Overview
In the rapidly evolving landscape of Large Language Models, developers require precise data to choose the right tool for complex software engineering tasks. This report evaluates the performance of OpenAI: gpt-oss-120b vs DeepSeek: DeepSeek V3.2 using our rigorous Coding Performance with 10 Evaluators benchmark. By leveraging a comparative ranking methodology, we provide insights into how these models handle instruction-following and technical accuracy in real-world scenarios.
Benchmark Results
The comparative evaluation focused on the ability of each model to generate high-quality, functional code. Based on the aggregate rankings from our 10 evaluators, DeepSeek V3.2 secured the top position, demonstrating a superior grasp of complex coding requirements compared to the gpt-oss-120b variant.
| Model | Overall Score | Accuracy | Instruction Following |
|---|---|---|---|
| DeepSeek: DeepSeek V3.2 | 5.28 | 5.28 | 5.28 |
| OpenAI: gpt-oss-120b | 4.72 | 4.72 | 4.72 |
Criteria Breakdown
Our evaluation criteria focused on two pillars: Accuracy and Instruction Following. In coding tasks, accuracy is defined by the functional correctness of the generated logic, while instruction following measures the model's adherence to specific formatting constraints or library requirements provided in the prompt.
DeepSeek V3.2 achieved a consistent score of 5.28 across both metrics, signaling a high level of reliability for developers. OpenAI: gpt-oss-120b followed closely with a score of 4.72. While the score spread of 0.56 indicates a measurable difference in performance, both models remain highly competitive for general programming assistance.
Cost & Latency
Efficiency is a critical bottleneck in production coding environments. The following table summarizes the cost profiles for these models based on our test run.
| Model | Total Cost (USD) | Avg Completion Tokens | Cost per Output Token |
|---|---|---|---|
| DeepSeek: DeepSeek V3.2 | $0.000447 | 146 | $0.000764 |
| OpenAI: gpt-oss-120b | $0.000360 | 414 | $0.000218 |
While DeepSeek V3.2 leads in performance, OpenAI: gpt-oss-120b offers significant cost advantages, particularly when handling larger output sequences, with a lower cost per output token of $0.000218.
Use Cases
- DeepSeek: DeepSeek V3.2: Ideally suited for high-stakes coding tasks where accuracy is the primary constraint and the priority is minimizing debugging time.
- OpenAI: gpt-oss-120b: An excellent choice for high-volume, cost-sensitive applications like automated docstring generation, boilerplate creation, or large-scale refactoring tasks.
Verdict
The comparison of OpenAI: gpt-oss-120b vs DeepSeek: DeepSeek V3.2 highlights a classic trade-off between peak performance and operational expenditure. For developers prioritizing the highest coding accuracy, DeepSeek V3.2 is the clear winner. However, for teams optimizing for budget without sacrificing too much quality, OpenAI: gpt-oss-120b remains a highly efficient and capable contender.