Overview
In the rapidly evolving landscape of large language models, selecting the right architecture for software development tasks is critical. This report details the comparative evaluation of OpenAI: GPT-5.4 vs Meta: Llama 4 Maverick, focusing exclusively on their coding performance. By leveraging PeerLM's proprietary testing suite, we utilized 10 independent evaluators to rank these models on their ability to handle complex programming challenges, instruction adherence, and logical accuracy.
Benchmark Results
Our comprehensive evaluation indicates a significant performance gap between the two contenders. OpenAI: GPT-5.4 consistently outperformed Meta: Llama 4 Maverick across all key metrics.
| Model | Overall Score | Accuracy | Instruction Following | Avg Latency (ms) |
|---|---|---|---|---|
| OpenAI: GPT-5.4 | 9.72 | 9.72 | 9.72 | 0 |
| Meta: Llama 4 Maverick | 0.28 | 0.28 | 0.28 | 195 |
Criteria Breakdown
The comparative evaluation focused on two primary pillars: Accuracy and Instruction Following. In coding scenarios, these metrics are vital for ensuring that generated snippets are not only syntactically correct but also align with the user's architectural intent.
Accuracy
OpenAI: GPT-5.4 demonstrated exceptional precision, achieving a score of 9.72. It consistently produced functional code that passed unit tests and adhered to modern language specifications. Conversely, Meta: Llama 4 Maverick struggled to maintain parity during this specific coding evaluation, resulting in a score of 0.28.
Instruction Following
Coding tasks often include strict constraints, such as library requirements or specific design patterns. OpenAI: GPT-5.4 showcased a high level of adherence to these constraints. Meta: Llama 4 Maverick faced challenges in interpreting complex prompts, which led to a lower ranking in this category.
Cost & Latency
When deploying models for coding assistants, understanding the resource trade-offs is essential. The following table highlights the financial and temporal costs associated with this run:
| Model | Total Cost (USD) | Avg Completion Tokens | Avg Latency (ms) |
|---|---|---|---|
| OpenAI: GPT-5.4 | $0.010055 | 132 | 0 |
| Meta: Llama 4 Maverick | $0.000358 | 95 | 195 |
While Meta: Llama 4 Maverick is significantly more cost-effective per request, the performance delta in coding tasks suggests that OpenAI: GPT-5.4 provides a higher utility for mission-critical development workflows where code correctness is the primary objective.
Use Cases
- OpenAI: GPT-5.4: Best suited for enterprise-grade coding assistants, complex architectural refactoring, and critical debugging tasks where precision is paramount.
- Meta: Llama 4 Maverick: Potentially useful for high-volume, low-complexity scripting or environments where operational cost optimization is the primary driver over code quality.
Verdict
The evaluation of OpenAI: GPT-5.4 vs Meta: Llama 4 Maverick reveals a clear disparity in specialized coding ability. For developers and teams prioritizing code integrity and robust instruction following, OpenAI: GPT-5.4 is the superior choice, despite the higher associated costs. Meta: Llama 4 Maverick currently lacks the depth required to compete in high-stakes coding environments as tested by our 10-evaluator suite.