GPT-5.4 vs Claude Opus 4.6: Which One Wins in 2025?
What's up, frontier model watchers — if you've been watching the OpenAI vs Anthropic race with a spreadsheet open and a coffee getting cold, this one's for you.
I've spent the last few weeks running both models through real tasks. Not demo prompts. Actual workflows: long-document analysis, agent runs, code review loops, web research chains. And I keep getting asked the same thing: "Which one should I actually build on?"
Here's my honest take — with the benchmarks to back it up.
What Each Model Is Built For
GPT-5.4: Unified Reasoning + Computer Use
According to OpenAI's official launch page, GPT-5.4 is positioned as their "most capable and efficient frontier model for professional work." The design philosophy is convergence: GPT-5.4 merges the coding capabilities of GPT-5.3-Codex with improved agentic workflows, computer use (native mouse and keyboard control), and document/spreadsheet handling — all in one model.
The key architectural bet OpenAI made: a real-time router decides internally whether to answer quickly or think longer, based on task complexity. You don't need to manually switch between "fast" and "thinking" modes for most workflows. GPT-5.4 Thinking adds an upfront plan before reasoning starts — you can redirect it mid-thought before it finalizes an answer.
The short version: GPT-5.4 is built for breadth. Computer use, spreadsheets, presentations, coding, research — it wants to be the one model that handles all of them without you managing separate tools.
Claude Opus 4.6: Depth, Agentic Planning, and Long-Context Reliability
Anthropic's official positioning is sharper and more specific: Opus 4.6 is built for sustained, high-stakes work — tasks that require planning across large codebases, long document sets, or extended reasoning chains that other models abandon mid-way.
The headline engineering change is adaptive thinking: the model itself decides how much reasoning depth a task needs, rather than applying uniform effort to everything. Simple tasks get fast responses; hard tasks trigger deeper chain-of-thought without you configuring it. Agent teams in Claude Code let multiple Claude instances work on a task in parallel, each handling independent subtasks before merging results.
The short version: Opus 4.6 is built for depth. When the task is genuinely hard — multi-step agentic coding, long-context document work, complex professional reasoning — Anthropic is betting Opus 4.6 goes further without breaking.
Head-to-Head Benchmark Comparison
Reasoning / Knowledge Work
On economically valuable knowledge work — finance, legal, research — Opus 4.6 outperforms GPT-5.2 by around 144 Elo points on GDPval-AA, translating to winning roughly 70% of head-to-head comparisons. GPT-5.4 wasn't benchmarked against Opus 4.6 directly on GDPval-AA at launch, but given GPT-5.4's positioning as an efficiency upgrade over 5.2, the gap likely persists.
Computer Use (OSWorld-Verified)
GPT-5.4 leads here — and this is the gap that actually matters for autonomous desktop agents. Opus 4.6 reaches 72.7% on OSWorld, its best computer-use result to date, but GPT-5.4's 75.0% is the first frontier model score above human expert baseline. For agentic automation workflows involving real desktop or browser control, GPT-5.4 has a real edge.
Coding
This is where the picture gets complicated. GPT-5.3-Codex achieves 77.3% on Terminal-Bench 2.0 and 56.8% on SWE-Bench Pro, while Opus 4.6 leads on SWE-Bench Verified at 80.8%. The two benchmarks measure different things: Terminal-Bench tests autonomous command-line execution; SWE-Bench Verified tests real-world bug resolution in GitHub repos. For production coding agents, Opus 4.6's SWE-Bench lead is the more relevant number. For raw terminal automation, GPT's Codex lineage still holds the crown.
Note: GPT-5.4 inherits the Codex coding architecture but hasn't published standalone Terminal-Bench numbers at this writing.
Search / Web Browsing (BrowseComp)
Standard GPT-5.4 hits 82.7% on BrowseComp, with GPT-5.4 Pro scoring 89.3%. Opus 4.6 single-agent scores 84.0%, rising to 86.8% with a multi-agent harness. At the Pro tier, GPT-5.4 Pro leads. For teams not on the Pro plan, Opus 4.6 is the stronger web research model.
Context Window
GPT-5.4 has a larger default context window. Opus 4.6's 1M token beta is API-only and carries a premium pricing tier above 200K tokens. On the 8-needle 1M MRCR v2 benchmark, Opus 4.6 scores 76% at long-context retrieval — a qualitative shift compared to Sonnet 4.5's 18.5%, but GPT-5.4's 1.05M default gives it a deployment simplicity advantage.
