Claude Computer Use Review: Hands-On Testing (2026)
GPT-5.4 Review 2026: Computer Use and 1M Context
OpenAI shipped GPT-5.4 on March 5, 2026. I’ve been running it for eight days. The short version: it’s the first OpenAI model that can operate a real desktop better than the average human, and the pricing undercuts the competition by a wide margin.
The longer version is more nuanced. GPT-5.4’s strengths cluster in specific areas, and whether it’s worth paying for depends almost entirely on whether those areas overlap with your actual work.
Quick Verdict: GPT-5.4
Aspect Rating Overall Score ★★★★½ (4.5/5) Best For Agentic workflows, long-document analysis, professional coding Minimum Access ChatGPT Plus, $20/month API Pricing $2.50/1M input, $15/1M output (up to 272K tokens) Context Window 1M tokens (API) Computer Use 75% OSWorld-Verified (vs 72.4% human baseline) Coding 57.7% SWE-bench Pro (vs Claude Opus 4.6’s 45.9%) Intelligence Index 57 (tied with Gemini 3.1 Pro Preview; Claude Opus 4.6: 53) Bottom line: GPT-5.4 is OpenAI’s best model, with a legitimate lead on computer use and software engineering benchmarks. The tool search API cuts token costs by 47% for agent developers. For everyday ChatGPT users, the improvements are real but less dramatic. For developers building agentic pipelines, this is the release to evaluate.
What’s Actually New Here
Three capabilities define this release: native computer use, a 1M token context window, and a tool search API that cuts agent costs by 47%. Each section below covers one in depth.
The brief version: GPT-5.4 crosses the human baseline on GUI navigation benchmarks, closes the context window gap with competitors, and introduces a smarter way to handle large tool registries in agentic pipelines. Released March 5, 2026 across ChatGPT, the API, Codex, and GitHub Copilot.
Computer Use: The Numbers Behind the Headline
The 75% OSWorld score isn’t marketing. OSWorld-Verified tests an AI on real GUI navigation: actual desktop environments, real software, no API shortcuts. The model sees screenshots, plans actions, clicks, types, and evaluates what happened.
GPT-5.2 scored 47.3% on the same benchmark. GPT-5.4 is at 75.0%. That’s a 58% relative improvement in one release cycle, crossing the 72.4% human benchmark in the process.
In practice, what this means: An AI agent running GPT-5.4 can fill out multi-step web forms, move data between applications without an API, operate desktop software, and navigate admin UIs that were previously out of reach for automation. The bottleneck for a lot of workflow automation has been “this tool doesn’t have an API.” GPT-5.4 reduces how much that matters.
The honest caveat: 75% success rate means 1-in-4 attempts fail. That’s not reliable enough for unsupervised critical workflows. You need error handling, retry logic, and human review for anything where a failed attempt has consequences. For supervised or semi-automated workflows, it’s highly practical today.
For a direct head-to-head comparison of GPT-5.4 and Claude Opus 4.6 specifically on agentic tasks, the GPT-5.4 vs Claude Opus 4.6 comparison goes deeper on workflow-specific performance.
The Tool Search API: Why Developers Should Care
This feature doesn’t get enough coverage. For anyone building AI agents with large tool registries (which is increasingly everyone doing serious agentic work), this is the most practically valuable new capability in GPT-5.4.
The problem it solves: Large agent systems have dozens or hundreds of available tools. Loading all tool definitions into context at conversation start has two costs: token cost (definitions are verbose) and reasoning cost (the model has more context to manage). Naive tool loading at scale gets expensive and degraded fast.
What tool search does: Instead of loading all definitions upfront, the API queries for relevant tool definitions based on the current task context. The model retrieves what it needs when it needs it.
The result: 47% reduction in token usage at identical accuracy on OpenAI’s internal benchmarks. That’s not a rounding error. For a production agentic pipeline running thousands of calls per day, that’s close to halving infrastructure costs while maintaining the same output quality.
This feature is only accessible through the API — ChatGPT Plus users won’t interact with it directly. But it’s significant for anyone building on top of GPT-5.4.
SWE-bench Pro: GPT-5.4 Leads on Software Engineering
The benchmark that surprised me most: GPT-5.4 scores 57.7% on SWE-bench Pro against Claude Opus 4.6’s 45.9%. SWE-bench Pro tests real-world GitHub issues — actual bugs from production codebases, not synthetic coding puzzles. The 11.8-point gap is substantial.
That’s a notable shift from the historical narrative where Claude consistently led on coding. Opus 4.6 is a strong coding model (see our Claude Opus 4.6 review for full coverage), but GPT-5.4 is now ahead on this specific benchmark.
What SWE-bench Pro measures matters: identifying root causes in existing codebases, navigating unfamiliar code, producing patches that actually fix the reported issue. This is the kind of coding work that matters in professional development, not just “write a function that reverses a string.”
