MODELLVERZEICHNIS

MODELL-VERZEICHNIS

Muse Spark

Muse Spark

Meta's Muse Spark is a closed, cost-efficient family for coding and agentic work. Muse Spark 1.3 ties the frontier on general intelligence at the lowest cost per task, on a rapid release cadence.

AKTUELLE MODELL-MOMENTAUFNAHME

Provider: Meta
Current anchor: Muse Spark 1.3 (latest)
Lifecycle: Current
Weights: Closed
Reviewed: 3 September 2026

Muse Spark models in context

Muse Spark is Meta's closed family for coding and agentic work, accessed through Muse Code and the Meta Model API. For enterprise teams the family name is only the start: the pinned model ID, surface, tool implementation, and rapid release cadence all affect behaviour. Beam runs your agents on whichever model fits the job, with governance and orchestration on top.

Meta's current anchors:

  • Muse Spark 1.3 is the latest model, generally available, scoring 61 on the Artificial Analysis Intelligence Index, level with GPT-5.6 Sol and Grok 4.6.

  • Muse Spark 1.2 and 1.1 are the prior releases in a fast cadence (Intelligence Index 57 and 53).

  • A higher Muse Spark 1.3 (max) variant scores 62 in limited partner preview.

Muse Spark's distinguishing trait is cost efficiency: it reaches a top-tier intelligence level at the lowest cost per task of the leading models. With four releases in five months, pin the exact model ID and recheck the lineup before release.

Where Muse Spark is distinctive

Muse Spark stands out on cost-efficient, tool-heavy agentic and coding work. It ties the frontier on general intelligence while costing the least per task, and it is fast, generating around 235 tokens per second.

Strengths to test

  • High-volume agentic workloads where cost per task is the binding constraint.

  • Multi-step, tool-calling agents, where Meta reports fewer tool calls and tokens per task.

  • Coding and terminal work, where it leads its tier (Terminal-Bench 2.1 88.8, DeepSWE v1.1 75.4).

Trade-offs and failure modes

  • It is a closed model; self-hosted weights are not an option.

  • The hardest long-horizon reasoning still favours frontier models like Claude Opus 5 or Fable 5.1.

  • A four-release-in-five-months cadence means behaviour can shift between versions; pin the model ID.

  • Availability and feature parity vary across Meta surfaces; confirm on the intended route.

Build a task-specific test set that measures cost per completed task, tool-call efficiency, and coding success separately, on the exact model ID intended for production.

Deployment and enterprise decision notes

Meta documents access through Muse Code and the Meta Model API. Verify the model ID, release stage, region, feature availability, quota, and data controls on that exact surface before committing.

Best fit

A strong candidate for high-volume, cost-sensitive agentic work, multi-step tool-calling, and coding or terminal agents, where near-frontier quality at the lowest cost per task matters most.

Not the best fit

The hardest long-horizon reasoning and highest-stakes decisions are better routed to a frontier model. Muse Spark is also unsuitable when self-hosted weights are a hard requirement, since it is closed.

Data and governance

Record the pinned model ID, surface, region, retention and training settings, tool permissions, and evaluation results. Re-validate after each fast release, and treat tool use and code execution as separate security boundaries.

Official sources

Beam AI support status

Under evaluation. This page is a model-selection reference, not confirmation of a Beam integration, benchmark result, data-residency promise, or production recommendation. Validate the exact provider surface and model version in the intended workflow before release.

Anwendungsfall 1

High-volume tool-calling agent

Run a multi-step agent that calls tools across your systems where cost per task is the constraint. Evaluate tool-call efficiency, recovery after a failed call, and total operating cost at volume rather than a demo sample.

Anwendungsfall 2

Coding and terminal workflow

Plan, implement, and run changes in a repository or terminal within explicit permission boundaries. Require automated checks and human review, and compare successful merged outcomes against a frontier model on the hardest tasks.

Anwendungsfall 3

Cost-sensitive back-office automation

Automate high-volume, rule-heavy back-office steps where the inference bill, not the occasional retry, is the limit. Track accuracy at scale, escalation quality, and cost per completed task.

Anwendungsfall 4

Multi-agent orchestration

Coordinate several agents on a larger job, using Muse Spark for the cost-sensitive, tool-heavy legs and a frontier model for the hardest. Validate shared context, handoffs, and audit trails before production.

Verwandte LLMs

Kuratierte Alternativen zum Vergleich, bevor Sie sich für eine Modellfamilie entscheiden.

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Gemini

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Claude

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Claude

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Llama

Die Llama-4-Familie von Meta bietet native multimodale Open-Weight-Modelle. Scout setzt auf besonders lange Kontexte und effiziente Bereitstellung, während Maverick auf eine stärkere allgemeine multimodale Leistungsfähigkeit abzielt.

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Heute starten

Bauen Sie KI-Agenten mit dem passenden Modellen für Beam AI

Erfahren Sie, wie Beam gesteuerte KI-Workflows über die Modellfamilie hinweg orchestrieren kann, die Ihren Anforderungen entspricht.

Heute starten

Bauen Sie KI-Agenten mit dem passenden Modellen für Beam AI

Erfahren Sie, wie Beam gesteuerte KI-Workflows über die Modellfamilie hinweg orchestrieren kann, die Ihren Anforderungen entspricht.

Heute starten

Bauen Sie KI-Agenten mit dem passenden Modellen für Beam AI

Erfahren Sie, wie Beam gesteuerte KI-Workflows über die Modellfamilie hinweg orchestrieren kann, die Ihren Anforderungen entspricht.

Häufig gestellte Fragen

Häufig gestellte Fragen

Fragen zu Modellauswahl, Deployment, Governance und Support von Beam AI beantwortet.

What is the current Muse Spark lineup?

Meta currently offers Muse Spark 1.3 (latest) in Muse Code and the Meta Model API, following 1.2 and 1.1, with a higher 'max' variant in limited partner preview. Model names and lifecycle labels change quickly (four releases in five months), so record the exact model ID or release used in testing and confirm it against the linked provider documentation before production.

How can an enterprise access or deploy Muse Spark?

Muse Spark is available through Muse Code and the Meta Model API. Availability, regional controls, service terms, and feature parity can vary by route. Evaluate the exact provider surface that will carry production traffic, rather than assuming every option behaves identically.

What workloads are a strong fit for Muse Spark?

Muse Spark is a strong shortlist candidate for high-volume, cost-sensitive agentic work, multi-step tool-calling, and coding or terminal agents. Treat that as a shortlist hypothesis, not a universal ranking. Use representative prompts, tools, and failure cases to compare quality, latency, reliability, and total operating cost.

What should security and governance teams review for Muse Spark?

Review the pinned model ID, surface, region, retention and training settings, tool permissions, and the fast release cadence. Document the data path, model version, region, human-review points, and incident fallback before the workflow is approved.

Does this page confirm Beam support for Muse Spark?

No. Beam support is marked Under evaluation because no approved integration or production-support evidence is attached to this CMS record. The page can guide discovery and evaluation, but the implementation owner must verify access, controls, tool behaviour, and operational fit before making a customer commitment.