Gemini 3.5 Flash
Google DeepMind has announced the release of Gemini 3.5 Flash, a model that delivers fast and consistent performance at a lower price point. Gemini 3.5 Flash is capable of planning and reasoning across large codebases and can deploy subagents to work in parallel. The model has outperformed its predecessor, Gemini 3.1 Pro, on various benchmarks, including Terminal-Bench 2.1 and GDPval-AA.
- ▪Gemini 3.5 Flash delivers fast and consistent performance at a lower price point than other leading models.
- ▪The model can plan and reason across massive codebases and deploy subagents to work in parallel over a long horizon.
- ▪Gemini 3.5 Flash outperforms Gemini 3.1 Pro on coding and agentic benchmarks.
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| Original publisher | X (formerly Twitter) |
| Canonical URL | https://twitter.com/GoogleDeepMind/status/2056787990110994511 |
| Publication time | Tue, 19 May 2026 17:25:55 +0000 |
| Retrieval time | 2026-05-19T17:34:57.931Z |
| Last seen | 2026-05-19T17:34:57.931Z |
| 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 |
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| 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
Google DeepMind@GoogleDeepMindReplying to @GoogleDeepMindGemini 3.5 Flash delivers fast, consistent performance that rivals other leading models – at a fraction of the price. It can plan and reason across massive codebases and deploy subagents to work in parallel over a long horizon. It outperforms 3.1 Pro on coding and agentic benchmarks like Terminal-Bench 2.1, GDPval-AA, and MCP Atlas.5:24 PM · May 19, 202684.1KViews:host{display:inline-block;direction:ltr;white-space:nowrap;line-height:1}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre}11:where(number-flow-react){line-height:1}number-flow-react >…
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