6 tracked changes to Meta models in the twelve months to 2026-09-24, newest first. Each one cites Meta's own announcement and carries one recommended action.
These are the ids and API surfaces Meta named in the announcements we track. They are what the entries below are about, and they are not a claim about everything Meta ships.
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.
Summarisation: A transcription step feeds the summariser its input, so the text being summarised changes even though the summarising model did not. Galtea warns against updating ground truth to match model behaviour when the outputs shift.
Extraction: If fields are pulled out of a transcript, a new transcription model changes the input to every fixture. Braintrust tracks every change to a dataset so experiments can pin to specific versions, which is what keeps a before and after run comparable.
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.
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.
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.
Rerun the suiteIf 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.
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.
Agent: An open weight model is pinned by you, so the swap happens when you upgrade rather than when the provider ships. Future AGI writes that agent traces in contemporary frameworks are routinely 15-40 spans deep, so the comparison reads the trace and not the final answer alone.
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.
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.
Agent: Whole repository work and other long running tasks are scored end to end by default. Arize Phoenix writes that end to end evaluation only inspects the two endpoints, the input and the final output, and that reasoning, tool selection and the decision path are where the interesting failures sit.
Generation: Running 1.2 and 1.1 in the same pass is pairwise comparison of two application versions, one of the four evaluator types LangSmith documents.
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.
Meta announced Muse Spark 1.1, a multimodal reasoning model built for agentic tasks, with what it calls major gains in tool and computer use, coding and multimodal understanding. Meta states it has a 1 million token context window and launched a public preview of the Meta Model API for developer access.
Agent: Changes in tool and computer use land in the middle of the trace. Arize Phoenix reconstructs tool_path, the ordered tool calls with their arguments, and tool_io, what each tool was called with and what it returned, and warns that a correct looking answer can come from a broken path.
Rerun the suiteIf you track Meta models, open an account on the Meta Model API and run your existing suite once so later versions have a baseline to be compared against.
Meta put muse-spark-1.1 in public preview on the Meta Model API for United States developers, priced pay as you go at $1.25 per million input tokens and $4.25 per million output tokens. Meta lists end to end agentic workflows, advanced coding and native multimodal perception as the areas of improvement.
Rerun the suiteAdd the published input and output rates to your cost model, and note that the preview is limited to United States developers when you plan access.
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.
Questions about Meta changes
Where do the Meta entries come from?
From Meta's own pages. Every entry links to the changelog, release note, deprecation table or pricing page that carries the announcement. Nothing here is sourced from coverage or from reports by users.
Does DriftCaught test Meta models?
No. This site publishes no measurement of any model. We restate what the provider announced and cite published practitioner guides for what that kind of change typically touches.
What should I do when Meta announces something?
Check whether the model id you run is named. If it is, rerun your suite against your dataset and read the result against the thresholds you agreed. Each entry carries one recommended action: rerun, re-pin, re-prompt or migrate.
Is every Meta announcement here?
No. The log covers changes to the model families we track for customers, inside a twelve-month window, where the announcement is reachable on Meta's own domain. Send us a link to anything missing and we will add it.