DeepResearch’s performance is evaluated using multiple benchmarks beyond its score on “Humanity’s Last Exam.” While that exam tests broad reasoning and problem-solving skills, other metrics focus on specific technical capabilities. For example, DeepResearch has been tested on standard NLP benchmarks like GLUE (General Language Understanding Evaluation) and SuperGLUE, which measure tasks such as text classification, question answering, and natural language inference. These benchmarks provide granular insights into its ability to handle structured language tasks, with scores often compared against models like GPT-4 or PaLM to contextualize performance.
Another set of benchmarks includes domain-specific evaluations. For instance, DeepResearch’s code-generation skills have been measured using HumanEval (a Python coding test) and MBPP (Mostly Basic Python Problems), where it demonstrates proficiency in generating syntactically correct and logically sound code. In mathematical reasoning, it has been tested on datasets like GSM8K (grade-school math problems) and MATH, which require multi-step calculations and symbolic manipulation. These results highlight strengths in logical consistency and domain adaptation, though performance varies depending on problem complexity and training data coverage.
Efficiency metrics also play a role in evaluating DeepResearch. Parameters like inference speed (latency), memory usage, and computational cost per query are tracked, especially for real-time applications. For example, in a deployment scenario requiring low-latency responses (e.g., chatbots), benchmarks might compare its throughput in tokens per second against smaller models like Mistral-7B. Additionally, robustness tests—such as adversarial attacks or out-of-distribution generalization—assess how well it handles edge cases or noisy inputs. While not always publicly documented, these metrics are critical for developers integrating DeepResearch into scalable systems.