Meta Muse Glimmer
Meta Superintelligence Labs' 30B multimodal model for always-on local agents — tuned for tool use, long tasks, and failure recovery.
Meta's Muse Glimmer is an open-weight text model. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. It runs entirely on your own hardware through Ollama: no API key and no per-use fees.
- Released
- Aug 2026
- Pricing
- Free, runs on your hardware
- Inputs
- Text, Images
- Text rank
- #125
- Code rank
- #121
Samples
Generated with Muse Glimmer via Ollama. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API is a set of rules that lets different software talk to each other. It is an agreed way for one program to ask another for information or an action, and receive a reliable response back without needing to understand how it works inside.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
Use a hosted authentication provider. With two people you cannot afford the ongoing security, compliance, and maintenance burden of passwords, MFA, password resets, breach response, and evolving standards. A provider lets you ship login in hours and focus on product-market fit instead of becoming an identity team. The single strongest argument against that is loss of control and long term cost. If the provider changes pricing, suffers an outage, or is compromised, your user access is hostage to their decisions and you have no direct mitigation. You also build deep dependency early, which can make migration painful and expensive once you scale.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
Your AI spending looks unpredictable because the technology is still maturing and the cost drivers change quickly. First, usage is not linear. A model may work well in a pilot with a few hundred queries but cost explode when rolled out to thousands of employees who ask longer questions and trigger more retries. Second, vendor pricing is shifting. Providers change rates for compute and tokens, introduce new model tiers, and charge differently for fine-tuning, retrieval, and guardrails. A contract signed today can be uncompetitive in six months. Third, internal demand is organic. New teams discover capabilities and build shadow experiments without central approval, creating fragmented workloads that are hard to track or consolidate. You can make this more controllable without slowing innovation. Establish a small AI finance office to tag projects, set usage budgets, and require a lightweight business case before production. Standardize on a few approved models and platforms so costs are visible and negotiable. Review spend monthly against expected value, not just usage, and renegotiate vendor agreements with volume caps and flexibility clauses. With clear guardrails and visibility, spending becomes a managed investment rather than a surprise. This approach gives you predictable ranges and keeps strategic options open as the market evolves today.
Meta
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