Mirendil unveils Fern, an AI model claiming a 10 million token context window. Mirendil's Fern 0.1 scored 100% on single-needle retrieval and 90% on multi-key retrieval at the full 10 million tokens. Access is listed as coming soon, with early requests open and no pricing disclosed, so enterprise and research-lab customers will likely be first. A one-year-old AI lab just claimed a context window ten times bigger than Gemini or Claude offer today, while its company-run benchmark reported 45% accuracy on multi-hop questions at the full context length. Mirendil released Fern 0.1 on October 26, and the headline figure is hard to ignore. The model can hold up to 10 million tokens of context, according to the company, which it says is roughly equivalent to 23 megabytes of code or about 500,000 lines, close to the full size of Nvidia's Megatron-LM codebase. For comparison, Google's newer Gemini 4 Argon and Anthropic's Claude Opus 5 both have 1-million-token context windows. If Fern's number holds up under real use, it's not an incremental gain. It's a different category of tool. Context window size determines how much text a model can actually hold in its head at once: an entire codebase, a full legal contract, days of agent logs, without chopping everything into chunks and hoping a retrieval system finds the right piece later. That retrieval workaround, known as RAG, is the industry's current patch for memory limits, and it's exactly what a 10-million-token window promises to make unnecessary for certain jobs. Mirendil isn't a garage startup. It launched out of stealth in June with a $200 million seed round at a $1 billion valuation, co-led by Andreessen Horowitz and Kleiner Perkins with Nvidia also putting in money, according to reporting from The Next Web and SiliconANGLE at the time. Founders Behnam Neyshabur and Harsh Mehta both came out of Anthropic and Google, and the company's stated mission was never general chat. It's building AI meant to help researchers design experiments, tune training runs, and evaluate models faster than a human team can. Fern fits that mission directly rather than chasing ChatGPT's lane. Mirendil says Fern 0.1 is a post-trained version of Mirendil-Base, a foundation model the company says it built in just 3.5 months with a single researcher, for under $10 million in compute. That's a strikingly lean figure for a model of this scale, though the company itself is the only source for it. Mirendil is pitching Fern at auditing training data, debugging failed training runs from logs, and reading an AI agent's full multi-day trace end to end, tasks where a huge, literal window beats clever retrieval because nothing can afford to get missed. Tailcall's ForgeCode tops its own TermBench 2.0 leaderboard at 81.8%, but on the independently run SWE-bench Verified it edges Codex 1 by just 0.6 points, 72.7% to 72.1%, a far narrower gap than its marketing suggests. - how ForgeCode compares to Claude Code performance - ForgeCode TermBench 2.0 leaderboard score explained The benchmark tells a more complicated story. Here's the part worth reading past the headline number. Mirendil evaluated the base model with RULER, a long-context benchmark developed by Nvidia researchers, at the full 10-million-token length. Based on 20 examples for each reported result at that length, the company reported 100% accuracy on single-needle retrieval, 90% on multi-key retrieval, and 45% on multi-hop questions. That's not a hidden flaw. It's long-context AI's open secret, and Mirendil published it rather than burying it. Long-context models can perform worse on more demanding retrieval and reasoning tasks than on single-needle tests, and a 10x bigger window on the easy test doesn't mean 10x more usable intelligence on the hard one. A 45% score on multi-hop questions at full length means the model gets those wrong more than half the time. For a lab pitching itself on rigor, publishing that number is a point in its favor. For buyers deciding whether to trust Fern with something like full codebase analysis, it's the number to actually plan around, not the 10 million headline. Access to Fern is listed as "coming soon," with Mirendil taking early access requests as of its launch date and no pricing disclosed yet. Given the company's framing around AI research workflows rather than consumer chat, expect enterprise and research-lab customers, not a public chatbot, to be first in line. Whether Fern finds a real niche or gets swallowed once Gemini or Claude simply extend their own windows past 1 million tokens is the open question, and it's one only actual usage, not a benchmark page, will answer. Join the discussion. No replies yet. Start the discussion. Judith Murphy is a financial journalist and market analyst covering AI, technology stocks, and emerging market trends. She has contributed to multiple financial publications and brings a data-driven approach to her coverage of the technology sector and its impact on global markets.

Former researchers from leading AI labs have launched Mirendil, raising $200 million in seed funding at a $1 billion valuation. Andreessen Horowitz and Kleiner Perkins led the round, with Nvidia also participating. The startup builds AI systems that can improve themselves with minimal human intervention, potentially accelerating discoveries in medicine and materials science. The founders left Anthropic to pursue broader access to advanced AI tools. The 20-person team includes talent from Google and xAI. Mirendil has already secured a major Google Cloud partnership for infrastructure support. The platform aims to enable scientists outside large corporations to create custom models for specific research problems. The high seed-stage valuation reflects investor confidence in democratising AI research capabilities beyond giant laboratories.

A16z in talks to invest in AI startup Mirendil's $1b round. Mirendil, an AI startup founded by former Anthropic researchers, is in talks to raise up to US$1 billion at a US$5 billion valuation including the investment, according to people familiar with the matter. Kleiner Perkins is in talks to lead the round, and Andreessen Horowitz is also discussing an investment. The talks come about three months after Mirendil announced a US$200 million seed round led by the same firms at a US$1 billion valuation. Mirendil said it is building widely accessible self-improving AI models. The startup plans to launch a frontier model for engineering and research work by the beginning of next year. The approach also faces scrutiny because recursive self-improvement could outpace human testing and oversight. Stay updated on the go with our mobile app. Get latest insights with smoother, more personalized experience through TIA mobile app. How would you feel if you could no longer use Tech in Asia? Share, tag us, and land on our Wall of!