based tool integrations, and honest uncertainty handling. The ARC AGI 2 gap is real, and it shows in production on the tasks that are hardest.

My recommendation: if your primary agentic use case is computer use or web-browsing-enabled tasks, start with GPT-5.4. If you’re building code-heavy agents or complex reasoning pipelines, start with Opus 4.6. If you’re not sure, the consumer plans ($20/month each) let you test both against your actual workflows without a big commitment.

Start here:

• Try GPT-5.4 in ChatGPT →
• Try Claude Opus 4.6 →

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Frequently Asked Questions

Is GPT-5.4 better than Claude Opus 4.6 for agentic workflows?

It depends on the workflow. GPT-5.4 leads on computer use (75% vs 72.7% OSWorld), has native web browsing, and costs 2x less per input token. Claude Opus 4.6 leads on reasoning (68.8% vs ~54% on ARC AGI 2), coding quality, and MCP-based tool integrations. For screen-based automation: GPT-5.4. For code-heavy agents: Opus 4.6.

How does the pricing compare for agentic pipelines?

GPT-5.4 is $2.50/1M input tokens standard (doubles past 272K tokens). Claude Opus 4.6 is $5.00/1M input tokens with no tiering, or $2.50/1M in batch mode with 24-hour delivery. For real-time high-volume pipelines, GPT-5.4 is cheaper. For batch processing, they’re roughly equivalent.

Can Claude Opus 4.6 use a computer like GPT-5.4 can?

Both models support computer use. Claude Opus 4.6 scores 72.7% on OSWorld; GPT-5.4 scores 75.0%. In practice, both require human oversight and error handling. Neither is reliable enough for fully unsupervised production automation. GPT-5.4’s implementation is more deeply integrated into Codex and the API by design.

Which model is more reliable for agent tasks?

From my testing, Claude Opus 4.6 is better at flagging uncertainty rather than proceeding confidently in the wrong direction. When an agent hits an unexpected edge case, Opus is more likely to pause and ask than to barrel forward. GPT-5.4 has improved significantly on hallucination reduction (33% fewer false claims vs GPT-5.2), but Opus’s default caution remains an advantage for supervised agentic workflows.

Does Claude Opus 4.6 have web access?

No. As of March 2026, Claude Opus 4.6 cannot browse the web natively. GPT-5.4 has web browsing built in. For agents that need live information or current documentation, GPT-5.4 is the practical choice. Claude agents that need web access require an MCP server integration to enable it.

What’s the context window difference for long-horizon agent tasks?

Both support 1M tokens in the API now. GPT-5.4 added 1M tokens as of its March 2026 launch; Claude Opus 4.6 added 1M tokens with its February 2026 release. GPT-5.4’s input pricing doubles past 272K tokens, so sustained use of the full window costs more than the headline rate suggests.

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Last updated: March 11, 2026. Pricing and benchmarks verified against OpenAI’s GPT-5.4 announcement and Anthropic’s Claude documentation. Computer use scores from OSWorld-Verified evaluation. GDPval professional performance data from OpenAI’s system card.

Related: Claude Opus 4.6 Review | GPT-5.4 Review: Computer Use and 1M Context | Best AI Agents 2026 | AI Automation Workflows Guide