LLM Knowledge Base — Benchmarks, Tokens & Context Explained

Intelligence density is Prism ML's size-normalized quality metric for LLM deployments. It is defined as:

D = -log₂(1 - score/100) / size_GB

where score is the model's average benchmark score in percent and size_GB is the deployed size in GB. The numerator converts the error rate into an information-style scale (quality gains close to 100 count for more), and dividing by gigabytes measures how much benchmark intelligence each GB of footprint buys — reframing model choice from best score to most intelligence per gigabyte.

For Ternary-Bonsai-2-27B-gguf: an 84.78 average over 14 thinking-mode benchmarks at 5.95 GB gives D = 0.457/GB — about 1.8x the densest conventional build (Qwen3.8-27B IQ2_XXS at 0.257/GB), nearly 9x the FP16 baseline (0.053/GB at 54.66 GB), and up from the previous release (Ternary Bonsai 27B at 0.416/GB). Prism ML introduced the metric when the Bonsai line launched and has used it to frame every release since.

Terminal-Bench is an open benchmark that measures how well AI agents solve real, complex tasks in a live terminal or command-line environment — installing dependencies, analyzing data, debugging and repairing code. It is maintained as a continuous benchmark with tagged releases on the Harbor Hub precisely so the task frontier does not saturate, and each task ships with a programmatic verification suite the agent must satisfy, so success is scored against executable criteria rather than judged text.

Scores are harness- and version-relative: Nanbeige4.3-33B posts 80.9 on Terminal-Bench 2.1 with the Terminus 2 agent harness and 23.8 on the much harder Terminal-Bench 4.0 with mini-swe-agent v2.4.6 — versus Qwen3.8-27B (73.0 / 6.3), Muse-Glimmer-30B (61.8 / 1.6) and Gemma4-31B (47.2 / 0.0) under one shared harness and inference settings. These results are Nanbeige's headline agentic-coding evidence.