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Data as of October 15, 2026 · How the score is built

Muse Spark Open

Decision readingMuse Spark Open scores 62.7 out of 100 and ranks #45 of 224. This profile shows 7 source-displayable benchmark rows; its strongest eligible category is Agentic at #27. No comparable first-party API price is published in the catalog.

Released Oct 15, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

62.7/100

field median 49.8#45 of 224 ranked models

Public

#45of 224

Verified —

Price

API rate not published

input median $0.75No comparable first-party hosted token rate

Speed

Not measured

field median 92 tok/sTime to first token not measured

Context

1Mtokens

field median 256,000Maximum output length is tracked separately

Strongest published evidence

Agentic ranks #27. Particularly useful for coding agents, browser research, and computer-use workflows.

Validate before choosing

7 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.

Source-linked · 7 displayable benchmark rows

Follow model changes

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #27 of 127Percentile 79thWeight 22%4 benchmarksVerified
60.2
CodingWeight 20%0 benchmarksNot measured
Not measured
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalRank Not rankedWeight 12%3 benchmarksVerified
83.6
KnowledgeWeight 12%0 benchmarksNot measured
Not measured
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathWeight 5%0 benchmarksNot measured
Not measured

7 of 680 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic4/4 verified
  2. CodingNot measured
  3. ReasoningNot measured
  4. Multimodal3/3 verified
  5. KnowledgeNot measured
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. MathNot measured
Verified sourceProvisionalNot measured

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

Muse Spark Open category percentile values

  • Agentic79th percentile
  • CodingNot eligible
  • ReasoningNot eligible
  • MultimodalNot eligible
  • KnowledgeNot eligible
  • MultilingualNot eligible
  • Instruction followingNot eligible
  • MathNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#27/127
  2. CodingNot ranked
  3. ReasoningNot ranked
  4. MultimodalNot ranked
  5. KnowledgeNot ranked
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Agentic opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Agentic4 rows
Agentic benchmark values, best verified comparison, weight, and source status
JobBenchScore61.8%Versus best verified row

Best verified: Muse Wonder Full · 75.3%

Gap13.5 behindWeight6% ref. weight
MCP AtlasScore89.2%Versus best verified row

Best verified: Muse Spark Open · 89.2%

GapBest verifiedWeight4% ref. weight
HLE w/ toolsHumanity's Last Exam with toolsScore61.0%Versus best verified row

Best verified: Claude Opus 5.5 · 67.7%

Gap6.7 behindWeight3% ref. weight
Toolathlon-VerifiedScore72.8%Versus best verified row

Best verified: Claude Opus 5 · 80.6%

Gap7.8 behindWeight3% ref. weight
Multimodal3 rows
Multimodal benchmark values, best verified comparison, weight, and source status
ZeroBenchScore50.0%Versus best verified row

Best verified: Muse Spark Open · 50.0%

GapBest verifiedWeightDisplay only
BabyVisionScore80.2%Versus best verified row

Best verified: Qwen3.8 Max · 82.0%

Gap1.8 behindWeightDisplay only
SimpleVQAScore73.6%Versus best verified row

Best verified: Qwen3.7 Plus · 81.7%

Gap8.1 behindWeightDisplay only

Bars run 0–100; the dark tick marks the best source-verified value

All 7 rows

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Oct 15, 2026 · you are here

    Muse Spark Open

    Score 62.7 · Price not listed

Base entry

Radar

Muse Spark Open release history

Full release history

Radar confirmed these at the source. Use Muse Spark Open in your work? Explore Radar to follow supported changes and choose your alerts.

Radar

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

Weights model ID
meta-models/Muse-Spark-Open-816B-A42BMeta Muse Spark Open model card
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
text, image, video, audioMeta Muse Spark Open model card
Output modalities
textMeta Muse Spark Open model card
Parameters
Not sourced yet
Availability
Hugging Face · Amazon SageMaker AI · Google Cloud Model Garden · Microsoft Foundry Models · self-hosted inferenceMeta AI Research: Muse Spark Open
Cloud regions
Not tracked yet
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing recordMeta AI Research blog
Self-host
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

Muse Spark Open ranks #45 of 224 on the public leaderboard with a score of 62.66/100. It does not yet have enough sourced coverage for a verified position.

Muse Spark Open is a open weight model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Open weights on Hugging Face in BF16 (meta-models/Muse-Spark-Open-816B-A42B) and NVFP4 (meta-models/Muse-Spark-Open-816B-A42B-NVFP4); the repositories are gated behind a license form. Meta lists hosted API partners Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, and weight downloads through Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Input is text, image, video and audio; output is text. Reasoning effort is low, medium, high or xhigh (default xhigh). Meta cites about 700 GB of VRAM for NVFP4 and 2 TB for BF16.

7 of 680 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Agentic at #27. particularly useful for coding agents, browser research, and computer-use workflows.

Last updated October 15, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

Questions

How does Muse Spark Open perform overall in AI benchmarks?

Muse Spark Open ranks #45 out of 224 models on the public BenchAlign leaderboard, with a score of 62.66/100. Its evidence status is Estimated, and this profile shows 7 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is Muse Spark Open good for agentic tool use and computer tasks?

Muse Spark Open ranks #27 out of 127 eligible models for agentic tool use and computer tasks, with a public category score of 60.2/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is Muse Spark Open good for multimodal and grounded tasks?

Muse Spark Open has source-displayable benchmark coverage for multimodal and grounded tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is Muse Spark Open open source?

Muse Spark Open is an open-weight model from Meta. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Does Muse Spark Open have full benchmark coverage on BenchLM?

No. Muse Spark Open currently has 26 source-displayable rows across 680 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of Muse Spark Open?

Muse Spark Open has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

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Compare Muse Spark Open with every tracked model949 comparisons