ModelRefs / Research Assistant — Canonical Workflow
Research Assistant — Canonical Workflow
Canonical Research Assistant workflow: reasoning models, retrieval, evaluation and benchmarks.
Overview
A research assistant decomposes complex questions, retrieves grounded sources, reasons across them and synthesises cited answers. It pairs a reasoning-capable LLM with retrieval and evaluation loops so analysts can trust the output.
Implementation profile
| Category | reasoning-models |
|---|---|
| Implementation maturity | production |
| Primary use cases | reasoning, summarization, rag |
| Deployment options | managed-api |
| Architectures | serverless-api, managed-container |
Candidate models with published references
- BGE-M3
- GPT-5
- GPT-5 Mini
- Claude Sonnet 4.5
- Claude Opus 4
- Gemini 3 Pro
- Llama 4 Scout
- DeepSeek R1
- Mistral Large 2
- Command R+
- o3
- o4 Mini
Coverage means the model is a candidate worth evaluating for this workflow, not a ranking or a recommendation. Models whose reference pages are still in review are omitted.
Benchmarks relevant to this workflow
- MMLU — Massive Multitask Language Understanding — 57 academic and professional subjects.
These are the benchmarks whose task shape bears on this workflow. Each one only describes its own protocol and date, so confirm the harness matches your workload before treating a score as evidence.
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Research Assistant — Canonical Workflow.