Can open source models fine-tuned on domain specific data truly match the out of the box reasoning capabilities of Claude 3.5 Sonnet for B2B sales?
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Can open source models fine-tuned on domain specific data truly match the out of the box reasoning capabilities of Claude 3.5 Sonnet for B2B sales?
2 real searches · 1 real-search pass
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (2)
Classes come from the query-classifier (a local Laya model). 1 of 2 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT2 real searches · gpt-6
open source fine tuned models domain specific data reasoning capabilities benchmark Claude 3.5 Sonnet sales agents 2025 2026 Google ↗
fine-tuning versus RAG domain-specific language models reasoning performance enterprise tasks benchmark Google ↗
- Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications · alphaxiv.org
- PIKE-RAG: Enabling industrial LLM applications with domain-specific data · microsoft.com
- Benchmarking Domain Intelligence · databricks.com
- Published in Transactions on Machine Learning Research (01/2026) · openreview.net
Queries
2 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
open source fine tuned models domain specific data reasoning capabilities benchmark Claude 3.5 Sonnet sales agents 2025 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Reason | — | — | 1x of 1 | |
fine-tuning versus RAG domain-specific language models reasoning performance enterprise tasks benchmark OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 |