Best Local AI Models for Long Context
Long context models can process entire documents, codebases, and extended conversations in a single prompt. While most models default to 4K-8K context, several open-weight models support 32K to 128K+ tokens locally. More context means more RAM usage, so hardware matters even more for these workloads.
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Top Long Context Models (All Hardware)
RAM Requirements
min 24 GB
min 192 GB
min 48 GB
min 80 GB
min 256 GB
min 256 GB
min 32 GB
min 14 GB
Frequently Asked Questions
What is the best local model for long documents?
Qwen3.5 9B supports long context natively and performs well on document analysis. For 128K context, larger models like Qwen3 14B are the better choice if you have 24GB+ RAM to hold both weights and the KV cache.
How much RAM does a long context model need?
Context length directly impacts RAM usage. A 7B model at 4K context uses about 5.5GB, but at 32K context it may use 8-10GB. At 128K context, even a 7B model can need 16GB+ RAM. Plan for roughly 2x the base RAM requirement at maximum context.
Can I analyze a full codebase locally?
Yes, with limitations. A 128K context window holds roughly 90K words or 300-400 files of typical code. For larger codebases, you will need to chunk the input or use tools that intelligently select relevant files.
Does long context slow down the model?
Yes. Longer prompts increase time-to-first-token because the model must process more input. At 32K tokens, expect 3-8 seconds to start generating. At 128K tokens, it can take 15-30+ seconds depending on hardware.