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OpenAI: GPT-5.5 vs Anthropic: Claude Opus 4.7: Coding Performance with 10 Evaluators

In our latest Coding Performance with 10 Evaluators benchmark, we compare OpenAI: GPT-5.5 vs Anthropic: Claude Opus 4.7 to determine the superior coding assistant.

OpenAI: GPT-5.5

3.7

/ 10

vs

Anthropic: Claude Opus 4.7

6.3

/ 10

Key Findings

Top PerformanceAnthropic: Claude Opus 4.7

Achieved the highest overall score of 6.32 in the coding evaluation.

Instruction FollowingAnthropic: Claude Opus 4.7

Consistently outperformed GPT-5.5 in adhering to complex coding constraints.

Cost EfficiencyAnthropic: Claude Opus 4.7

While total cost was higher, it offers better value per output token compared to GPT-5.5.

Specifications

SpecOpenAI: GPT-5.5Anthropic: Claude Opus 4.7
Provideropenaianthropic
Context Length1.1M1.0M
Input Price (per 1M tokens)$5.00$5.00
Output Price (per 1M tokens)$30.00$25.00
Max Output Tokens128,000128,000
Tierfrontierfrontier

Our Verdict

Anthropic: Claude Opus 4.7 emerges as the clear winner in this coding-focused benchmark, significantly outperforming OpenAI: GPT-5.5 in both accuracy and instruction following. While GPT-5.5 remains a capable model, the 2.64-point score gap demonstrates that Claude Opus 4.7 is currently more reliable for technical development tasks.

Overview

The landscape of Large Language Models is evolving rapidly, particularly in specialized technical domains. This PeerLM analysis focuses on OpenAI: GPT-5.5 vs Anthropic: Claude Opus 4.7, specifically evaluating their capabilities in a rigorous Coding Performance with 10 Evaluators suite. By utilizing comparative, ranking-based evaluation methods, we provide an objective look at how these state-of-the-art models handle complex programming tasks.

Benchmark Results

Using a panel of 10 expert evaluators, we tested both models across a series of coding prompts. The results highlight a clear performance gap in favor of Anthropic's latest iteration.

Model Overall Score Accuracy Instruction Following
Anthropic: Claude Opus 4.7 6.32 6.32 6.32
OpenAI: GPT-5.5 3.68 3.68 3.68

Criteria Breakdown

Our evaluation focused on two primary pillars: Accuracy and Instruction Following. In coding contexts, accuracy refers to the syntactical correctness and logic of the generated code, while instruction following measures the model's ability to adhere to specific constraints, such as library requirements or architectural patterns.

  • Anthropic: Claude Opus 4.7 demonstrated superior consistency across both criteria, earning an overall score of 6.32.
  • OpenAI: GPT-5.5 trailed with a score of 3.68, indicating greater difficulty with the specific nuances of the 10-evaluator coding test set.

Cost & Latency

Performance must be balanced against operational costs. Below is the cost breakdown for the tokens processed during this evaluation suite:

Model Total Cost (USD) Cost/Output Token
OpenAI: GPT-5.5 $0.03079 $0.03487
Anthropic: Claude Opus 4.7 $0.038385 $0.029941

Use Cases

Given the results of the Coding Performance with 10 Evaluators benchmark, Anthropic: Claude Opus 4.7 is currently the recommended choice for high-stakes programming tasks, such as complex refactoring or architectural design, where accuracy is paramount. While OpenAI: GPT-5.5 remains a viable tool, it may require more iterative prompting or human oversight in technical environments.

Verdict

The comparison of OpenAI: GPT-5.5 vs Anthropic: Claude Opus 4.7 reveals that Anthropic holds a significant edge in technical reasoning. With a score spread of 2.64, Claude Opus 4.7 is the definitive leader for coding tasks that demand strict adherence to complex instructions.

Backed by real data

View the Full Evaluation Report

See every response, score, and evaluator judgment behind this comparison. All data from PeerLM's blind evaluation pipeline.

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Methodology

Evaluated using PeerLM's blind evaluation pipeline with 4 responses per model across 2 criteria.