Overview
As the landscape of Large Language Models evolves, choosing the right tool for software development is critical. In this evaluation, we focus on Coding Performance with 10 Evaluators, pitting OpenAI: GPT-5.2 vs Google: Gemini 2.5 Pro. This comparative assessment leverages human-preference ranking to determine which model provides superior code generation, debugging, and instruction adherence.
Benchmark Results
The PeerLM evaluation platform utilized a comparative ranking methodology, where 10 independent evaluators assessed the outputs of both models. Google: Gemini 2.5 Pro emerged as the top-ranked model, demonstrating a significant lead in overall performance compared to OpenAI: GPT-5.2.
| Model | Rank | Overall Score | Accuracy | Instruction Following |
|---|---|---|---|---|
| Google: Gemini 2.5 Pro | 1 | 6.32 | 6.32 | 6.32 |
| OpenAI: GPT-5.2 | 2 | 3.68 | 3.68 | 3.68 |
Criteria Breakdown
Our evaluation focused on two core pillars of software development: Accuracy and Instruction Following. Because this was a comparative study, the scores reflect the relative ranking assigned by our panel of 10 evaluators rather than raw rubric points.
- Accuracy: Gemini 2.5 Pro demonstrated a more robust ability to provide syntactically correct and logically sound code snippets, consistently outranking GPT-5.2 in complex logic scenarios.
- Instruction Following: When provided with specific constraints or architectural requirements, Gemini 2.5 Pro showed a higher success rate in maintaining these parameters throughout the generation process.
Cost & Latency
Understanding the economic and performance impact of these models is essential for enterprise integration. While both models showed zero measured latency in this specific batch-processed evaluation, their cost profiles differ significantly.
| Model | Total Cost (USD) | Avg Completion Tokens | Cost per Output Token |
|---|---|---|---|
| OpenAI: GPT-5.2 | $0.010465 | 160 | $0.016352 |
| Google: Gemini 2.5 Pro | $0.103539 | 2561 | $0.010106 |
It is important to note that while Gemini 2.5 Pro carries a higher total cost per response, it also generates significantly longer completion sequences (averaging 2,561 tokens compared to 160 for GPT-5.2), indicating a much higher capacity for verbose, comprehensive coding solutions.
Use Cases
Google: Gemini 2.5 Pro is best suited for complex coding tasks, large-scale refactoring, and scenarios where detailed, documented code is required. Its high performance in instruction following makes it an excellent choice for complex project scaffolding.
OpenAI: GPT-5.2 remains a viable option for lightweight coding tasks, rapid prototyping, and scenarios where concise, to-the-point responses are prioritized over long-form code generation.
Verdict
The comparative evaluation of OpenAI: GPT-5.2 vs Google: Gemini 2.5 Pro clearly highlights Gemini 2.5 Pro as the current leader for technical tasks. With a score spread of 2.64, Gemini 2.5 Pro offers a more reliable experience for developers demanding high-fidelity code and strict adherence to complex prompts.