Day 1 impressions of Fable 5.1: - talks much more like a normal human now, less Claudish. - web search still feels worse than GPT-5.6 Sol. It often misses the key things to search for. - still much better than GPT models at frontend. - my daily driver is now Kimi K3. We dogfood it heavily at Databricks, and you’d be surprised how good it is.

We unfortunately have decided that we cannot continue providing access to our models through Cursor and are ending our partnership. It boils down to trust and we’ve asked that this takes effect on November 12 to give you some time to plan. Many have used the GPT models through Cursor and here are options we know should work in the future: - We will continue to allow using your own OpenAI API key and similarly will continue to provide access through our IDE extensions for Cursor. - We will keep working with the broadest range of tools and harnesses, some of which are OSS, but also many many closed-source ones. We are as committed as ever to continue supporting developers and the flourishing ecosystem of tools, harnesses and products. We will also continue to invest in our own open-source initiatives and believe in broad optionality for developers. You can read more about our decision in the blog:

introducing ocr in anydoc now your agents can read scanned docs for free → sub-5ms for non ocr → 190ms median per ocr page → layout detection, tables, formulas → ocr hosted option via @firecrawl → free, no api key needed

Excited to announce our new @firecrawl startup deal! More companies are being founded than ever & we want to power every one of them. Now startups get 2x their monthly credits for a full year (up to $30k), plus a Slack channel with our team and swag.

Introducing the new $30k Firecrawl deal for YC 4 years ago we joined YC, and it totally changed the trajectory of @firecrawl Now we're offering the community: - Up to 5x your plan's monthly credits - $30k/yr max - Shared Slack channel & 🔥 swag Build something agents want!

Announcing the Artificial Analysis Search Index, benchmarking how search API providers perform on quality, cost, and speed when used by an agent. We are initiating coverage with Parallel, Exa, Firecrawl, You (dot) com, Tavily, Keenable, and Brave Search is one of the most important tools for agents. Search providers make different choices about how they search, rank, and package results, and those choices change what the model reads and how it acts. We are expanding our benchmarking coverage to search APIs, so developers can pick a search provider on measured quality, cost, and speed. Each provider result pairs a search API provider with the same model, GPT-5.6 Luna (medium). The model runs inside Stirrup, our open-source agent harness, with tools for searching and fetching pages from the web - only the search provider behind the search tool changes. At launch, the leaderboard covers 11 results across 7 search providers, and we’ll keep expanding coverage as we look to provide the most accurate and comprehensive benchmarking of search providers for AI agent usage. Key elements of the Artificial Analysis Search Index: ➤ Three equally weighted benchmarks: the Search Index is the average of DeepSearchQA (900 broad research questions that need many searches, graded with an F1 score over answer items), BrowseComp (a 200-sample hard subset of facts that need multi-hop browsing), and AA-Omniscience (a 600 question private subset, balanced across 6 domains) ➤ Same agent, different search provider: the agent has 25 turns available to complete each task. Its web search tool returns the search provider's native response payload (with content modes standardized to snippets), with a maximum of 10 results and contamination sources filtered out ➤ Model-only baseline: we compare search agent results to the same model answering single-shot without tools, showing how much each provider lifts the model above its internal knowledge ➤ Cost and Time per Task: we aggregate the time and cost spent on both model inference and search. This is key - search APIs have different cost and latency structures, but these can be offset where they help an agent use fewer turns and save on costly language model inference Key results: ➤ Parallel, Exa, and Firecrawl have the strongest overall performance, with Artificial Analysis Search Index scores of 75, 74, and 73 respectively at launch ➤ All search providers tested substantially improve knowledge-based benchmark performance: the model only baseline scores 33 on the Search Index, while search-included provider results score between 65 and 75 ➤ Focused search results reduce spend on model inference: Parallel Search (advanced) search costs more per task than Parallel Search (basic) but less per task in total ($0.084 vs $0.11). Higher quality results cut the model's token use by over 40% in this case, more than offsetting increased search costs while reaching higher benchmark scores ➤ Fast search calls do not guarantee fast tasks: Parallel Search (turbo) has the fastest average search calls among Parallel's tiers (0.51s per query vs 1.03s for Parallel Search (basic)) but the basic tier scores higher on quality (73 vs 67) and the two land close on total time per task

Introducing the official Firecrawl connector for Claude 🔥 Add state-of-the-art web search to your AI agents (94.7% on SimpleQA). Powered by our live indexes for fresher results across research, the open web, and more. Live in the @AnthropicAI connector directory now!