When was Muse Spark 1.2 released?

Meta released Muse Spark 1.2 on 5 Aug 2026. It is available through the Opper gateway.

Does Muse Spark 1.2 support zero data retention?

With abuse monitoring, yes. On Meta through the Opper gateway, prompts and outputs are not used for training and are kept only for abuse monitoring.

Where is Muse Spark 1.2 hosted?

Muse Spark 1.2 is available on multi-region hosting through the Opper gateway. See the per-route table for details.

Is Muse Spark 1.2 trained on prompts and outputs?

No. Every hosting route for Muse Spark 1.2 is configured so customer prompts and outputs are not used to train models by default.

Is a GDPR DPA available for Muse Spark 1.2?

Yes. A GDPR Data Processing Agreement is available on at least one route serving Muse Spark 1.2. Contact hello@opper.ai to receive it.

Muse Spark 1.2

by Meta

Released 607 days after Llama 3.3 70B · All Meta releases

Muse Spark 1.2 arrived on August 5, 2026 as the coding-focused flagship of Meta Superintelligence Labs' Muse family, following the original Spark in April and the 1.1 refresh in July. The model keeps the line's million-token context window and multimodal input across text, images, audio, and video, while sharpening first-attempt coding accuracy and long multi-step reasoning. Debugging sprawling codebases, validating changes that touch thousands of files, and orchestrating parallel subagents are the workloads it was built for, and it also powers Muse Code, Meta's terminal coding agent. Tool use, MCP integrations, and persistent background agents make it a strong pick for deep agentic software engineering pipelines.

Call Muse Spark 1.2 on Opper with the OpenAI SDK. Sign up without a credit card. Get started

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Key info

Input

Output

Features

Context window

Max output

—

Input price

$1.25 /1M

Output price

$4.25 /1M

Released

5 Aug 2026

Zero data retention with abuse monitoring
No training by default
GDPR DPA available

Available routes

Muse Spark 1.2 runs on 1 route through the Opper gateway. Compare residency, zero data retention and training posture at a glance, with full data-handling detail per route below.

Provider	Region	Zero data retention	Training	Input	Output
Meta	Multi		No	$1.25	$4.25

Data handling per route

Each route hosting Muse Spark 1.2 has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.

Meta — Multi-region

Zero data retention with abuse monitoring: prompts and outputs are kept for abuse monitoring only, for a window the provider does not state, and are not used for training. No training on customer data. GLOBAL; SCCs; DPA available.

Zero data retention: With abuse monitoring, window not stated.
Training: No training on customer data.
Logging: Abuse monitoring
Abuse monitoring: On by default, holds flagged content, a person can view samples
GDPR DPA: DPA available
Transfer mechanism: SCCs

Benchmarks

Independent benchmark scores — composite indices for reasoning, coding, and math, plus individual eval scores where available.

Global rank#36 of 643 LLMs

TierStrong

Output speed270 tok/s

First token12.22s

Intelligence Index39.8

Coding Index72.2

Reasoning & knowledge

GPQA Diamond

90%

Humanity's Last Exam

46%

Long-context reasoning

79%

Coding

SciCode

57%

See full leaderboard →Benchmarks via Artificial Analysis · View on AA

Get started

Call Muse Spark 1.2 through the Opper gateway with one API key. Opper is drop-in compatible with the OpenAI, Anthropic and Google AI SDKs.

Muse Spark 1.2 is a premium model on Opper. Sign up needs no credit card: you get an API key straight away and the free models work in the playground and the API. Add a card to use premium models, pay-as-you-go with no minimum.

Set it up with your agent

Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up Opper for you.

Or call it directly

1import OpenAI from "openai";
2
3const client = new OpenAI({
4  apiKey: process.env.OPPER_API_KEY,
5  baseURL: "https://api.opper.ai/v3/compat",
6});
7
8const completion = await client.chat.completions.create({
9  model: "meta/muse-spark-1.2",
10  messages: [{ role: "user", content: "Hello" }],
11});
12console.log(completion.choices[0].message.content);