Beating GPT-5.6 Sol on retrieval with 100x cheaper open models
The problem is that turning raw data into something usable is hard, and letting agents read, search, and mutate data cheaply at scale requires advanced infra. Pointing Castform at Neon skips both.”Ying Hang Seah, cofounder, Castform A "good agent" needs to be strong in 2 areas: Context: can we provide the tools to find the right data? Model: can the model decide what to search for?
- ▪The problem is that turning raw data into something usable is hard, and letting agents read, search, and mutate data cheaply at scale requires advanced infra.
- ▪Pointing Castform at Neon skips both.”Ying Hang Seah, cofounder, Castform A "good agent" needs to be strong in 2 areas: Context: can we provide the tools to find the right data?
- ▪Model: can the model decide what to search for?
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Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Neon |
| Canonical URL | https://neon.com/blog/how-castform-neon-beats-frontier-models-on-price-and-efficiency |
| Publication time | Wed, 05 Aug 2026 18:18:56 +0000 |
| Retrieval time | 2026-08-05T19:05:47.442Z |
| Last seen | 2026-08-05T19:05:47.442Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 4bcLUeJNyMQY · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Back to Blog/ProductHow Castform + Neon Beats Frontier Models on Price and EfficiencyA 4B open-source model post-trained with Castform retrieved search results as accurately as GPT-5.6 Sol, while costing 100x lessPranav Aurora, Ying Hang Seah, Angel PanAug 05, 2026Subscribe to our changelogReceive only our latest updates. No spam, guaranteed.Subscribe “Most teams' best training data is just sitting in their databases. The problem is that turning raw data into something usable is hard, and letting agents read, search, and mutate data cheaply at scale requires advanced infra.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Neon.
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