Claude Opus 4.7 vs. GPT-5.4: Which Frontier Model Should You Use?

GPT-5.4 launched on March 5, 2026 as OpenAI's flagship for professional work, consolidating coding and reasoning into a single general-purpose model. Six weeks later, on April 16, Anthropic released Claude Opus 4.7, built around a different bet: a model that handles long-horizon engineering autonomously and stays coherent across the kind of sessions where most agents fall apart.

This is a useful moment to compare them directly, though one thing to flag: this piece went out the same day Opus 4.7 launched, so the head-to-head numbers below are mostly vendor-reported. Treat them as a starting point, not a verdict.

If you want prior-generation context, we have a separate Claude Opus 4.6 deep-dive.

Opus 4.7 vs. GPT-5.4 Head-to-Head Comparison

Here is a quick reference before we get into each area. Pricing is where most of the interesting nuance lives, and we will cover that in its own section.

Key specs for both models compared. Image by Author.

Gemini 3.1 Pro is a real alternative if your primary need is bulk document processing or long legal analysis; it runs at lower per-token costs with a 2M context window. This article stays focused on the Anthropic versus OpenAI comparison.

How each vendor frames its model tells you a lot about what they expect you to use it for.

Model positioning and intended use

OpenAI positions GPT-5.4 as a unified general-purpose model. It absorbs the coding capabilities that previously lived in GPT-5.3-Codex, so developers no longer need to route requests to different endpoints by task type. One model, one endpoint, whatever the task.

Anthropic's pitch for Opus 4.7 is narrower: a model optimized for "coding, agents, computer use, and enterprise workflows," with long-horizon autonomy as the main distinction. You hand off hard engineering work and trust it to catch its own errors before reporting back. Worth flagging that Opus 4.7 is Anthropic's most capable generally available model, but not their top; Claude Mythos Preview sits above it, restricted to defensive cybersecurity workflows.

That distinction shows up at the extremes: very long-running coding sessions, or pipelines that chain dozens of tools.

Coding and agentic workflows

On repository-level coding, Opus 4.7 leads on the benchmarks each vendor chose to report (full numbers below). It introduced self-output verification, meaning the model checks its own work before reporting back, and Genspark specifically called out its loop resistance: Opus 4.7 is less likely to get stuck cycling on a single problem. That is the kind of thing you only care about once you have had an agent loop for 40 minutes on nothing.

GPT-5.4 leads Terminal-Bench 2.0 by about six points (75.1% versus 69.4%), though Anthropic flags that GPT-5.4's number comes from a self-reported harness. GPT-5.4 also introduced mid-response plan adjustment through Interactive Thinking: during complex reasoning, you can intervene before the model finishes generating and redirect it if the path looks wrong. Opus 4.7 has no equivalent. The SWE-bench gap is real, though: six points on a vendor-selected benchmark is useful signal, not a verdict.

Context window and long-context work

Both models support roughly 1M tokens; what differs is what happens to your bill when you use that context. Opus 4.7 charges a flat rate across the full window, so a 900K-token request costs the same per token as a 9K one. GPT-5.4 charges $2.50 per million under 272K input tokens, but cross that threshold and the entire session reprices. I will cover the exact numbers in the pricing section.

There is also a tokenizer wrinkle: Opus 4.7 can map the same text to up to 35% more tokens than 4.6. Per-token price is unchanged, but the effective cost per task can rise.

On actual long-context performance, partner testing put Opus 4.7 tied for the highest consistency score across six research modules at 0.715. RAG pipelines that fill close to the 1M limit should be tested on your own workload before relying on vendor benchmarks.

Tool use, multimodality, and environment interaction

The tool surfaces look similar on paper and differ more in practice. On OSWorld-Verified (desktop computer use), Opus 4.7 now leads at 78.0% versus GPT-5.4's 75.0%, with both above the human expert baseline of 72.4%. The picture flips on browser-based web research: GPT-5.4 hits 89.3% on BrowseComp (Pro variant) versus Opus 4.7's 79.3%. A single "computer use" headline obscures the desktop-versus-browser split.

Opus 4.7's headline multimodal upgrade is vision resolution: images up to 2,576 pixels on the long edge, roughly 3.75 megapixels, more than three times prior Claude models, processed at higher fidelity automatically with no API parameter. XBOW, a security testing partner, reported visual acuity jumping from 54.5% on Opus 4.6 to 98.5% on 4.7, the sharpest single-benchmark gain across any partner evaluation in this release.

The two also differ on tool architecture. GPT-5.4's tool search system loads definitions on demand rather than embedding all of them in the prompt, cutting token overhead in large tool ecosystems. Opus 4.7 reasons through a problem before reaching for tools, using fewer tool calls overall; tool usage increases at higher effort levels.

Steerability, reliability, and output style

Opus 4.7 takes instructions literally. It will not generalize from one item to another or infer requests you did not make, so prompts written for 4.6 can behave unexpectedly; Anthropic recommends re-tuning. The upside is reliability in long agentic loops: Ramp's engineering team noted significantly less step-by-step guidance was needed in multi-tool workflows, and Hexagon's testing found Opus 4.7 at low effort roughly equivalent to Opus 4.6 at medium.

Anthropic also introduced xhigh as a new effort level between high and max, and raised Claude Code's default to xhigh for all plans. Combined with the new tokenizer, output token counts can run higher than on 4.6 on later agentic turns; Task Budgets (now in public beta) let you cap what an agent spends in a session. GPT-5.4's steerability story centers on Interactive Thinking, as I covered in the coding section, and OpenAI's prompt guide notes the model performs well given explicit output contracts.

