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Claude Opus 5 vs OpenAI: GPT-5.6 Sol Pro: Coding Performance with 10 Evaluators

PeerLM's latest comparative analysis of Claude Opus 5 vs OpenAI: GPT-5.6 Sol Pro evaluates coding performance using 10 specialized evaluators.

Claude Opus 5

8.6

/ 10

vs

OpenAI: GPT-5.6 Sol Pro

1.4

/ 10

Key Findings

Coding AccuracyClaude Opus 5

Achieved a significantly higher accuracy score of 8.57 compared to 1.43.

Instruction FollowingClaude Opus 5

Demonstrated superior capability in adhering to complex coding constraints.

Cost EfficiencyClaude Opus 5

Delivered better performance at a lower total cost per evaluation run.

Specifications

SpecClaude Opus 5OpenAI: GPT-5.6 Sol Pro
Provideranthropicopenai
Context Length1.0M1.1M
Input Price (per 1M tokens)$5.00$2.50
Output Price (per 1M tokens)$25.00$15.00
Max Output Tokens128,000128,000
Tierfrontierfrontier

Our Verdict

Claude Opus 5 decisively outperforms OpenAI: GPT-5.6 Sol Pro in coding tasks, showing both higher accuracy and better instruction following. With lower latency and cost, it is the superior choice for developers. OpenAI: GPT-5.6 Sol Pro failed to maintain competitive parity in this specific 10-evaluator benchmark.

Overview

In the rapidly evolving landscape of Large Language Models, choosing the right architecture for software development tasks is critical. This report provides a detailed comparative analysis of Claude Opus 5 vs OpenAI: GPT-5.6 Sol Pro, specifically focusing on their Coding Performance with 10 Evaluators. By utilizing PeerLM’s comparative ranking methodology, we provide an objective look at how these models handle complex coding prompts and instruction following.

Benchmark Results

The evaluation focused on two primary pillars: Accuracy and Instruction Following. The results highlight a distinct performance gap between the two models when subjected to rigorous, multi-evaluator scrutiny.

ModelOverall ScoreAccuracyInstruction Following
Claude Opus 58.578.578.57
OpenAI: GPT-5.6 Sol Pro1.431.431.43

Criteria Breakdown

The comparative evaluation revealed that Claude Opus 5 holds a significant advantage in coding tasks. With an overall score of 8.57, it demonstrated a superior ability to adhere to complex coding requirements and maintain logical flow. In contrast, OpenAI: GPT-5.6 Sol Pro struggled to meet the specific requirements set by the 10 evaluators, resulting in a lower comparative ranking of 1.43 across both metrics.

Accuracy

Accuracy in coding contexts requires not only syntactical correctness but also logical robustness. Claude Opus 5 consistently outperformed the competition, providing code that was functionally accurate and aligned with the intent of the prompt.

Instruction Following

The ability to follow specific constraints—such as using particular libraries, adhering to style guides, or implementing specific design patterns—is where the models diverged most sharply. Claude Opus 5's high score reflects its reliability in production-grade coding environments.

Cost & Latency

Performance is only one part of the equation; efficiency determines the scalability of these models in a development workflow.

  • Claude Opus 5: Highly efficient with a total cost of $0.099755 over the evaluation run. It maintained an exceptionally low latency profile.
  • OpenAI: GPT-5.6 Sol Pro: Exhibited higher operational costs at $0.147555 and an average latency of 372ms, suggesting a more resource-intensive execution path.

Use Cases

Given the results of this Coding Performance with 10 Evaluators study, Claude Opus 5 is the recommended choice for developers requiring high-fidelity code generation and strict adherence to complex instructions. OpenAI: GPT-5.6 Sol Pro may require further optimization or prompt engineering adjustments before it can match the reliability demonstrated by its counterpart in this specific testing suite.

Verdict

Claude Opus 5 is the clear leader in this coding benchmark. It offers better accuracy, tighter instruction following, and a more favorable cost structure compared to OpenAI: GPT-5.6 Sol Pro.

Backed by real data

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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.