Cy vs Llama 3.2 90B Vision Instruct

Cy and Llama 3.2 90B Vision Instruct are both large language models tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

Cy

Cy lives inside Slack and connects to your existing tool stack — CRM, inbox, LinkedIn, accounting software, ad accounts — executing multi-step jobs without a handoff back to you. The vendor demonstrates this with accounts receivable chasing, outbound sequence management, influencer pitching, and weekly reporting, all running on a schedule without a trigger from your team. Because there is no canvas to configure and no workflow builder to learn, setup is fast and the barrier to delegation is low. The ceiling appears when you need branching logic, conditional routing, or exceptions handled differently by account type — the page describes plain-English instructions, not configurable decision trees. Teams that need auditable step-by-step control over what Cy does when something goes wrong will find the abstraction uncomfortable.

Llama 3.2 90B Vision Instruct

Meta's 90B multimodal large language model with vision capabilities, fine-tuned for instruction-following across text and image understanding tasks.

Attribute	Cy	Llama 3.2 90B Vision Instruct
Pricing	Paid	—
Price	$20/mo	—
Free trial	No	No
Open source	No	Yes
Has API	No	No
Self-hosted option	No	No
Platforms	Slack	—
Pros	Executes multi-tool jobs end to end inside Slack without switching context, so your team is not the integration layer between CRM updates, email sends, and Notion logs. Scheduled always-on roles mean daily work like invoice chasing or outbound sequencing runs without a daily prompt, so recurring tasks do not slip when your team is heads-down on something else. Plain-English instructions replace workflow configuration, so there is no builder to learn and delegation does not require an ops specialist to set up. Connects to 1,000-plus integrations and syncs results back to the tools already in your stack, which means you are not maintaining a parallel system of record alongside Cy. Learns from each run and logs what worked, so repeated jobs compound rather than starting from scratch each cycle — the vendor's outbound example shows it updating a playbook automatically.	Strong multimodal capabilities combining text and vision in a single model Competitive performance with proprietary vision models like GPT-4V Fully open-source with published weights under permissive license Efficient 90B parameter size suitable for on-premise deployment Excellent instruction-following and reasoning abilities
Cons	Plain-English instructions are the only control layer, so when a job hits an exception — a disputed invoice, a prospect who asked to be removed, an escalation that needs a different tone — there is no rule to write and no branch to configure. The task either runs incorrectly or Cy stops and waits. Teams with high exception rates will spend more time correcting outputs than they saved by delegating. There are no approval gates before Cy acts in live systems. If an outbound sequence goes to the wrong segment or an invoice is chased incorrectly, the action has already happened in HubSpot, Amplemarket, or your inbox before you see the thread summary. Teams that need to review before anything ships in a customer-facing context will need a different architecture — one that surfaces a draft and waits for sign-off. No self-hosted option exists, which is a hard stop for organizations under data residency requirements or in regulated industries where customer data cannot leave a specific environment. Teams in those categories move to a self-hostable alternative regardless of how well the workflow fits.	Requires significant computational resources (GPU memory) for inference Vision performance not yet benchmarked against all major proprietary competitors Slightly lower performance on some specialized vision tasks compared to larger proprietary models

Bottom line

Llama 3.2 90B Vision Instruct is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Cy and Llama 3.2 90B Vision Instruct?

Cy is Paid, while Llama 3.2 90B Vision Instruct is unknown pricing and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is Cy better than Llama 3.2 90B Vision Instruct?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.