One note from Anthropic's own safety evaluation: Opus 4.7 improved on honesty and prompt injection resistance versus 4.6, but slightly regressed on resisting overly detailed harm-reduction advice on controlled substances. Anthropic's overall assessment: "largely well-aligned and trustworthy, though not fully ideal in its behavior."

Opus 4.7 vs. GPT-5.4 on Benchmark Tests

Benchmarks are worth looking at carefully, and worth trusting only up to a point. Both vendors chose the benchmarks that favor them, and Vals.ai and Artificial Analysis had not yet indexed Opus 4.7 at the time this was written. Test on your own tasks before drawing conclusions from any of these.

Coding benchmarks

The table below covers the most relevant coding evidence from each vendor's release materials.

Coding benchmarks favor Opus 4.7 clearly. Image by Author.

SWE-bench has several variants and both vendors highlighted the one where they perform best. Anthropic applied memorization screens and reports that Opus 4.7's margin holds after excluding flagged problems. Worth context: Z.ai's open-weight GLM-5.1 briefly led SWE-bench Pro at 58.4% in early April 2026 before Opus 4.7's 64.3% arrived, so any "state of the art" claim here has a short shelf life.

Agent and computer-use benchmarks

With Opus 4.7's release, Anthropic published comparison numbers for both models across most agentic benchmarks. The picture is mixed rather than one-sided.

The picture splits by environment: Opus 4.7 wins on desktop, tool use, and knowledge work; GPT-5.4 wins on browser research. Several GPT-5.4 numbers come from the Pro variant, so the standard tier may score lower. Independent runs on a shared scaffold are the next step.

Opus 4.7 vs. GPT-5.4 Pricing

The headline rates look simple. The actual cost picture is not.

API pricing structure

The pricing difference is easiest to understand through a few concrete scenarios.

At a 100K-token input and 10K-token output request (well under GPT-5.4's 272K threshold), GPT-5.4 costs roughly $0.40 versus Opus 4.7's $0.75. Close to half the price for short-to-medium context work.

At 500K input and 20K output, past GPT-5.4's threshold, the two models cost roughly the same: $2.95 versus $3.00. At 900K input and 10K output, they are almost identical.

The 272K repricing threshold is the part that catches people off guard: it applies to the entire session, not just the tokens above the cutoff. A pipeline that regularly sends 280K-token prompts pays the full long-context rate on every single request, not just the extra 8K. This is a session-level reprice, not a marginal surcharge.

GPT-5.4 costs rise past 272K tokens. Image by Author.

As I mentioned in the context window section, the new tokenizer can map the same input to up to 35% more tokens than on Opus 4.6. The per-token price is unchanged, but your actual cost per task can rise. Measure on real traffic; extrapolating from 4.6 baselines will give you a number that is too low.

Both platforms offer roughly a 90% discount on cached input tokens: $0.50 per million for Opus 4.7, $0.25 per million for GPT-5.4 under 272K. The Batch APIs add another roughly 50% off for non-urgent work. For asynchronous workloads, those discounts are the single largest lever on either platform.

There are also per-tool costs that tend to get missed. Anthropic charges $10 per 1,000 web searches, plus standard token costs for retrieved content. OpenAI charges for file search storage and queries separately. These add up in tool-heavy pipelines.

Cost for different workloads

For short-context, high-volume work (API calls under 100K tokens, batch classification, rapid iteration), GPT-5.4 is cheaper. The input cost gap can approach 2x.

Past 272K tokens, the advantage reverses. Opus 4.7's flat rate becomes easier to budget and nearly matches GPT-5.4 on total cost.

Both platforms charge a small data-residency premium (around 10% on either side). At that level, it's a compliance decision, not a pricing one. For agentic Claude Code sessions, Task Budgets (covered in the steerability section) are the main lever for token spend.

Is Claude Opus 4.7 Better Than GPT-5.4?

There is no universal answer, and any article that tells you there is one is selling something.

Choose Claude Opus 4.7 if your primary work is long-running software engineering where self-verification matters, your agent operates desktop applications, your prompts regularly exceed 272K tokens, your workflow reads dense screenshots or technical diagrams, or you are already on Claude Code, Cursor, Replit, or Devin.

Choose GPT-5.4 if your agent does heavy browser-based web research, your workloads stay under 272K tokens and cost matters, you want deferred tool loading on a large tool ecosystem, or your team is already on the OpenAI Responses API.

Consider testing both if your work splits between autonomous web research and long-form coding. GPT-5.4's browser and terminal strengths suit agentic web workflows; Opus 4.7's loop resistance and flat-rate pricing work better for deep engineering sessions and document-heavy pipelines.

Choosing the right model for your workflow. Image by Author.

One thing cuts across both choices: Batch API discounts can matter more than the model decision for asynchronous workloads. And since independent benchmarks for Opus 4.7 are still catching up, a pilot on a real slice of your own work is worth more than any comparison article, including this one.

Conclusion

The gap between Claude Opus 4.7 and GPT-5.4 is less about which model is smarter and more about what shape of work you are doing.

Anthropic bet on autonomy: a model built to hold coherence over long engineering runs and check its own output. OpenAI bet on breadth: a wider tool surface and cheaper rates for the majority of prompts that stay under 272K tokens.

Pricing is where most teams get caught off guard, and as I covered earlier, the pricing changes at 272K sessions is the specific trap. What actually moves monthly spend more than the base rate choice is usually caching and the Batch API discounts on either platform.

The benchmark gaps are single digits, and both vendors have been shipping new models every few weeks. Pick the one that fits your actual stack and revisit in a month.

If you want to go deeper on putting these models to work, our Software Development with Cursor course covers AI-assisted coding workflows in practice.