Claude Opus 4.7 vs GPT-5.4: The Definitive 2026 Flagship Model API Comparison

Note: Data from BenchLM (benchlm.ai), Eden AI (edenai.co), Evolink.AI, ModelsLab, GlbGPT, and official Anthropic/OpenAI release pages. Integration guidance is engineering analysis based on public information.

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1. Benchmark overview: Is there a clear #1?

From BenchLM’s provisional leaderboard (April 2026):

Metric	Claude Opus 4.7	GPT-5.4
BenchLM overall rank	#2 (94 pts)	#4 (93 pts)
Coding average	72.9	57.7
MCP Atlas (agentic core)	77.3%	67.2%
Knowledge tasks	68.2	slight edge
Agentic average	74.9	42.9
Visual acuity (XBOW)	98.5%	not disclosed

The gap is real but not dominant: Opus 4.7 wins on hard tasks and reliability; GPT-5.4 wins on cost and knowledge workloads. BenchLM’s own verdict: “Opus 4.7 finishes one point ahead — not a blowout, but enough to call.”

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2. Pricing: 2–6× cost difference

Dimension	Claude Opus 4.7	GPT-5.4 (standard)	GPT-5.4 Pro
Input	$5 / 1M tokens	$2.50 / 1M	$30 / 1M
Output	$25 / 1M tokens	$15 / 1M	$75 / 1M
Cached input	—	$0.625 / 1M	—
Context window	1M tokens	1.05M tokens	1.05M
Max output	undisclosed	128K tokens	128K

GPT-5.4 is roughly half the cost of Opus 4.7 on standard tier. Its cached input pricing ($0.625/M) is a significant advantage for multi-turn agent conversations. GPT-5.4 Pro at $30/M input is 6× Opus 4.7 — premium tier reserved for the most demanding workloads.

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3. Context window and token efficiency

GPT-5.4 offers 1.05M tokens context vs Opus 4.7’s 1M. GPT-5.4 also supports up to 128K max output — useful for long-form report generation. However, Opus 4.7’s tokenizer inflation (1.0–1.35×) and heavier thinking at high effort means real-world token spend in long-horizon tasks is ~1.2–1.5× higher. Use effort parameters and task budgets to control Opus 4.7 spend.

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4. Coding: Opus 4.7 dominates hard tasks

Benchmark	Claude Opus 4.7	GPT-5.4
SWE-bench (Rakuten)	3× Opus 4.6	not disclosed
Terminal Bench 2.0	96% (vs 54.5% on Opus 4.6)	not disclosed
CursorBench	70% (vs 58% Opus 4.6)	not disclosed
XBOW visual acuity	98.5%	not disclosed

The most striking demo: Opus 4.7 autonomously built a complete Rust text-to-speech engine — neural model, SIMD kernels, browser demo — then verified its own output. No equivalent GPT-5.4 public demo exists. GPT-5.4’s advantage in coding is its native spreadsheet plugins (Excel, Google Sheets) and strong document formatting output.

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5. Agent workflows and tool use

MCP Atlas (core agentic benchmark): Opus 4.7 77.3% vs GPT-5.4 67.2% — a 10-point gap.

Dimension	Claude Opus 4.7	GPT-5.4
Tool calling style	Anthropic native Tool Use	OpenAI Function Calling
MCP protocol	supported (via claude-code)	supported
Native spreadsheet plugins	no	Excel + Google Sheets
Computer Use accuracy	improving (54.5%→98.5%)	75%
Fine-grained effort control	xhigh tier	no equivalent

GPT-5.4’s native Computer Use at 75% accuracy is a genuine differentiator for desktop automation and GUI operation. Opus 4.7’s xhigh effort provides superior reasoning quality control for the most difficult multi-step tasks.

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6. Safety and compliance

Opus 4.7 deploys under Project Glasswing — automatic detection and blocking of high-risk cybersecurity requests, with a Cyber Verification Program whitelist for legitimate security researchers. GPT-5.4 has no comparable public safety architecture. For security research, pentesting, and red-teaming use cases, Opus 4.7 offers a clearer compliance path.

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7. NixAPI routing strategy

export async function routeTask(task: Task) {
  // Hard SWE → Opus 4.7
  if (task.type === 'swe' && task.difficulty === 'hard') {
    return opus47.chat(task.messages, { effort: 'xhigh' });
  }
  // Desktop automation / Computer Use → GPT-5.4
  if (task.type === 'computer-use') {
    return gpt54.chat(task.messages, { reasoning: { effort: 'high' } });
  }
  // Long-context knowledge Q&A → GPT-5.4 (cost advantage)
  if (task.type === 'knowledge' && task.longContext) {
    return gpt54.chat(task.messages);
  }
  // General agentic workflows → Opus 4.7
  if (task.type === 'agentic') {
    return opus47.chat(task.messages, { effort: 'high' });
  }
  // Simple tasks → Sonnet 4.6 or MiniMax M2.7
  return sonnet46.chat(task.messages);
}

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8. Summary: The workload determines the answer

Use case	Recommended	Why
Hard SWE / complex coding	Claude Opus 4.7	72.9 coding, 3× SWE-bench improvement
High-frequency agentic workflows	Claude Opus 4.7	MCP Atlas 77.3%, highest reliability
Desktop automation / Computer Use	GPT-5.4	75% native accuracy
Long-context knowledge tasks	GPT-5.4	1.05M context, 128K output, cached input
Cost-sensitive general use	GPT-5.4	1/2 Opus 4.7 price
Security research / pentesting	Claude Opus 4.7	Project Glasswing compliance
Vision-intensive tasks	Claude Opus 4.7	98.5% XBOW, 2,576px resolution

The core insight: Opus 4.7 is the reliability choice for hard tasks; GPT-5.4 is the flexibility choice for cost-sensitive general work. NixAPI’s multi-model routing architecture is designed exactly for this — let workloads auto-route to the right model.