Ember-1
Ember-1: half the tokens, same answersEmber-1 is a new specialized model from Fireworks Research that delivers Kimi K3’s quality with 40% fewer tokens. Built on Kimi K3, it learned to cut unnecessary reasoning while keeping the thinking that matters. We tested it on external benchmarks, in live customer A/B tests, and on our own coding and agent workloads, and quality held up in every setting.
- ▪Ember-1: half the tokens, same answersEmber-1 is a new specialized model from Fireworks Research that delivers Kimi K3’s quality with 40% fewer tokens.
- ▪Built on Kimi K3, it learned to cut unnecessary reasoning while keeping the thinking that matters.
- ▪We tested it on external benchmarks, in live customer A/B tests, and on our own coding and agent workloads, and quality held up in every setting.
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Story provenance
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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 | Fireworks AI |
| Canonical URL | https://fireworks.ai/blog/ember-1 |
| Publication time | Sun, 27 Sep 2026 17:31:53 +0000 |
| Retrieval time | 2026-09-27T19:46:40.048Z |
| Last seen | 2026-09-27T19:46:40.048Z |
| 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 | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| 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
Ember-1: half the tokens, same answersEmber-1 is a new specialized model from Fireworks Research that delivers Kimi K3’s quality with 40% fewer tokens. Built on Kimi K3, it learned to cut unnecessary reasoning while keeping the thinking that matters. We tested it on external benchmarks, in live customer A/B tests, and on our own coding and agent workloads, and quality held up in every setting. Available today, Ember-1 kicks off an ongoing series of specialized models by Fireworks, shaped by what developers want next. Ember is just the start of what you could build with the Fireworks Training platform.How Fireworks Research built Ember-1We heard from users that they needed K3’s coding capabilities at a lower cost, because its long reasoning traces made automated coding expensive at scale.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Fireworks AI.
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