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MetaNew version

muse-image-1.0

New version, announced 25 August 2026. Rerun the suite: If image generation sits inside a product flow, run your standing prompt set against this model and keep the outputs as a baseline for the next version.

What Meta says changed

Meta made muse-image-1.0 available to developers on the Meta Model API at $0.01 per image. Meta describes anchored composition for consistency across a series of images and reasoning driven edits.

Meta: Muse Image: generate, edit, and compose images on Meta Model API

announced 2026-08-25 . verified 2026-09-22

This section restates the provider. It adds no judgement the provider did not make, and it contains no measurement of ours.

Entry

Provider
Meta
Models
muse-image-1.0
Change type
New version
Announced
2026-08-25
Verified
2026-09-22

What this kind of change typically touches

  • Generation

    There is no single correct output to match a generation prompt against, so a fixed prompt set and the recorded outputs carry the comparison. Galtea warns against updating ground truth to match model behaviour.

    Galtea

What to do this week

Rerun the suite

If image generation sits inside a product flow, run your standing prompt set against this model and keep the outputs as a baseline for the next version.

If you are on a plan

A tracked provider update triggers a rerun of your suite within 2 business days of the announcement, and you get a written verdict. That is one of the six guarantees on every plan.

See plansRead EV-RERUN

Other Meta changes

MetaNew version

muse-voice-transcribe-1.0

Meta made muse-voice-transcribe-1.0 available on the Meta Model API for streaming speech to text with speaker diarization. Meta lists the price as $3.00 per 1,000 minutes and offers a WebSocket streaming endpoint and an HTTP batch endpoint.

Rerun the suiteIf a transcription step feeds a downstream feature, run your existing audio fixtures through this model and compare the transcripts before switching any pipeline.

Source: Meta, Build with Muse Voice Transcribe on Meta Model API . verified 2026-09-22

MetaNew version

meta-models/Muse-Glimmer-30B

Meta released Muse-Glimmer-30B, a 30 billion parameter open weight model for local agent workloads, under the Apache 2.0 licence with a 128K token default context window. Meta lists quantized builds and support for vLLM, llama.cpp, ExecuTorch, Ollama and LM Studio.

Rerun the suiteIf you self host a Llama 4 model, run your current suite against Muse-Glimmer-30B on the same hardware and record both quality and throughput before planning a move.

Source: Meta, Muse Glimmer: an open-weight model built for local agents . verified 2026-09-22

MetaNew version

muse-spark-1.2, muse-spark-1.2-contributor

Meta released muse-spark-1.2 with a 1M token context window, priced at $1.25 per million input tokens, $0.15 per million cached input tokens and $4.25 per million output tokens. Meta states the model was co-trained with its harness and trained on whole repository generation and other long running tasks.

Rerun the suiteRun your coding and long context cases against muse-spark-1.2 and muse-spark-1.1 in the same pass, and note that the contributor tier is rate limited by tokens in a rolling window rather than by request count.

Source: Meta, Meet Muse Spark 1.2 and Muse Code: a coding model and the agent built to run it . verified 2026-09-22

Questions

How do I know this is accurate?
The entry links to Meta's own page, and the date we read it is on this page. If the provider's page and this page disagree, the provider's page is right and we want to hear about it.
Does this mean my feature broke?
No. It means a thing your feature depends on changed. Whether anything about your output changed is a question only a rerun against your own dataset can answer, and that is what the recommended action is for.
Why is there no score or benchmark here?
Because we publish none. This site restates provider announcements and cites published practitioner guides. Any measurement we make belongs to the customer whose dataset produced it.

Sources

Change-log entries restate provider announcements with sources and dates. Impact notes cite public practitioner reports. The site publishes no measurements of its own on any model.

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