---
title: "Mac-1 6.6B Model Outperforms Major Competitors"
url: https://stacklist.com/card/2f692381-2c6f-46ef-a26a-2d3144124489
source_url: "https://x.com/cjzafir/status/2062928405344272880"
stack: https://stacklist.com/stack/4aae218c-38d7-4c05-b7ae-f2db0029a2e8
summary: "CJ Zafir announces Mac-1, a 6.6B parameter AI model that reportedly outperforms Claude Haiku 4.5, GPT 5.4 mini, and Gemini 3 Flash while running locally on a MacBook M3 with only 7GB RAM. The model supports web search, tool calling, follow-up questions, file search, email writing, and event booking, with full benchmark results promised within two days."
tags: "ai-model, mac-1, benchmark, small-language-model, on-device-ai, macbook, siri-alternative"
key_entities: "CJ Zafir (person), Mac-1 (technology), Claude Haiku 4.5 (technology), GPT 5.4 mini (technology), Gemini 3 Flash (technology), MacBook M3 (technology), Siri (technology), small language model (concept)"
classification: "snippet"
content_hash: "sha256:b8e017ae3cb54871c01fb9d2dfc6aa7d53b9dfcd7ad8832ecfbaa5555deb85d0"
acp_version: "0.2"
token_counts_approximate: 665
visibility: public
agent_accessible: true
status: "final"
---

# Mac-1 6.6B Model Outperforms Major Competitors

CJ Zafir @cjzafir Our first model Mac-1 6.6B beating 3 giant models. - Haiku 4.5 - GPT 5.4 mini - Gemini 3 flash Running this model on my Macbook M3 24GB. (model takes only 7GB RAM) It searches web, call tools, ask follow-ups, tell jokes, find contacts, search files, write emails, book events, ers and so much Siri can't do. Read again, a 6.6B model. Will share full 2000+ scenario test results & benchmark scores in 2 days. 4:03 PM · Jun 5, 2026 774.5K Views :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 1 6 2 number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 162 :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 1 0 8 number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 108 :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 1 . 8 K number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 1.8K :host{display:inline-block;direction:ltr;white-space:nowrap;line-height:var(--number-flow-char-height, 1em) !important}span{display:inline-block}:host([data-will-change]) span{will-change:transform}.number,.digit{padding:round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) 0}.symbol{white-space:pre} 1 . 4 K number-flow-react > span{font-kerning:none;display:inline-block;line-height:var(--number-flow-char-height, 1em) !important;padding:calc(round(nearest, calc(var(--number-flow-mask-height, 0.25em) / 2), 1px) * 2) 0} 1.4K Read 162 replies