Muse Spark 1.3 vs Muse Spark Open
Muse Spark 1.3 scores higher on the Models at Work leaderboard (59 vs 54 out of 100). On independent tests alone, Muse Spark 1.3 is ahead (61 vs 42). Muse Spark Open has no list price we could verify, so the two cannot be compared on cost.
| Muse Spark 1.3 · Meta | Muse Spark Open · Meta | |
|---|---|---|
| Leaderboard rank | #20 | #26 |
| Score (0–100) | 59 | 54 |
| Kind of work | Everyday default | Run it yourself |
| Price per 1M tokens | $0.78 for a typical mix (estimate) | Not verified |
| Reads at once | Not verified | 1M tokens (about 750,000 words) |
| Where you can get it | Gemini Enterprise Agent Platform (Google Cloud), preview | Amazon SageMaker JumpStart |
| Can a company host it itself | Not verified | Yes (open weights) |
| Artificial Analysis Intelligence Index | Not measured | 25 |
| Arena text score | 1494 | Not measured |
| DistressBench (Scale) | 84.88 | Not measured |
| Humanity's Last Exam, Diamond (Scale) | 25.40 | 28.00 |
| Humanity's Sixth Sense (Scale) | 37.40 | 29.60 |
| Best for | Customer-facing chat, Sensitive support conversations, Teams already buying from Meta | Running AI on your own servers or your AWS account, Keeping sensitive data in-house |
Cost of 1,000 jobs
Neither vendor publishes a list price we could verify, so no job cost is worked out. See what the other models cost.
Choose Muse Spark 1.3 for
- Customer-facing chat
- Sensitive support conversations
- Teams already buying from Meta
Meta's earlier everyday model: near the top of Scale's test of replies to people in distress (84.88) but mid-table on hard exams, with no vendor list price on record.
Caveats: No vendor list price was found; the board uses Artificial Analysis's blended figure of $0.78 per million tokens from 10 October. Artificial Analysis dropped it when it rebuilt its index, so it has no current score there. On Google's cloud it is labelled preview, which usually means no service guarantee.
Choose Muse Spark Open for
- Running AI on your own servers or your AWS account
- Keeping sensitive data in-house
Meta's open model for running on your own servers: 25 on Artificial Analysis's index, level with Mistral Large 4, with no per-use price and Meta's own licence to read first.
Caveats: There is no per-use price: you pay for the hardware, and neither Meta nor AWS states what that costs. The licence is Meta's own, with a usage policy, not a standard open licence: have legal read it. It is a very large model that needs data-centre hardware. Meta's own test figures are not counted.
Questions people ask
Which is better, Muse Spark 1.3 or Muse Spark Open?
Muse Spark 1.3 scores higher on the Models at Work leaderboard (59 vs 54 out of 100). On independent tests alone, Muse Spark 1.3 is ahead (61 vs 42). Muse Spark Open has no list price we could verify, so the two cannot be compared on cost.
When should I choose Muse Spark 1.3 over Muse Spark Open?
Choose Muse Spark 1.3 for customer-facing chat, sensitive support conversations, teams already buying from meta. Meta's earlier everyday model: near the top of Scale's test of replies to people in distress (84.88) but mid-table on hard exams, with no vendor list price on record.
When should I choose Muse Spark Open over Muse Spark 1.3?
Choose Muse Spark Open for running ai on your own servers or your aws account, keeping sensitive data in-house. Meta's open model for running on your own servers: 25 on Artificial Analysis's index, level with Mistral Large 4, with no per-use price and Meta's own licence to read first.
Scores come from the Models at Work leaderboard: Score is 0 to 100 and computed, not typed. 40% is independent testing (Artificial Analysis, Arena, Scale, Epoch, Vals and others; each test is scaled against the best score on this board, and tests of real work, such as finished freelance projects or professional tasks, count double the exams). 30% is price: the vendor's list price for a typical mix of reading and writing, on a fixed scale from $0.05 to $60 per million tokens, so a model that costs a fifth as much gains real ground. 15% is what people report after running it, from Signals and linked threads. 15% is fit: how much text it can take in at once and where a business can get it (the products and clouds that carry it), with a small credit for open weights. A missing component drops out and the rest are reweighted. A model with no independent test yet is provisional and ranks below every measured one. Within three points is a tie. Vendor-published figures are labelled and never counted. Prices are vendor list prices where published; a typical mix assumes three parts reading to one part writing.