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Performance Data

Capsule compression
vs raw context

We benchmarked Mesh against standard raw context windows across 14+ frontier models including Claude 4.6 Opus/Sonnet and ChatGPT 4.5, alongside powerful local models like Llama and Gemma. The results are decisive: 5x fewer tokens, 100% recall.

NIAH Needle Recall
100%
Mesh (Ultra Capsules)
Budget Saturation
4.5k vs 24k
Tokens for full coverage
Multi-Hop Chain
12/12
Completeness vs 3/12 raw

LLM NIAH — Relevant Context Recall

Highlights from Claude 4.6 Opus & ChatGPT 4.5. Can the model see the files it needs without blowing the budget?

Mesh Workflow
100% PASS (5/5)
Raw Source
40% PASS (2/5)
Lower raw coverage → model guesses instead of reads because the needle file was pushed beyond the context window by irrelevant token noise.

Budget Saturation (Tokens required to grasp 200 files)

Tested across 14+ distinct model architectures. A smaller footprint allows room for the actual prompt generation.

Mesh (4,500T)
Raw (24,000T)

LLM Hallucination Rate (NOTFOUND Accuracy)

How often does the model invent files or hallucinate variables when the answer is not in the workspace? (Tested across 4 models)

Mesh Workflow
0% (Zero Hallucinations)
Raw Source
68% Hallucination Rate
Without Mesh, models hallucinated answers nearly 7 out of 10 times in a 200-file workspace because the context was too overwhelming.

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