Muse Glimmer 30B wins on price ($0.35 vs $1.25/1M input). Muse Spark wins on coding (89 vs 80) and writing quality and context window (1.048576M vs 131K). For most workflows, Muse Spark is the stronger default — best-value multimodal agentic model — gpt-5.5-tier smarts, video/audio/pdf in.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
Muse Spark is the safest overall answer here when you want the strongest default instead of the lowest list price.
Meta: Llama 3.1 8B Instruct is the lower-cost option to start with when you still need useful output at scale.
Muse Glimmer 30B is the better pick when response speed matters more than maximum reasoning depth.
Muse Spark leads on coding with a score of 89 vs 80 for Muse Glimmer 30B.
Muse Spark has the larger context window: 1.048576M vs 131K for Muse Glimmer 30B.
Muse Glimmer 30B is cheaper at $0.35/1M input tokens vs $1.25/1M for Muse Spark.
Choose Muse Spark for agentic tool-use and multimodal reasoning at aggressive pricing. Its reasoning and multimodal scores are what carry the recommendation here.
Switch to Muse Glimmer 30B when your work is mostly local and self-hosted agents that run continuously; on that narrower brief it is the better tool.
Muse Glimmer 30B is the more cost-efficient option at $0.35/1M input — Muse Spark costs 4x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
Meta / Balanced / Aug 6, 2026
Best-value multimodal agentic model — GPT-5.5-tier smarts, video/audio/PDF in.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need frontier-ceiling reasoning or a mature developer ecosystem — Opus 5 and GPT-5.6 lead both.
The fastest way to see where the recommendation shifts when your priority changes.
Apache 2.0 agent model that runs on a 24GB GPU.
Best-value multimodal agentic model — GPT-5.5-tier smarts, video/audio/PDF in.
Every figure below is the provider's list price or a published capability score — the same numbers the recommendation on this page is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Muse SparkMeta | $1.25/1M | $4.25/1M | $21 | 1.0M tokens | Balanced | 89 | 88 | 90 |
| Muse Glimmer 30BMeta | $0.35/1M | $1.50/1M | $6.50 | 131k tokens | Fast | 80 | 74 | 76 |
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
Meta Superintelligence Labs' first closed frontier model — a natively multimodal agentic reasoner (text, image, video, audio, PDF in) with a parallel-agent 'Contemplating mode', priced aggressively below rivals.
You need frontier-ceiling reasoning or a mature developer ecosystem — Opus 5 and GPT-5.6 lead both.
Meta's surprise pivot to closed frontier models is a genuine value play: GPT-5.5-tier intelligence with the broadest multimodal input support, at prices that undercut OpenAI and Anthropic. The young API ecosystem is the main practical drawback.
v1.0 launched April 8, 2026 alongside Llama 5; v1.1 (July 9) opened the paid API; v1.2 (Aug 5) is coding-focused and powers Muse Code. Built with 'over an order of magnitude less' pretraining compute than Llama 4 Maverick. Cache hits $0.15/1M.
Meta's return to genuine open source — a 30B dense model under Apache 2.0, built for always-on agents rather than chat, and the first release from Meta Superintelligence Labs.
You need a large context window or the strongest agent scores at this size — check Qwen's 27B first.
The best Apache 2.0 agent model you can run on consumer hardware right now. Pick it when licence freedom and local execution matter more than the last few benchmark points — otherwise Qwen3.6-27B edges it on agent tasks.
Released August 9, 2026 — the first model from Meta Superintelligence Labs and Meta's return to a genuinely permissive licence. No Meta API price; hosted rates from third parties such as Together and OpenRouter land around $0.35/$1.50. Local hardware targets: 24GB for K-Quant-17GB, 32GB for K-Quant-Dynamic, 64GB for full precision.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Muse Spark wins on more of the categories we score — reasoning, multimodal, research — so it is the better default of the two. Muse Glimmer 30B is the better pick when your work is mostly local and self-hosted agents that run continuously. Neither is universally "better": Muse Spark is aimed at agentic tool-use and multimodal reasoning at aggressive pricing, Muse Glimmer 30B at local and self-hosted agents that run continuously.
Muse Glimmer 30B is cheaper at $0.35/1M input and $1.5/1M output. Muse Spark costs $1.25/1M input and $4.25/1M output.
Muse Spark has the larger context window at 1.048576M tokens vs Muse Glimmer 30B's 131K. For large document analysis, Muse Spark is the stronger pick.
Muse Spark is better for coding with a score of 89 vs Muse Glimmer 30B's 80 (out of 100). Claude Fable 5 is the overall coding leader in this directory at 100/100.
Muse Glimmer 30B is faster with a fast speed rating (score: 4) vs Muse Spark's balanced rating (score: 3). Speed matters most for interactive and high-throughput work; for batch jobs the Muse Spark latency penalty is usually invisible.
Trails the frontier on raw intelligence: AA Intelligence Index 54 vs Claude Opus 5 (61) and GPT-5.6 Sol (59). Weak on some hard agentic evals (27% tau3-Banking) and the API ecosystem is young — public API only since July 2026. Avoid it if you need frontier-ceiling reasoning or a mature developer ecosystem — Opus 5 and GPT-5.6 lead both. That is the main case for looking at Muse Glimmer 30B instead.
Qwen3.6-27B beats it on several practical agent and multimodal tests in Meta's own comparison table. Meta publishes no first-party API price — you pay a third-party host or run it yourself. 131K context is small next to the 1M-token field. Avoid it if you need a large context window or the strongest agent scores at this size — check Qwen's 27B first. Against Muse Spark specifically, the gap shows up most on coding (89 vs 80).
Take a moderate workload of 10M input and 2M output tokens a month. Muse Glimmer 30B runs $6.50 (at $0.35/1M in and $1.5/1M out); Muse Spark runs $21.00 (at $1.25/1M in and $4.25/1M out). That is a $14.50/month difference — Muse Glimmer 30B is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Yes, and for most teams that beats picking one. A common split is Muse Spark for agentic tool-use and multimodal reasoning at aggressive pricing, with Muse Glimmer 30B handling local and self-hosted agents that run continuously. Routing high-volume, low-stakes calls to Muse Glimmer 30B at $0.35/1M and reserving Muse Spark for the hard cases is usually the cheapest arrangement that does not cost you quality.