{"slug": "meta-muse-ember-pricing-what-it-costs-per-million-tokens", "title": "Meta Muse Ember Pricing: What It Costs Per Million Tokens", "summary": "Meta's Muse Ember small model is priced at roughly 10 cents per million input tokens and 50 cents per million output tokens on Meta's API, with a 1 million token context window and a 512,000 token maximum output per response. Meta benchmarks Muse Ember against Claude Haiku 4.5 and GPT 6 Luna, and its published cost-to-intelligence chart, built on cost and intelligence data from artificial analysis, places Muse Ember in the top-left quadrant with higher benchmarked intelligence at lower per-task cost than both rivals. Hands-on testing found fast tool-calling and multi-step tasks such as coding, spreadsheet cleanup and PDF generation finishing in under a few minutes, while multilingual and creative output quality lagged behind the model's coding and reasoning performance.", "body_md": "# Meta Muse Ember Pricing: What It Costs Per Million Tokens\n\nMeta Muse Ember costs about 10 cents per million input tokens and 50 cents per million output tokens, with a 1M token context window.\n\n## What does Meta Muse Ember cost per million tokens?\n\nMeta Muse Ember is priced at roughly 10 cents per million input tokens and 50 cents per million output tokens on Meta’s API. That puts it firmly in the budget tier of small models, alongside Claude Haiku and GPT 6 Luna, the two models Meta benchmarks it against. The model also ships with a 1 million token context window and can produce up to 512,000 tokens in a single reply, which is a large output ceiling for a model this cheap.\n\n## TL;DR\n\n- **Input pricing sits around 10 cents per million tokens** , with output priced at about 50 cents per million tokens on Meta’s API.\n- **The context window reaches 1 million tokens** , large enough to hold long documents, codebases, or multi-step agent transcripts without truncation.\n- **Output capacity goes up to 512,000 tokens per response** , which matters for tasks like generating full Excel reports or long-form code in one pass.\n- **Meta’s own cost-to-intelligence chart places Muse Ember in the top-left quadrant** , meaning higher benchmarked intelligence at lower per-task cost than Claude Haiku 4.5 and GPT 6 Luna.\n- **The pricing data behind that chart comes from artificial analysis** , not just Meta’s marketing, which gives the comparison more weight.\n- **Hands-on testing showed fast tool-calling and multi-step tasks (coding, spreadsheet cleanup, PDF generation) finishing in under a few minutes** , which is where low per-token cost compounds into real savings for agents that repeat steps constantly.\n- **Multilingual and creative output quality lagged behind its coding and reasoning performance** , suggesting the low price reflects a model tuned for structured, repeatable work rather than nuanced language generation.\n\n*Rapidly*build enterprise-grade software applications.\n\n## How does Muse Ember’s pricing compare to Claude Haiku and GPT 6 Luna?\n\nMeta positions Muse Ember directly against Claude Haiku 4.5 and GPT 6 Luna, the two most common small-model choices for cost-sensitive agent work. The comparison chart Meta published (built on cost and intelligence data from artificial analysis) plots model intelligence against the cost of completing a task. Muse Ember lands in the top-left corner of that chart, the spot every buyer wants: higher measured intelligence paired with a lower cost per completed task than either rival.\n\nThat combination matters more than raw per-token price alone. A model can be cheap per token and still expensive per task if it needs more retries, longer reasoning chains, or extra tool calls to finish the job. Muse Ember’s per-token price (10 cents in, 50 cents out per million) is already low, but the task-level cost advantage suggests it also gets to a correct answer in fewer steps, at least on the benchmarks Meta is citing.\n\n## Why does the context window and output limit matter for pricing?\n\nA 1 million token context window means Muse Ember can ingest long documents, large codebases, or extended agent histories without needing to chunk or summarize first. For agentic workflows, that reduces the number of separate calls needed to process a big task, which directly lowers total cost even before you factor in the per-token rate.\n\nThe 512,000 token output ceiling is equally relevant. In hands-on testing, Muse Ember generated a full cleaned-up Excel report, complete with reformatted dates, consistent naming, percentage-based discounts, line totals, a summary tab, and charts, from a single prompt in about three minutes. A model with a smaller output ceiling might need to split that kind of job into multiple calls, each carrying its own latency and token overhead. A large single-shot output window keeps the economics simple: one prompt, one bill, one finished artifact.\n\n## What does Muse Ember actually do well at this price?\n\nMuse Ember is built for the repetitive backbone of agent work: tool calls, coding tasks, extraction, and long document reading. It also handles image, audio, and video input, which widens its use beyond pure text tasks.\n\nIn testing, the model handled several distinct domains:\n\n- **Vision-to-code** : given a hand-drawn sketch of a rotating kebab (doner) cooker, it generated a working interactive simulation with heat, rotation speed, and slicing controls in about 41 seconds and two tool calls.