Claude Opus 4.7 vs GPT-5.4 (2026): Which Is the Most Powerful AI?
Our Verdict: GPT-5.4 Leads on Coding — Claude Opus 4.7 Leads on WritingThe Frontier Model Face-Off of April 2026
On April 16, 2026, Anthropic released Claude Opus 4.7 — its most capable model to date, featuring a new xhigh effort reasoning mode and a significant upgrade to its vision capabilities at 3.75 megapixels. The release immediately reignited the debate between the two most powerful AI models currently available: Claude Opus 4.7 from Anthropic and GPT-5.4 from OpenAI.
Both models carry identical base pricing — $5 per million input tokens and $25 per million output tokens — making this a pure performance comparison without a cost trade-off. The differences come down to where each model excels: GPT-5.4 leads on Terminal-Bench 2.0 (75.1% vs 69.4%), the leading benchmark for agentic coding and terminal tasks. Claude Opus 4.7 leads on writing quality (47% vs 31% preference in blind human evaluations) and now offers the highest resolution image understanding available at any frontier lab.
This is the most closely contested comparison on AI Tool Duel. Neither model is universally superior — the right choice depends entirely on your primary use case.
Quick Comparison: Claude Opus 4.7 vs GPT-5.4
| Feature | Claude Opus 4.7 | GPT-5.4 |
|---|---|---|
| Pricing | $5 per million input tokens · $25 per million output tokens | $5 per million input tokens · $25 per million output tokens |
| Free Tier | No – API or Claude Max plan required | No – API or ChatGPT Plus/Pro required |
| Speed | Moderate (slower with xhigh effort); fast at standard effort | Fast across all task types |
| Best For | Complex reasoning, writing quality, vision tasks, deep analysis | Coding, agentic tasks, terminal bench, broad versatility |
| Rating | ★★★★★4.8/5 | ★★★★★4.7/5 |
Pros & Cons
Claude Opus 4.7
Pros
- ✓xhigh effort mode unlocks deepest reasoning for hardest problems
- ✓3.75MP vision upgrade — highest resolution image understanding available
- ✓47% preference rate on writing quality in blind human evaluations
- ✓200k token context window for large document analysis
- ✓Exceptional performance on graduate-level reasoning tasks (GPQA)
- ✓More nuanced, safer, and more transparent than competitors
- ✓Claude Code integration for agentic software development
Cons
- ✗xhigh effort mode is slow and significantly more expensive per query
- ✗No built-in image generation (text/vision analysis only)
- ✗Smaller plugin/integration ecosystem than ChatGPT
- ✗Occasionally over-cautious on edge case content requests
- ✗No voice mode comparable to ChatGPT's Advanced Voice
GPT-5.4
Pros
- ✓75.1% on Terminal-Bench 2.0 — leading agentic coding benchmark
- ✓Strong performance across all major reasoning benchmarks
- ✓Fastest inference at frontier quality
- ✓Deep integration with OpenAI ecosystem (Codex, DALL-E, voice)
- ✓Best plugin and third-party integration ecosystem
- ✓Advanced computer use and agentic task execution
- ✓Most widely deployed frontier model — mature, reliable infrastructure
Cons
- ✗69.4% vs 47% preference on writing quality — trails Claude Opus 4.7
- ✗No xhigh effort mode equivalent for extra-deep reasoning
- ✗3.75MP vision not matched — lower resolution image analysis
- ✗Closed model with no open-source alternative
- ✗Pro plan ($200/month) required for unlimited frontier access
The xhigh Effort Mode: What It Does and When to Use It
Claude Opus 4.7 introduced a new reasoning intensity level called xhigh effort — a step beyond the existing high effort mode. When activated, xhigh effort instructs Claude to spend significantly more compute on a single query, exploring more reasoning paths, checking its work more thoroughly, and producing more carefully calibrated responses. The result is a meaningful improvement on the hardest problems — complex graduate-level reasoning, multi-step mathematical proofs, and difficult coding problems that require careful planning.
The trade-off is latency and cost. xhigh effort responses can take 30–60 seconds for complex queries versus the near-instant responses of standard mode, and they consume substantially more tokens, increasing API costs. Anthropic designed xhigh effort for situations where getting the right answer is worth waiting for and paying more — medical research synthesis, legal analysis, complex financial modeling, and cutting-edge research assistance.
GPT-5.4 doesn't offer a direct equivalent to xhigh effort, relying instead on its o3-class reasoning architecture that applies extended chain-of-thought processing based on problem difficulty detection. OpenAI's approach is more automatic — the model judges how much reasoning a problem requires and allocates compute accordingly, without a user-selectable intensity mode. Both approaches achieve similar ends through different mechanisms.
Vision Capabilities: 3.75MP vs Standard Vision
Claude Opus 4.7's 3.75MP vision upgrade is a significant leap from its predecessor and represents the highest resolution image understanding available from any frontier AI model as of April 2026. At 3.75 megapixels, Claude Opus 4.7 can analyze fine details in diagrams, read small text in documents, interpret complex charts and technical schematics, and provide detailed analysis of high-resolution photographs with accuracy that lower-resolution vision models miss.
Practical applications for the upgraded vision include: analyzing detailed engineering diagrams and CAD drawings, reading handwritten notes and low-contrast text in photos, interpreting complex multi-panel scientific figures from research papers, and providing detailed analysis of high-resolution medical imaging (with appropriate professional supervision). For professionals who regularly need AI to analyze visual materials with fine detail, the 3.75MP upgrade meaningfully expands what's possible.
