Claude Opus 4.7 vs GPT-5.4 for Coding: Which Model Should Developers Use in April 2026?

Claude Opus 4.7 vs GPT-5.4 for Coding: Which Model Should Developers Use in April 2026?

Last Updated: April 17, 2026 | Reading Time: 11 minutes | Trend Alert: Opus 4.7 just dropped and the developer community is split.

Two days ago, Anthropic released Claude Opus 4.7. A week before that, developers were still debating whether GPT-5.4 was the best coding model. Now there is a genuine question: which of these two frontier models should you trust with your code?

This is not a benchmark dump. Both companies publish their own benchmark numbers and both claim to lead. What matters to developers is how these models perform on the work you actually do: writing features, debugging production issues, refactoring legacy code, and building projects from scratch.

Here is what the data shows, what early testers report, and where each model genuinely wins.

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The Core Difference

Before getting into specifics, understand that these two models were built with different priorities.

GPT-5.4 is OpenAI's unified frontier model. It merged the GPT and Codex product lines into one system. It has a 1.05M token context window, strong multimodal support, and a Computer Use API that lets it interact with desktop applications autonomously. OpenAI optimized for general-purpose reasoning across every domain.

Opus 4.7 is Anthropic's agentic coding specialist. This release was specifically tuned for long-running autonomous software engineering tasks. The architecture update includes a new tokenizer, adaptive thinking as the only reasoning mode, and a new "xhigh" effort level designed for the hardest coding work. Anthropic optimized for the specific use case of "give the model a task and let it run."

That philosophical difference shows up everywhere.

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Benchmark Numbers: What They Actually Mean

SWE-bench Verified

SWE-bench Verified is the standard benchmark for real-world coding. It tests whether a model can resolve actual GitHub issues from popular open-source repositories.

Anthropic reports that Opus 4.7 achieves state-of-the-art results on SWE-bench Verified, SWE-bench Pro, and SWE-bench Multilingual. They did not publish exact percentages in the announcement, but third-party testers have filled in the picture.

OpenAI published SWE-bench numbers for GPT-5.4 ranging from 71.7% to 74.9% depending on the configuration. The higher number requires their most expensive inference tier.

The takeaway: Opus 4.7 appears to lead on the hardest software engineering benchmarks. The margin is not disclosed precisely enough to call it a blowout, but the consensus from Cursor, Replit, Rakuten, and other early testers is that the gap is real and meaningful.

CursorBench: The Developer Reality Check

CursorBench is run by the Cursor team and reflects real-world coding performance inside their IDE. This is closer to what developers experience than synthetic benchmarks.

That is a 12-point jump in a single model generation. Cursor has not published GPT-5.4 numbers on CursorBench, which makes direct comparison difficult. But the Opus 4.7 improvement is large enough that it changed how Cursor positions the model internally.

Rakuten-SWE-Bench: Production Tasks

Rakuten runs their own internal SWE-bench variant using actual production engineering tasks from their systems. Opus 4.7 resolved 3x more tasks than Opus 4.6. No comparable GPT-5.4 data exists for this benchmark, so it cannot be used for direct comparison. But the magnitude of improvement (3x from one generation to the next) is unusual.

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Pricing: The Real Cost of Coding

Developers care about benchmarks, but managers care about the invoice. Here is the math.

At base pricing, GPT-5.4 is roughly half the cost of Opus 4.7 per token. That looks like a clear win for OpenAI until you factor in two things.

First, Opus 4.7 has effort levels. Hex's co-founder reported that "low-effort Opus 4.7 is roughly equivalent to medium-effort Opus 4.6." If low-effort Opus 4.7 can match medium-effort GPT-5.4 for your tasks, the effective cost narrows significantly because the model finishes faster with fewer tokens.

Second, Opus 4.7 has a new tokenizer that uses 1.0x to 1.35x more tokens for the same input text. On text-heavy workloads, your bill could increase even at the same per-token price. This offsets some of the efficiency gain from the effort levels.

Net pricing assessment: GPT-5.4 is cheaper per token. Opus 4.7 may be cheaper per task if you tune the effort level correctly. You need to test on your actual workload to know which is more economical.

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Agent Quality: Who Ships Better Code?

This is the section that matters most. Both models power autonomous coding agents: Opus 4.7 runs Claude Code, and GPT-5.4 powers Codex and Copilot.

Autonomous Task Execution

Opus 4.7 was specifically designed for long-running agentic loops. The improvements include:

• •Fewer tool call errors. Notion reported 1/3 fewer tool errors compared to Opus 4.6.
• •More regular progress updates during long traces, reducing the need for scaffolding.
• •Better self-verification on code edits, catching its own mistakes before finishing.
• •Task budgets (beta) that let you cap token spend on an entire agentic loop.

GPT-5.4's agentic strengths are different:

• •Computer Use API enables interaction with any desktop application, not just terminals and IDEs.
• •Tighter integration with GitHub (issues, PRs, Actions) through Copilot.
• •Multiple model tiers (mini, nano) let you route simple tasks to cheaper variants.

What Early Testers Say

The early-access data from companies that tested both models paints a consistent picture:

Opus 4.7 wins on: Complex, multi-step coding tasks where the agent needs to read a codebase, plan changes across multiple files, execute, debug errors, and iterate. Factory reported a 10-15% lift in autonomous task success. Vercel noted "more correct and complete one-shot coding."

