Mirendil, founded by ex-Anthropic researchers, unveils Fern: its first from-scratch model with up to 10M tokens of context
On Oct 26 Mirendil unveiled Fern, its first model, pretrained from scratch on its own Mirendil-Base (0.1-0.5T params) with a context window of up to ~10M tokens, about 0.5M lines of code or days of agent traces. Fern 0.1 got only a short round of post-training and targets AI R&D work such as auditing training data, debugging runs from logs and reading agent traces end to end. Vendor-reported: 73.4% on SWE-Bench Verified and 100% single-needle RULER retrieval at 10M, while multi-hop QA drops to 45% at 10M. Access is early-access only; no price or weights yet.
Key Takeaways
- Scale and context (vendor-reported): Mirendil-Base has 0.1-0.5T params; Fern reads up to ~10.2M tokens (about 23 MB or ~0.5M lines of code, roughly Megatron-LM scale), per the official blog
- Coding scores (run by Mirendil, OpenCode, 1 attempt): SWE-Bench Verified 73.4, SWE-Bench Pro v2-Hard 76.5, SWE Atlas QnA 53.2, DeepSWE v1.1 44.2, Terminal-Bench 4.0 just 6.1; Claude Opus 5.5 in the same table scores 98.2/100.0/66.4/74.2/66.4
- RULER long context (vendor-reported): single-needle retrieval is 100% from 32K to 10M; multi-hop HotpotQA falls from 65% at 32K to 45% at 10M, with only 20 samples per 10M cell
- Cost (vendor-reported): one researcher plus agents built Mirendil-Base in 3.5 months for under $10M of compute, with agents writing over 99% of the code, per the pretraining write-up
- Availability: Fern is the smallest of three models; Base-Large (0.5-2T) and Base-XL (2-10T) are still pretraining; access is via early-access requests only, with no price or weights announced
Linked Models & Agents:
