Thomson-1.0-Small LLM Developed by Thomson Reuters
It is designed for high-stakes professional work across legal, tax, and journalism domains, leveraging a continual learning paradigm and a 262,144 token context length. The model demonstrates strong performance in domain-specific tasks like legal document processing, tax Q&A, and deep research, while maintaining general capabilities. OverviewModel OverviewThomson-1.0-Small is a 35.1 billion parameter Mixture-of-Experts (MoE) causal language model developed by Thomson Reuters, in partnership with Imperial College London, DatologyAI, and Lambda.
- ▪It is designed for high-stakes professional work across legal, tax, and journalism domains, leveraging a continual learning paradigm and a 262,144 token context length.
- ▪The model demonstrates strong performance in domain-specific tasks like legal document processing, tax Q&A, and deep research, while maintaining general capabilities.
- ▪OverviewModel OverviewThomson-1.0-Small is a 35.1 billion parameter Mixture-of-Experts (MoE) causal language model developed by Thomson Reuters, in partnership with Imperial College London, DatologyAI, and Lambda.
Hacker News (AI / LLM) files mainly under ai. We currently carry 5,795 of its stories.
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 | Featherless |
| Canonical URL | https://featherless.ai/models/thomsonreuters/Thomson-1.0-Small |
| Publication time | Mon, 21 Sep 2026 08:58:46 +0000 |
| Retrieval time | 2026-09-21T09:38:48.015Z |
| Last seen | 2026-09-21T09:38:48.015Z |
| 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 | -RLuatKqPZjy · 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
Models Qwen3 5 Moethomsonreuters/Thomson-1.0-Small Hugging Face Use via API TEXT GENERATIONPricing:Input $0.4 / Cached $0.07 / Output $4Concurrent Unit Cost:3Model Size:35.1BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 18, 2026License:otherArchitecture:Transformer0.2K Featherless Exclusive WarmThomson-1.0-Small is a 35.1 billion parameter Mixture-of-Experts causal language model developed by Thomson Reuters, built upon the Qwen3.6-35B-A3B architecture. It is designed for high-stakes professional work across legal, tax, and journalism domains, leveraging a continual learning paradigm and a 262,144 token context length.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Featherless.
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