Exa vs Parallel for finding similar companies (lookalike APIs), benchmarked
Both Exa and Parallel return companies that resemble a seed account — Exa via neural company search using a seed name and description, Parallel via agentic web research with natural-language lookalike prompts. Here is how they compare on identical B2B seed companies, scored by the same LLM judge.
Exa vs Parallel, scored on the same seeds
Same input, same judge, same seed companies — so the gaps below reflect relevance, not different test sets. See the full methodology and matrix →
| Metric | Exa | Parallel | Winner |
|---|---|---|---|
| Precision@10 (top-of-list quality) | 95.0% | 74.8% | Exa |
| Precision@25 | 80.8% | 75.0% | Exa |
| Precision@100 (long-list quality, headline) | 52.4% | 67.5% | Parallel |
| Relevant companies returned | 2,515 | 3,242 | Parallel |
How Exa and Parallel find lookalikes
Exa surfaces lookalikes via neural company search using a seed name and description; Parallel via agentic web research with natural-language lookalike prompts. Same seed, same top-K, same LLM judge — so the mechanism difference shows up directly in the relevance numbers above.
Agent sign-up and use instructions
How an agent runs a lookalike / company search on each — the access model (does the agent get in on its own, or does a human?), the exact call, and the credits.
POST api.exa.ai/search · category: companykey · free tier
POST /v1beta/findall/entity-searchkey · free tierComparing other vendors? See the full benchmark → · What is a company lookalike API? →