7 AI Coding Models Tested Using the Same Prompt : Winners, Losers and Surprises

Geeky Gadgets

28-05-2025

What if a single prompt could reveal the true capabilities of today's leading coding language models (LLMs)? Imagine asking seven advanced AI systems to tackle the same complex task—building a functional web app that synthesizes real-time data into a structured dashboard—and comparing their performance side by side. The results might surprise you. From unexpected strengths to glaring weaknesses, these models don't just code; they reveal how far AI has come and where it still stumbles. With costs ranging from $15 to $75 per million tokens, the stakes are high for developers choosing the right tool for their workflows. So, which models shine, and which falter under pressure?
In the video below Prompt Engineering show how seven prominent LLMs—like Opus 4, Gemini 2.5 Pro, and Sonnet 3.7—stacked up when tested with identical prompts. You'll discover which models excelled at handling multi-step processes and which struggled with accuracy and hallucination issues. Whether you're a developer seeking cost-efficient solutions or a technical lead evaluating tools for complex projects, these findings offer actionable insights to help you make informed decisions. By the end, you might rethink how you approach AI-driven coding and whether a single model can truly meet all your needs—or if the future lies in combining their strengths. Comparing Coding LLM Performance Tested Models and Evaluation Criteria
The study examined the performance of seven models: Sonnet 4, Sonnet 3.7, Opus 4, Gemini 2.5 Pro, Quinn 2.5 Max, DeepSeek R1, and O3. Each model was tasked with creating a functional web app while demonstrating effective tool usage and avoiding hallucinated outputs. Gro 3 was excluded from the evaluation due to incompatibility with the prompt.
The evaluation focused on four critical areas to gauge the models' effectiveness: Information Synthesis: The ability to gather and integrate data from web searches.
The ability to gather and integrate data from web searches. Dashboard Accuracy: The precision in rendering structured dashboards.
The precision in rendering structured dashboards. Sequential Tool Usage: Effectiveness in managing multi-step processes.
Effectiveness in managing multi-step processes. Error Minimization: Reducing inaccuracies, such as hallucinated data or incorrect outputs. Performance Insights
The models demonstrated varying levels of success, with some excelling in specific areas while others faced significant challenges. Below is a detailed analysis of each model's performance: Opus 4: This model excelled in handling multi-step processes and agentic tasks, making it highly effective for complex workflows. However, its slower execution speed and high token cost of $75 per million tokens were notable drawbacks.
This model excelled in handling multi-step processes and agentic tasks, making it highly effective for complex workflows. However, its slower execution speed and high token cost of $75 per million tokens were notable drawbacks. Sonnet Models: Sonnet 3.7 outperformed Sonnet 4 in accuracy and tool usage, making it a more reliable choice for precision tasks. Sonnet 4, while less consistent, offered a budget-friendly alternative at $15 per million tokens.
Sonnet 3.7 outperformed Sonnet 4 in accuracy and tool usage, making it a more reliable choice for precision tasks. Sonnet 4, while less consistent, offered a budget-friendly alternative at $15 per million tokens. Gemini 2.5 Pro: The most cost-efficient model at $15 per million tokens, with additional discounts for lower usage. It handled simpler tasks effectively but struggled with sequential tool usage and complex data synthesis.
The most cost-efficient model at $15 per million tokens, with additional discounts for lower usage. It handled simpler tasks effectively but struggled with sequential tool usage and complex data synthesis. O3: This model performed well in sequential tool calls but was inconsistent in synthesizing and structuring information. Its token cost of $40 per million tokens provided a balance between affordability and performance.
This model performed well in sequential tool calls but was inconsistent in synthesizing and structuring information. Its token cost of $40 per million tokens provided a balance between affordability and performance. Quinn 2.5 Max: Accuracy issues, particularly with benchmarks and release date information, limited its reliability for tasks requiring precision.
Accuracy issues, particularly with benchmarks and release date information, limited its reliability for tasks requiring precision. DeepSeek R1: This model underperformed in rendering dashboards and maintaining accuracy, making it less suitable for tasks requiring visual outputs or structured data. Comparing 7 AI Coding Models: Which One Builds the Best Web App?
Watch this video on YouTube.
Dive deeper into coding language models (LLMs) with other articles and guides we have written below. Key Observations
Several patterns emerged during the evaluation, shedding light on the strengths and weaknesses of the tested models. These observations can guide developers in selecting the most suitable model for their specific needs: Sequential Tool Usage: Models like Opus 4 demonstrated exceptional capabilities in managing multi-step tasks, a critical feature for complex workflows.
Models like Opus 4 demonstrated exceptional capabilities in managing multi-step tasks, a critical feature for complex workflows. Hallucination Issues: Incorrect data generation, such as inaccurate release dates or benchmark scores, was a recurring problem, particularly for Quinn 2.5 Max and DeepSeek R1.
Incorrect data generation, such as inaccurate release dates or benchmark scores, was a recurring problem, particularly for Quinn 2.5 Max and DeepSeek R1. Dashboard Rendering: While most models successfully rendered dashboards, DeepSeek R1 struggled significantly in this area, highlighting its limitations for tasks requiring visual outputs.
While most models successfully rendered dashboards, DeepSeek R1 struggled significantly in this area, highlighting its limitations for tasks requiring visual outputs. Cost Variability: Token costs varied widely, with Gemini 2.5 Pro emerging as the most affordable option for simpler tasks, while Opus 4's high cost limited its accessibility despite its strong performance. Cost Analysis
The cost of using these models played a pivotal role in determining their overall value. Below is a breakdown of token costs for each model, providing a clearer picture of their affordability: Opus 4: $75 per million tokens, the highest among the models tested, reflecting its advanced capabilities but limiting its cost-efficiency.
$75 per million tokens, the highest among the models tested, reflecting its advanced capabilities but limiting its cost-efficiency. Sonnet 4: $15 per million tokens, offering a low-cost alternative with moderate performance for budget-conscious users.
$15 per million tokens, offering a low-cost alternative with moderate performance for budget-conscious users. Gemini 2.5 Pro: The most cost-efficient model, priced at $15 per million tokens, with discounts available for lower usage, making it ideal for simpler tasks.
The most cost-efficient model, priced at $15 per million tokens, with discounts available for lower usage, making it ideal for simpler tasks. O3: $40 per million tokens, providing a middle ground between cost and performance, suitable for tasks requiring balanced capabilities. Strategic Model Selection
The evaluation revealed that no single model emerged as the definitive leader across all tasks. Instead, the findings emphasized the importance of selecting models based on specific project requirements. For example: Complex Tasks: Opus 4 proved to be the most capable for multi-agent tasks requiring sequential tool usage, despite its higher cost.
Opus 4 proved to be the most capable for multi-agent tasks requiring sequential tool usage, despite its higher cost. Cost-Efficiency: Gemini 2.5 Pro offered the best value for simpler tasks with limited tool usage, making it a practical choice for budget-conscious projects.
Gemini 2.5 Pro offered the best value for simpler tasks with limited tool usage, making it a practical choice for budget-conscious projects. Budget-Friendly Options: Sonnet 3.7 outperformed Sonnet 4 in accuracy, but both models remained viable for users prioritizing affordability.
For highly complex projects, combining models may yield better results by using their individual strengths while mitigating weaknesses. Regardless of the model chosen, verifying outputs remains essential to ensure accuracy and reliability in your projects. This approach allows developers to maximize efficiency and achieve optimal results tailored to their unique requirements.
Media Credit: Prompt Engineering Filed Under: AI, Guides
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