The caveat I’d add: Benchmarks are controlled environments. I’ve noticed GPT-5.4 is strong at structured debugging when given clear context, but Claude still feels more nuanced on ambiguous architectural decisions. The benchmark lead is real; whether it translates to your specific codebase depends on what kind of coding work you’re doing.
For a full picture of how these models compare for developers specifically, the best AI coding assistants guide covers the full landscape.
Intelligence Index: Tied at the Top
The Artificial Analysis Intelligence Index puts GPT-5.4 and Gemini 3.1 Pro Preview tied at 57, with Claude Opus 4.6 at 53. This is a composite score across reasoning, coding, and knowledge benchmarks.
The tie with Gemini 3.1 is notable. Gemini 3.1 Pro Preview holds the largest context window advantage (2M tokens), better pricing on standard queries, and strong multimodal capabilities. GPT-5.4 matches it on the composite intelligence score while winning on computer use, SWE-bench Pro, and tool search.
The practical interpretation: right now, the three frontier models are close enough that use case matters more than picking the “best” model. GPT-5.4 isn’t ahead of everything on everything — it’s the best choice for specific workloads, not a universal winner.
Pricing Breakdown
| Access | Cost | Notes |
|---|---|---|
| ChatGPT Plus | $20/month | GPT-5.4 Thinking, 80 msgs/3hr |
| ChatGPT Pro | $200/month | Unlimited GPT-5.4 Pro, dedicated GPU |
| API (≤272K tokens) | $2.50 input / $15 output per 1M | Competitive |
| API (>272K tokens) | $5.00 input / $15 output per 1M | Watch this threshold |
| API Pro tier | $30.00 input per 1M | Extended reasoning |
| GitHub Copilot | Included in plan | GA since March 5 |
The API input pricing at $2.50/1M is competitive. For comparison, Claude Opus 4.6 runs $15/1M input — GPT-5.4 is 6x cheaper on input tokens. For high-volume inference, that gap matters.
The tiered context pricing is new for OpenAI and worth factoring carefully. If your workload regularly exceeds 272K tokens, actual input costs double. A 1M-token request in the extended range runs roughly $5 in input tokens alone. Model that before migrating high-context production workloads.
My Hands-On Experience
What Works
Long-document analysis is measurably better. I uploaded a 240-page technical procurement document this week and asked GPT-5.4 to identify all SLA commitments, response time requirements, and penalty clauses. Previous GPT models at this scale either missed sections or produced unreliable summaries. GPT-5.4 was systematic and accurate. It flagged items I would have caught myself and several I wouldn’t have.
The reasoning chain in Thinking mode is actually useful. Watching the model work through a complex architecture decision, I could see where it was evaluating tradeoffs versus where it had already resolved something. That transparency makes it easier to identify when the reasoning goes off track.
Hallucination reduction is real, not marketing. I’ve been deliberately asking GPT-5.4 questions where I know the answer, including edge cases where GPT-5.2 would confidently confabulate. The Thinking variant in particular hedges appropriately on uncertain information rather than filling in gaps with plausible-sounding fabrications.
What Doesn’t Work
The 80-message limit in ChatGPT Plus is frustrating. If you’re doing serious professional work with GPT-5.4 Thinking, 80 messages every 3 hours is a real constraint. You’ll hit it mid-project. The $200/month Pro plan removes it but that’s a significant jump from $20.
Computer use isn’t reliable enough for hands-off automation. I built a test workflow that automated filling out a multi-step SaaS admin interface. GPT-5.4 completed it successfully about 3 out of 4 times. The 1-in-4 failure usually came from unexpected UI states. It required a human in the loop. That’s fine for assisted automation, not fine if you’re trying to fully automate.
Cost scaling at high context. I’ve been running some large-context analysis tasks where I hit the 272K threshold repeatedly. Input costs at $5.00/1M in the extended range add up faster than the headline $2.50 figure suggests. Budget for it.
GPT-5.4 vs The Competition
| Capability | GPT-5.4 | Claude Opus 4.6 | Gemini 3.1 Pro |
|---|---|---|---|
| Intelligence Index | 57 | 53 | 57 |
| Computer Use | 75% OSWorld | 72.7% | Not reported |
| SWE-bench Pro | 57.7% | 45.9% | — |
| API Input Price | $2.50/1M | $15/1M | $4/1M |
| Context Window | 1M | 1M | 2M |
| Tool Search API | Yes (47% token savings) | No | No |
GPT-5.4 leads on coding, computer use, and pricing. Gemini leads on context window. Claude leads on complex reasoning benchmarks.
The pricing gap between GPT-5.4 and Claude Opus 4.6 is 6x on input tokens. At scale, that’s a meaningful cost advantage unless your workload specifically requires Anthropic’s reasoning depth or MCP integration.
For the full model landscape, see AI models compared 2026.
