Ranking basis
Editorial shortlist based on explicit grounding, retrieval, tool-use, and document-synthesis positioning.
Comparability limit: No shared RAG score is available; evaluate retrieval, reranking, generation, and citations separately.
Why these models
Command A
Explicitly optimized for enterprise retrieval and tools. An enterprise-oriented model tuned for retrieval-augmented generation, tool use, and multilingual business workflows, with weights available for research and non-commercial use.
Watch for: weights restrict commercial use; not optimized for consumer creative tasks.
Claude 3.7 Sonnet
Strong document synthesis and instruction following. A hybrid-reasoning model that lets developers trade latency and cost for a longer visible reasoning process. It is a strong fit for code maintenance and multi-step document work.
Watch for: closed weights; extended thinking increases token usage.
Mistral Small 3.1
Affordable self-hosted generation layer. A compact multimodal model aimed at low-latency assistants and local deployment. Its Apache license and moderate size make it unusually practical for customization.
Watch for: trails frontier models on complex reasoning; smaller ecosystem than llama.
How to choose
- Measure citation correctness separately from answer quality.
- Test refusal behavior when retrieval has no answer.
- Keep retrieval, reranking, and generation evaluations separate.
Evaluation recipe
Build a permissioned set of representative tasks, including expected failures and ambiguous inputs. Run candidates with equivalent prompts and tools. Score task success, latency, total tokens, retries, and human correction time. Keep provider-reported results separate from private measurements.