GPT-5.4 vs Claude Opus 4.6 - Computer Use Meets Agent Teams
GPT-5.4 leads on computer use and enterprise productivity at half the price. Claude Opus 4.6 leads on coding, agent teams, and long-context retrieval. Here is where each model wins.
6 min read
GPT-5.4 launched today with native computer use that beats human desktop performance and a 1M token context window at $2.50/$15 per million tokens. Claude Opus 4.6 has been the agentic benchmark since February, leading on coding, tool calling, and enterprise knowledge work at $5.00/$25 per million tokens.
Both models now share 1M context windows. Both target enterprise agentic workflows. The differences are in what each model does best - and how much it costs to get there.
TL;DR
- Choose GPT-5.4 if you need computer use (desktop automation, browser navigation), enterprise productivity (spreadsheets, financial modeling), or want frontier performance at lower cost ($2.50/$15 vs $5/$25)
- Choose Claude Opus 4.6 if you need the best coding performance (SWE-bench 80.8% vs 77.2%), multi-agent coordination (agent teams), reliable long-context retrieval (76% MRCR v2 at 1M tokens), or enterprise tool calling (91.9% tau2-bench)
Quick Comparison
| Feature | GPT-5.4 | Claude Opus 4.6 |
|---|---|---|
| Developer | OpenAI | Anthropic |
| Release Date | March 5, 2026 | February 5, 2026 |
| Context Window | 1M | 1M (beta) |
| Output Limit | Not disclosed | 128K |
| Input Modalities | Text, images | Text, images |
| API Pricing (Input) | $2.50/M | $5.00/M (<=200K), $10.00/M (>200K) |
| API Pricing (Output) | $15.00/M | $25.00/M (<=200K), $37.50/M (>200K) |
| Computer Use | Native (built-in) | Via Claude Code tools |
| Agent Teams | No | Yes (native coordination) |
| Variants | Standard, Thinking, Pro | Single model with effort levels |
| SWE-bench Verified | 77.2% | 80.8% |
| OSWorld-Verified | 75.0% | 72.7% |
| Terminal-Bench 2.0 | 75.1% | 65.4% |
| GDPval | 83.0% | 1,606 Elo (GDPval-AA) |
| GPQA Diamond | 92.8% | 91.3% |
| tau2-bench Retail | - | 91.9% |
GPT-5.4: Computer Use and Breadth
GPT-5.4's defining capability is native computer use. The model operates in two modes: code mode (writing Python with Playwright) and screenshot mode (issuing raw mouse and keyboard commands from visual input). It runs a build-run-verify-fix loop autonomously, confirming outcomes before declaring tasks complete.
The OSWorld-Verified score of 75.0% tops the human baseline of 72.4% and nearly doubles GPT-5.2's 47.3%. This is the largest single-generation improvement on any desktop automation benchmark. Terminal-Bench 2.0 at 75.1% leads Claude's 65.4% by nearly 10 points.
The enterprise productivity story is equally strong. GDPval at 83.0% covers 44 occupational categories of knowledge work. The spreadsheet modeling benchmark hits 87.5% on tasks resembling junior investment banking analyst work. Native Excel and Google Sheets integrations let GPT-5.4 read cells, run analysis, and write formulas without external tooling.
At $2.50/$15 per million tokens, GPT-5.4 costs half what Claude Opus 4.6 charges. For high-volume production workloads, this gap compounds. Processing 100 million output tokens costs $1,500 with GPT-5.4 versus $2,500 with Opus (or $3,750 at Opus's long-context rate).
Where GPT-5.4 falls short is coding and multi-agent workflows. SWE-bench Verified at 77.2% trails Opus by 3.6 points - meaningful for teams where code quality is the primary concern. There's no equivalent to Claude's agent teams for coordinating parallel sub-agents on complex projects.
Claude Opus 4.6: Coding and Agent Coordination
Opus 4.6 was built for sustained agentic execution. SWE-bench Verified at 80.8% is the highest coding score among frontier models. Tau2-bench Retail at 91.9% shows near-perfect tool calling reliability. BrowseComp at 84.0% leads on web research tasks.
Agent teams are the differentiator. Opus can spawn and coordinate parallel sub-agents through Claude Code, decomposing complex tasks into independently executed subtasks. Anthropic demonstrated this with 16 agents building a 100,000-line C compiler that passes 99% of the GCC test suite. GPT-5.4 has no equivalent - it operates as a single agent.
