Mirendil releases Fern coding model with 10M-token context
MarkTechPost: Mirendil Launches Fern 0.1, Its First Model, With 10M-Token Context
Mirendil has released Fern, its first AI model. Mirendil Fern is a from-scratch coding model with a context window of up to 10M tokens. Mirendil explains how that equals about 23 MB of code, or roughly half a million lines. That is a codebase on the scale of Megatron-LM, held in context at once. Fern 0.1 is the first version, built from Mirendil-Base after a short round of post-training. For developers, its like, you load a whole repo, a full training log or days of agent traces at once. TL;
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Mirendil released Fern 0.1, its first AI model designed specifically for coding and research work, with a context window of up to 10 million tokens, equivalent to about 23 MB of code or roughly half a million lines. The model was built by one researcher plus AI agents in under $10 million in compute costs, using only publicly available training data with no distillation or synthetic data. Fern excels at long-context retrieval tasks, achieving 100 percent accuracy on standard benchmarks at every length from 32K to 10 million tokens, but performs weaker on agentic coding benchmarks compared to frontier models like Claude Opus 5.5. The model is currently available only through early access request and is intended for use cases like loading entire codebases, analyzing training logs, and debugging long agent traces in a single context window.
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