The Opus 4.7 vs GPT-5.5: Comparison beyond benchmark

Opus 4.7 shipped April 16. GPT-5.5 followed seven days later on April 23. Sticker price favors Opus on output: $25 per million tokens versus $30 for GPT-5.5. Input matches at $5.

On identical coding tasks, GPT-5.5 produces roughly 72% fewer output tokens than Opus 4.7. Opus narrates. It explains its reasoning, describes what it's about to do, documents as it works. Inside a chat window that reads as helpful. Inside an agent loop hitting hundreds of inference calls per task, every line of narration is a billable token.

Run the numbers on a support agent handling 500 tickets a day. GPT-5.5 averages 2,000 output tokens per ticket. Opus 4.7 averages 7,100. The monthly API delta lands around $5,100. At a billion tokens a day across an enterprise, the cheaper-per-token model becomes the more expensive deployment.

NVIDIA's engineers reported 25–50% better cost efficiency on agentic workflows running GPT-5.5-style architectures. Internalize that number before picking a model on output price.

Where each model wins

The benchmarks split along the lines each lab optimized for.

Terminal-Bench 2.0 (multi-step terminal work, compiling, configuring, running tools):

• GPT-5.5: 82.7%
• Opus 4.7: 69.4%

SWE-Bench Pro (resolving real GitHub issues end-to-end):

• Opus 4.7: 64.3%
• GPT-5.5: 58.6%

OSWorld-Verified (operating real computer environments):

• GPT-5.5: 78.7%
• Opus 4.7: 78.0%

GDPval (knowledge work across 44 occupations): GPT-5.5 hits 84.9%.

Long-context retrieval at 512K–1M tokens:

• GPT-5.5: 74%
• Opus 4.7: 32.2%

OpenAI tuned GPT-5.5 for autonomy: tool use, long horizons, retrieval over big context. Anthropic tuned Opus 4.7 for code precision and instruction coherence. Anthropic added a self-verification step where the model checks output for logical faults before returning it. Production teams using Opus reported double-digit drops in feedback cycles because the model caught issues before delivery.

Teams running GPT-5.5 in Codex saw the inverse pattern. The model stays on task longer without pausing for clarification or abandoning halfway. For multi-step engineering work, that persistence compounds across the loop.

Latency

• GPT-5.5: ~3 seconds to first token
• Opus 4.7: ~0.5 seconds

For interactive workflows where someone watches the cursor, the 2.5-second gap shows up in the feel of the tool. For background agents, total wall-clock dominates first-token latency, and GPT-5.5's token efficiency narrows the gap.

Both ship 1M token context windows, so window size stopped being the differentiator. Retrieval reliability inside the window took its place, and GPT-5.5 leads there.

How I'd pick

Pick GPT-5.5 for:

• autonomous agents running long horizons
• high-volume workloads where token spend hits margin
• long-context retrieval over codebases or document sets
• multi-tool orchestration

Pick Opus 4.7 for:

• production code patches where review overhead drives cost
• instruction-heavy work where self-verification cuts revision cycles
• reasoning across interconnected systems where coherence beats speed

The token-efficiency gap rewards GPT-5.5 hardest in the workloads where Opus looks cheap on paper. Teams that pilot at low volume, where Opus's narration tax barely registers, get burned when production traffic separates the projection from the bill.

Run both on a slice of your real traffic. Measure tokens per task, not tokens per million. The pricing page is the wrong place to make this call.