Pricing Comparison
All figures are standard API rates as of March 2026, per 1M tokens.
Sources: OpenAI pricing page · Anthropic Claude pricing
GPT-5.4 is meaningfully cheaper on input tokens — roughly half the price per million. Opus 4.6 pricing remains at $5/$25 per million tokens, with up to 90% savings with prompt caching and 50% with batch processing. For high-volume applications where input tokens dominate (RAG pipelines, document processing at scale), GPT-5.4 has a significant cost advantage. For output-heavy workflows where caching applies, Opus 4.6's 90% prompt caching discount can close the gap.
Real-World Use Case Fit
GPT-5.4 Is Better If You...
- Need native computer use agents — desktop automation, form-filling, legacy portal navigation. The OSWorld gap (75.0% vs 72.7%) is small but real, and GPT-5.4's computer-use architecture is more mature.
- Are building high-volume document pipelines — input-heavy workflows where GPT-5.4's $2.50/M input pricing cuts costs roughly in half vs Opus 4.6.
- Want one model for spreadsheets, slides, and web research — GPT-5.4's unified design handles all three without model switching.
- Need the largest default context window — 1.05M tokens out of the box, no beta headers required.
For a deeper look at computer use specifics, see GPT-5.4 Computer Use: What It Can Actually Do.
Claude Opus 4.6 Is Better If You...
- Are building production coding agents — 80.8% on SWE-Bench Verified is a real gap over GPT-5.4's inherited Codex score of ~57.7%. For bug resolution in real codebases, Opus 4.6 is the stronger choice.
- Need sustained long-horizon tasks — Opus 4.6 has a 50% task-completion time horizon of 14 hours and 30 minutes as estimated by METR, the longest of any frontier model tested. For overnight agentic runs, this matters.
- Work in finance, legal, or research — GDPval-AA and BigLaw Bench results make Opus 4.6 the current leader for professional knowledge work. Opus 4.6 achieved the highest BigLaw Bench score of any Claude model at 90.2%.
- Need parallel agent coordination — Agent Teams in Claude Code is a real capability for distributing large tasks across multiple Claude instances simultaneously.
Limitations of Each
GPT-5.4 limitations:
- Computer use still fails on highly dynamic interfaces and non-standard UI frameworks. The ~25% failure rate on OSWorld means unsupervised production deployment on sensitive systems remains risky.
- The 272K context pricing cliff is a real cost trap for long-session workflows — above that threshold, input pricing doubles.
- GPT-5.4 Pro tier (needed for 89.3% BrowseComp and maximum computer use performance) is significantly more expensive than the standard tier.
Claude Opus 4.6 limitations:
- 1M token context is beta-only on the API — not available through claude.ai or third-party cloud platforms yet.
- SWE-Bench Verified shows a minor regression vs Opus 4.5 (80.8% vs 80.9%) — not material, but worth watching.
- Higher base pricing ($5/$25) makes it expensive for high-volume, input-heavy pipelines without aggressive prompt caching.
- Opus 4.6 can add cost and latency on simpler tasks when using high effort — Anthropic recommends dialing effort down to medium for straightforward work.
Verdict & Decision Framework
Stop me if this sounds familiar: "Which model is better?" is the wrong question. The right question is: "Which model is better for this specific workflow?"
Here's the framework I actually use:
Pick GPT-5.4 if: your workflow involves computer use automation, high-volume document processing, or you need the lowest input token cost. It's also the simpler operational choice — one model, broad capability, large default context.
Pick Opus 4.6 if: your workflow involves production code agents, complex long-horizon reasoning, or professional domain work (legal, finance, research). The SWE-Bench lead and 14.5-hour task horizon are genuine differentiators for serious agentic deployments.
The honest routing rule for March 2026:
Neither model wins everything. Anyone telling you otherwise has a vendor preference or hasn't run both on real tasks.
At Macaron, we built our agent to handle the kind of multi-step workflow handoffs that both models get stuck on — translating conversations into structured, trackable tasks without losing context across steps. If you want to test how your workflow holds up in practice, try running a real project through Macaron and see what actually gets to done.
Related Articles