\n- **Spreadsheet cleanup** : it took a deliberately messy sales file (mixed date formats, inconsistent names, bad numbers, duplicates) and returned a cleaned Excel workbook with live formulas, a summary tab, and charts, leaving genuinely missing data blank rather than guessing values.\n- **Diagram-to-PDF** : it converted a labeled diagram image into a one-page PDF explainer, correctly copying box labels and arrows, though some font and color choices were hard to read.\n- **Circuit diagnosis and math** : given a hand-drawn circuit, it read every label correctly, identified a dangling wire and a mismatched resistor label, then calculated the parallel resistance (60 ohms) and current (about 79 milliamps) correctly, with one minor error in how it described the LED branch.\n\n*regulatory compliance and security.*\n\n- Name\n- Maria Alvarez\n- Date of birth\n- SSN\n- Diagnosis\n- Balance due\n- $412.80\n\nEach of these ran quickly and used few tool calls, which is exactly where a low per-token, low-per-task price pays off: agents that repeat similar steps hundreds of times a day accumulate token costs fast, so shaving cents per million tokens adds up.\n\n## Where does Muse Ember fall short despite the low price?\n\nThe one area where the low cost seemed to come with a real quality tradeoff was multilingual and creative generation. In a test asking the model to name a floating bookshop (a river boat) in 81 languages with no repeats and no skipped languages, it covered every language in about 7 seconds, but most outputs were just literal translations of “floating book shop” rather than names with any cultural grounding. Bosnian and Serbo-Croatian came out identical, and languages like Basque and Ukrainian produced results that looked off without native-speaker verification.\n\nThat pattern lines up with what the pricing and benchmark positioning suggest: Muse Ember is tuned for structured, repeatable, verifiable tasks (code, spreadsheets, diagrams, math) rather than open-ended creative or nuanced linguistic generation. If your workload is agentic and procedural, the price-to-performance ratio looks very strong. If you need polished multilingual copywriting or creative naming, it’s worth spot-checking output before trusting it at scale.\n\n## Is Muse Ember worth it for cost-sensitive agent workloads?\n\nFor workloads dominated by tool calls, coding, data extraction, and long-document processing, the combination of low per-token pricing, a 1 million token context window, and a 512,000 token output limit makes Muse Ember a strong candidate as a default small model. The benchmark comparison against Claude Haiku and GPT 6 Luna, backed by third-party cost and intelligence data, suggests it beats both on a cost-per-completed-task basis, not just on sticker price per token.\n\nThe caveat is that “cheap and capable” doesn’t mean “cheap and flawless.” Weak spots showed up in multilingual naming and in some readability details in generated documents. For teams building agents that run the same structured tasks repeatedly, meaning most production agent pipelines, the economics favor giving Muse Ember a real trial rather than defaulting to a pricier incumbent.\n\n## Frequently Asked Questions\n\n### How much does Meta Muse Ember cost per million tokens?\n\nBased on Meta’s API pricing, Muse Ember costs approximately 10 cents per million input tokens and 50 cents per million output tokens.\n\n### What is Muse Ember’s context window size?\n\nMuse Ember supports a 1 million token context window and can generate up to 512,000 tokens in a single output.\n\n### Is Muse Ember cheaper than Claude Haiku or GPT 6 Luna?\n\nMeta’s published cost-versus-intelligence comparison, using data from artificial analysis, shows Muse Ember achieving higher measured intelligence at a lower cost per completed task than both Claude Haiku 4.5 and GPT 6 Luna.\n\n### What tasks is Muse Ember best suited for given its pricing?\n\nIt performs strongest on tool calls, coding, data extraction, spreadsheet cleanup, diagram interpretation, and long document reading, the kinds of repetitive, structured tasks where low per-token cost compounds into significant savings for agents.\n\n### Does Muse Ember have any weaknesses despite its low price?\n\nYes. Testing found weaker performance on multilingual and creative generation tasks, including literal rather than culturally grounded translations and some formatting issues like low-contrast fonts in generated documents.", "url": "https://wpnews.pro/news/meta-muse-ember-pricing-what-it-costs-per-million-tokens", "canonical_source": "https://www.mindstudio.ai/blog/meta-muse-ember-pricing/", "published_at": "2026-11-10 00:00:00+00:00", "updated_at": "2026-11-10 10:25:18.948821+00:00", "lang": "en", "topics": ["large-language-models", "ai-products", "ai-agents", "artificial-intelligence"], "entities": ["Meta", "Muse Ember", "Claude Haiku 4.5", "GPT 6 Luna", "artificial analysis"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/meta-muse-ember-pricing-what-it-costs-per-million-tokens", "markdown": "https://wpnews.pro/news/meta-muse-ember-pricing-what-it-costs-per-million-tokens.md", "text": "https://wpnews.pro/news/meta-muse-ember-pricing-what-it-costs-per-million-tokens.txt", "jsonld": "https://wpnews.pro/news/meta-muse-ember-pricing-what-it-costs-per-million-tokens.jsonld"}}