GPT-5.4 also features strong vision capabilities and integration with DALL-E for image generation (which Claude lacks natively). GPT-5.4's vision analysis is excellent for standard use cases — reading documents, analyzing charts, describing images — but the resolution ceiling is lower than Claude Opus 4.7's new standard, which matters for the specific use cases involving very fine visual detail.
Benchmark Deep Dive: Coding vs Writing vs Reasoning
Terminal-Bench 2.0 is the leading evaluation for agentic coding and terminal-based task execution — tasks that require planning multi-step sequences, executing commands, debugging errors, and navigating codebases autonomously. GPT-5.4's 75.1% score versus Claude Opus 4.7's 69.4% represents a meaningful gap on these agentic coding tasks. For developers using AI to autonomously execute complex development workflows, GPT-5.4 is the stronger choice based on this benchmark.
On writing quality, blind human evaluation studies consistently show a preference for Claude Opus 4.7's outputs at 47% versus GPT-5.4 at approximately 31% (with the remainder going to other models or tie scores). Claude's prose is described by evaluators as more natural, more nuanced, better at matching requested tone and voice, and less prone to the slightly formulaic quality that AI-generated writing sometimes exhibits. For content creators, marketers, and writers using AI assistance, this 47% preference rate is a significant edge.
On general reasoning benchmarks (MMLU, GPQA, MATH), both models score within a few percentage points of each other, with Claude Opus 4.7's xhigh effort mode providing a meaningful advantage on graduate-level GPQA questions specifically. For most professional use cases, the practical difference in reasoning quality is small — both are genuinely frontier models capable of handling complex, nuanced tasks with high reliability.
Pricing and Access: Identical Cost, Different Ecosystem
Both Claude Opus 4.7 and GPT-5.4 carry the same base API pricing: $5 per million input tokens and $25 per million output tokens. This pricing equivalence makes the choice a pure capability decision rather than a cost trade-off — you're paying the same amount for each and getting different strengths depending on your use case. Note that xhigh effort mode in Claude Opus 4.7 consumes additional tokens compared to standard mode, effectively increasing cost per query for complex problems.
Access differs between the two. Claude Opus 4.7 is available through the Anthropic API and included in the Claude Max plan, which offers a monthly compute budget rather than per-token billing. GPT-5.4 is available through the OpenAI API and requires ChatGPT Pro ($200/month) for unlimited access in the ChatGPT interface, or standard Plus ($20/month) with usage caps.
The ecosystem difference matters for some users. GPT-5.4 connects to the massive OpenAI ecosystem including Codex, DALL-E 3, the GPT Store, and thousands of third-party integrations. Claude Opus 4.7 connects to Claude Code for agentic development, Anthropic's Constitutional AI safety systems, and a growing but smaller integration ecosystem. For developers building applications, OpenAI's more mature API ecosystem and wider adoption may influence the choice regardless of raw model performance.
Which Should You Pick?
Choose Claude Opus 4.7 if you...
- →Prioritize writing quality — essays, reports, creative and marketing content
- →Need maximum vision resolution for analyzing detailed diagrams or documents
- →Work on the hardest reasoning problems where xhigh effort mode pays off
- →Do long-document analysis requiring the 200k context window
- →Prefer Anthropic's safety-focused, transparent approach to AI development
Choose GPT-5.4 if you...
- →Do agentic coding and terminal tasks where Terminal-Bench scores matter
- →Need image generation (DALL-E) alongside analysis in the same platform
- →Want the broadest plugin ecosystem and third-party integrations
- →Build applications using the most mature and widely adopted AI API
- →Need fast, reliable inference at frontier quality across all task types
Bottom Line
Claude Opus 4.7 and GPT-5.4 are the two most capable AI models available in April 2026, and the choice between them is genuinely use-case dependent. Writers, researchers, and anyone doing deep analytical work should favor Claude Opus 4.7 — the writing quality advantage and xhigh effort reasoning mode are meaningful. Developers, agentic AI builders, and teams embedded in the OpenAI ecosystem should favor GPT-5.4. Many power users access both through their respective APIs and route tasks based on the model's demonstrated strengths.
Frequently Asked Questions
What is Claude Opus 4.7's xhigh effort mode?
xhigh effort is a reasoning intensity setting launched with Claude Opus 4.7 that instructs the model to spend significantly more compute on a single query — exploring more reasoning paths, checking its work more carefully, and producing more thoroughly validated responses. It's designed for the hardest problems where accuracy matters more than speed: complex research synthesis, graduate-level reasoning, advanced mathematics, and detailed code analysis. xhigh effort responses are slower (30–60 seconds for complex queries) and consume more tokens, so it's best reserved for high-stakes queries that genuinely need the extra depth.
How significant is GPT-5.4's Terminal-Bench 2.0 lead over Claude?
GPT-5.4 scored 75.1% on Terminal-Bench 2.0 versus Claude Opus 4.7's 69.4% — a roughly 6 percentage point gap. Terminal-Bench 2.0 evaluates agentic coding tasks in terminal environments: planning multi-step execution, writing and debugging code autonomously, and navigating complex development workflows. For developers using AI to autonomously complete coding tasks, this lead is meaningful. For non-coding tasks, writing quality, and long-context analysis, the benchmark positions reverse.
Why does Claude Opus 4.7 prefer to use 3.75MP vision?
Claude Opus 4.7's 3.75MP vision upgrade allows it to process images at significantly higher resolution than previous Claude versions and most competing models. Higher resolution means it can read small text in photos, analyze fine details in technical diagrams, interpret complex scientific figures with multiple data panels, and provide more accurate analysis of high-resolution images generally. For use cases involving detailed visual analysis — engineering diagrams, research paper figures, medical images (with professional supervision), or handwritten documents — the resolution upgrade produces noticeably better results.