The 1M context window comes with proven retrieval accuracy. MRCR v2 at 76.0% at the full million-token window shows Opus actually finds information accurately at extreme context lengths. GPT-5.4's 1M window is new and retrieval accuracy data is not yet available from independent benchmarks.
Opus supports 128K output tokens - double the previous generation and relevant for long code generation, document synthesis, and detailed analysis tasks. GPT-5.4's output limit hasn't been published.
The cost penalty is real. Opus at $5.00/$25.00 is 2x more expensive on input and 1.67x more on output compared to GPT-5.4. Above 200K tokens, the gap widens further: Opus jumps to $10.00/$37.50 while GPT-5.4 maintains flat pricing across its context window.
Benchmark Comparison
| Benchmark | GPT-5.4 | Claude Opus 4.6 | Winner |
|---|---|---|---|
| OSWorld-Verified (desktop) | 75.0% | 72.7% | GPT-5.4 (+2.3) |
| Terminal-Bench 2.0 (agentic) | 75.1% | 65.4% | GPT-5.4 (+9.7) |
| GDPval (knowledge work) | 83.0% | - | GPT-5.4 |
| SWE-bench Verified (coding) | 77.2% | 80.8% | Claude (+3.6) |
| GPQA Diamond (science) | 92.8% | 91.3% | GPT-5.4 (+1.5) |
| MMMU Pro (visual) | 81.2% | 77.3% | GPT-5.4 (+3.9) |
| BrowseComp (web research) | - | 84.0% | Claude |
| tau2-bench Retail (tool calling) | - | 91.9% | Claude |
| ARC-AGI-2 (reasoning) | 73.3% | 68.8% | GPT-5.4 (+4.5) |
| Humanity's Last Exam | - | 53.1% | Claude |
| MRCR v2 @ 1M (retrieval) | - | 76.0% | Claude |
GPT-5.4 leads on benchmarks involving computer interaction and broad knowledge. Claude leads on coding, tool calling, web research, and long-context retrieval. The two models have truly different strengths rather than one dominating across the board.
Pricing Analysis
| Cost Factor | GPT-5.4 | Claude Opus 4.6 |
|---|---|---|
| Input (per 1M tokens) | $2.50 | $5.00 (<=200K) / $10.00 (>200K) |
| Output (per 1M tokens) | $15.00 | $25.00 (<=200K) / $37.50 (>200K) |
| Pro/Fast tier | $30/$180 (Pro) | $30/$150 (Fast, preview) |
| Batch API | TBD | $2.50/$12.50 (50% off) |
| 100M output tokens/month | $1,500 | $2,500 - $3,750 |
GPT-5.4 is consistently cheaper at standard rates. The gap is most pronounced for workloads using the full 1M context window, where Opus's long-context pricing kicks in. Opus's Batch API (50% discount) closes the gap for asynchronous workloads, but GPT-5.4 retains a cost advantage at standard rates.
Both models offer premium tiers at roughly similar pricing: GPT-5.4 Pro at $30/$180 and Claude's Fast mode preview at $30/$150.
Verdict
Choose GPT-5.4 if your workload involves desktop automation, browser navigation, spreadsheet analysis, or any task where the model needs to interact with software rather than just generate text. GPT-5.4's 75% OSWorld score is a real capability gap - it can reliably complete multi-step desktop tasks that Claude handles less consistently. The 2x cost advantage and three-variant system (base, Thinking, Pro) also make it the better choice for budget-conscious teams that want to route queries by difficulty.
Choose Claude Opus 4.6 if coding quality is your primary metric, if you need multi-agent coordination for complex projects, or if your workflow depends on reliable retrieval from very long contexts. The 3.6-point SWE-bench lead, 91.9% tool calling accuracy, and agent teams represent capabilities GPT-5.4 doesn't match. For development teams using Claude Code, Opus remains the stronger option. The cost premium is the trade-off.
For most teams, the answer will be both. Route computer use tasks, desktop automation, and bulk knowledge work to GPT-5.4. Route complex coding, multi-agent projects, and long-context retrieval tasks to Claude. This is the model routing pattern we described in how to choose the right LLM in 2026 - the frontier is splitting into specializations, and picking one model for everything means leaving performance on the table.
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