GPT-5.4 wins on: Speed of iteration, cost efficiency for straightforward tasks, and breadth of ecosystem integration. If you need an agent that works inside GitHub, responds to issues, and handles routine development work, GPT-5.4's tooling is more mature.

Neither wins on: Hallucination resistance. If factual accuracy in generated code is your primary concern, Grok 4.20 still leads with its 78% AA Omniscience score, though that is not a coding-specific benchmark.

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Vision and Multimodal: Edge Cases That Matter

For most coding tasks, multimodal capabilities are secondary. But they matter for specific workflows:

• •Reading UI screenshots to reproduce bugs or implement designs
• •Parsing error screenshots from teammates who send images instead of text
• •Analyzing architecture diagrams and generating code from them

Opus 4.7 jumped from 1.15MP to 3.75MP image resolution. XBOW's visual acuity benchmark went from 54.5% (Opus 4.6) to 98.5% (Opus 4.7). That is a massive improvement for any workflow involving screenshots.

GPT-5.4 has solid multimodal support across text, images, audio, and video. It does not have the same pixel-level resolution gains that Opus 4.7 claims.

If your coding workflow involves screenshots, diagrams, or visual debugging, Opus 4.7 has a meaningful edge right now.

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Developer Experience: APIs and Tooling

Opus 4.7 API Changes

If you are upgrading from Opus 4.6, there are breaking changes:

• •temperature, top_p, and top_k parameters are gone. Any non-default value returns a 400 error.
• •Extended thinking budgets (budget_tokens) are removed. Only adaptive thinking remains.
• •Thinking content is hidden by default. You need to opt in with "display": "summarized".
• •The new tokenizer uses more tokens for the same text.

These changes make the API simpler but require migration work. If you are starting fresh with Opus 4.7, you will not notice. If you are upgrading an existing integration, budget time for the migration.

GPT-5.4 API

GPT-5.4's API is stable and well-documented. No breaking changes from GPT-5.3. The Computer Use API is the major addition, and it works through the standard chat completions interface.

The OpenAI SDK ecosystem is larger, with more community libraries, examples, and third-party integrations. If you are building tooling around the API, GPT-5.4 has more off-the-shelf support.

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Effort Levels vs Model Tiers

This is an interesting architectural difference.

Opus 4.7 uses effort levels (low, medium, high, xhigh, max) on a single model. You pay the same per-token price regardless of effort. The tradeoff is tokens consumed: higher effort uses more thinking tokens but produces better results.

GPT-5.4 uses model tiers (nano, mini, standard, Pro/xhigh). Each tier has different pricing and capabilities. You explicitly choose which tier to call.

The Opus approach is simpler to manage: one model ID, one set of prompts, just adjust the effort parameter. The GPT-5.4 approach gives you more explicit cost control: route easy tasks to nano at $0.20/$1.25 per million tokens and reserve the full model for complex work.

Both approaches work. The right choice depends on whether you prefer a single model with adjustable effort or separate models with distinct price points.

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The Recommendation Matrix

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What About Claude Code vs Codex?

The model comparison maps onto the tool comparison, but they are not identical.

Claude Code runs on Opus 4.7 and is a terminal-based agent. It reads your entire repo, plans changes, executes them, reads error output, and iterates. It works in any language, any framework, any environment.

Codex runs on GPT-5.4 and lives inside OpenAI's platform. It handles tasks asynchronously: you submit a task, it runs in a sandbox, and you get back the result. It integrates with GitHub for issue-to-PR workflows.

Both ship with their respective pro subscriptions ($20/mo for Claude Pro, similar tiers for OpenAI). The difference is workflow preference: do you want an agent in your terminal (Claude Code) or an agent in your browser (Codex)?

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The Bottom Line

Opus 4.7 is the better model for pure coding ability right now. The SWE-bench data, CursorBench results, and early tester reports from Cursor, Notion, Vercel, Factory, and Rakuten all point in the same direction. If you want the model most likely to solve your hardest coding problem correctly on the first attempt, Opus 4.7 has the edge.

GPT-5.4 is the better ecosystem play. It is cheaper, has more tooling, integrates more deeply with GitHub, and gives you multiple price-performance tiers. If you are building a development platform or managing costs at scale, GPT-5.4's flexibility wins.

The honest answer for most developers is to use both. Route complex autonomous tasks to Opus 4.7 through Claude Code, and use GPT-5.4 (or its mini/nano variants) for inline suggestions, quick generations, and routine work. The best setup in April 2026 is not one model. It is the right model for each task.

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Further Reading

• •Claude Opus 4.7: Benchmarks, Pricing, and Migration Guide - Our detailed breakdown of what changed from Opus 4.6
• •GPT-5.4 vs Gemini 3.1 Ultra vs Grok 4.20: Frontier Model Showdown - Where GPT-5.4 stands against other frontier models
• •AI Coding Agents in 2026: Claude Code vs Cursor vs Copilot vs Windsurf - Tool-level comparison
• •Anthropic's Official Opus 4.7 Announcement
• •OpenAI GPT-5.4 Documentation