Who Should Use GPT-5.4
Strong fit:
- Developers building AI agents that interact with real software through screen-based interfaces
- Teams processing large document sets that benefit from 1M context
- Engineers building agent pipelines who want to optimize token costs with the tool search API
- GitHub Copilot users who get GPT-5.4 automatically
- Anyone doing software engineering tasks at the frontier — the SWE-bench Pro numbers are real
Evaluate carefully:
- Current GPT-5.3 Instant users primarily doing fast chat who don’t need computer use
- Teams with Claude workflows built around MCP server integrations — switching costs are real
- Anyone who needs fully automated, unsupervised computer use for production workflows
Look elsewhere:
- Teams needing 2M+ context (Gemini 3.1 Pro still has the largest window)
- Workloads where Claude Opus 4.6’s reasoning depth on ambiguous problems is the primary need
- Budget-constrained projects where the free tier suffices (see our best free AI tools guide)
How to Get Started
ChatGPT users:
- Open ChatGPT and select GPT-5.4 Thinking from the model picker
- Plus subscribers get 80 messages/3 hours; Pro subscribers get unlimited access
API users:
- Update your model parameter to
gpt-5.4in your API calls - Watch the 272K token threshold — input pricing doubles past it
- Enable tool search API to reduce token costs for large tool registries
- Use Codex for computer-use agentic workflows
GitHub Copilot users: GPT-5.4 is already live as the default model. No action needed.
The Bottom Line
GPT-5.4 is OpenAI’s best model. The computer-use capability at 75% OSWorld is a genuine step forward. The SWE-bench Pro lead over Claude is real and matters for coding agents. The tool search API is the most underrated feature for developers. And at $2.50/1M input tokens, the pricing is hard to argue with.
The honest qualification: “best model” doesn’t mean “right model for everything.” Claude Opus 4.6 is still stronger on complex reasoning benchmarks. Gemini 3.1 Pro has a larger context window. The intelligence index tie at 57 is a signal that the frontier is crowded.
For agentic workflows, coding agents, and long-document analysis, GPT-5.4 is the model I’m reaching for first now. For deep reasoning tasks where I’m willing to pay more for nuance, Claude Opus 4.6 still earns its premium. Both can be worth having.
Frequently Asked Questions
What does GPT-5.4 cost?
ChatGPT Plus is $20/month and includes GPT-5.4 Thinking with 80 messages every 3 hours. ChatGPT Pro at $200/month gives unlimited GPT-5.4 Pro access. API pricing is $2.50 per million input tokens and $15 per million output tokens up to 272K tokens, doubling to $5.00/1M input past that threshold.
How does GPT-5.4 compare to Claude Opus 4.6 on coding?
GPT-5.4 scores 57.7% on SWE-bench Pro versus Claude Opus 4.6’s 45.9%. That’s the most comprehensive real-world coding benchmark, testing actual GitHub issues on production codebases. GPT-5.4 leads on that metric. Claude still holds advantages on some reasoning benchmarks and has a more mature MCP-based agent ecosystem.
What is the tool search API?
The tool search API lets GPT-5.4 retrieve tool definitions on demand rather than loading all tool definitions into context upfront. For agent systems with large tool registries, this reduces token usage by 47% at identical accuracy. It’s available through the API, not through the ChatGPT interface.
Is GPT-5.4 really better at computer use than humans?
On the specific OSWorld-Verified benchmark, yes: 75% vs 72.4% human baseline. But benchmarks measure structured tasks in controlled environments. The 25% failure rate on real-world computer use means human oversight is still essential for production workflows. It’s better than any previous OpenAI model, and it’s competitive with other frontier models, but it isn’t “set and forget.”
What changed in the Artificial Analysis Intelligence Index?
GPT-5.4 and Gemini 3.1 Pro Preview are currently tied at 57 on the Artificial Analysis Intelligence Index. Claude Opus 4.6 sits at 53. This composite score measures performance across reasoning, coding, and knowledge. The tie signals that the frontier is crowded. No single model dominates across all dimensions.
Can I access GPT-5.4 on the free ChatGPT tier?
No. GPT-5.4 requires at minimum a ChatGPT Plus subscription at $20/month. Free tier users stay on GPT-4o and older models. There’s no announced timeline for GPT-5.4 to reach the free tier.
How does the 1M context window pricing work?
Standard API pricing ($2.50/1M input) applies up to 272K tokens. Past that threshold, input pricing doubles to $5.00/1M. Output pricing stays at $15/1M throughout. ChatGPT Plus subscribers deal with message limits (80/3hr) rather than per-token context pricing.
Last updated: March 13, 2026. Benchmarks sourced from OpenAI’s GPT-5.4 announcement. Pricing verified against openai.com/api/pricing. Intelligence Index from Artificial Analysis. SWE-bench Pro results from OpenAI’s system card.
