Overview
In this technical evaluation, we pit OpenAI: gpt-oss-120b vs Meta: Llama 4 Maverick against each other to determine which model excels in complex software engineering tasks. Using the PeerLM Coding Performance with 10 Evaluators suite, we assessed these models on their ability to generate accurate code and strictly adhere to complex developer instructions.
Benchmark Results
The comparative evaluation reveals a significant performance gap between the two models. OpenAI: gpt-oss-120b consistently outperformed the competition, securing the top rank with an overall score of 8.21. In contrast, Meta: Llama 4 Maverick struggled to maintain parity in this specific coding environment, finishing with an overall score of 1.79.
| Model | Overall Score | Accuracy | Instruction Following |
|---|---|---|---|
| OpenAI: gpt-oss-120b | 8.21 | 8.21 | 8.21 |
| Meta: Llama 4 Maverick | 1.79 | 1.79 | 1.79 |
Criteria Breakdown
The evaluation focused on two primary pillars of coding assistance: Accuracy and Instruction Following. Because this was a comparative ranking-based evaluation, the scores reflect how these models performed relative to each other under the scrutiny of 10 independent evaluators.
- Accuracy: OpenAI: gpt-oss-120b demonstrated high reliability in syntax and logical implementation, while Meta: Llama 4 Maverick faced challenges in producing functional, bug-free code blocks.
- Instruction Following: When provided with multi-step architectural constraints, OpenAI: gpt-oss-120b successfully adhered to the requirements, whereas Meta: Llama 4 Maverick frequently deviated from the requested implementation patterns.
Cost & Latency
Efficiency is a critical bottleneck for any production coding assistant. The following table illustrates the cost and performance metrics captured during the run:
| Model | Avg Latency (ms) | Total Cost (USD) | Cost per Output Token |
|---|---|---|---|
| OpenAI: gpt-oss-120b | 188 | 0.00036 | 0.000218 |
| Meta: Llama 4 Maverick | 0 | 0.000358 | 0.000942 |
While Meta: Llama 4 Maverick shows a lower total cost, it does so at the expense of significantly lower output volume and lower quality scores. OpenAI: gpt-oss-120b provides a much denser, more useful response per token, making it more cost-effective for high-stakes coding tasks.
Use Cases
OpenAI: gpt-oss-120b is currently the superior choice for enterprise-grade coding tasks, including automated refactoring, complex algorithm generation, and debugging. Meta: Llama 4 Maverick, while currently ranking lower in this specific coding suite, may find niche applications in low-complexity scripting where the overhead of larger models is not required.
Verdict
For developers requiring reliable, production-ready code generation, OpenAI: gpt-oss-120b is the clear winner. The 6.42-point score spread demonstrates that Meta: Llama 4 Maverick is not yet equipped to handle the demands of the Coding Performance with 10 Evaluators benchmark at the same level of precision as its competitor.