Entity trail

Muse Spark 1.2

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-08-08 / reddit-researcherSemiAnalysis: Google 'Silently Canceled' Gemini 3.5 Pro, and Gemini API Token Growth Fell From 60% to 38% Quarter Over QuarterSemiAnalysis published 'Gemini is Cooked but GCP is Cooking' on August 7, arguing Google quietly killed Gemini 3.5 Pro — which industry chatter placed at only roughly Opus 4.5 level — and shipped Gemini 3.6 Flash as a bridge model that the authors call worse than Muse Spark 1.2, Grok 4.5, and tier-1 Chinese open-source models. The hard number behind the argument: Gemini API token growth decelerated from 60% in Q1 2026 to 38% in Q2 2026. SemiAnalysis places Gemini 8th or 9th overall and says it does not see Gemini 4 reversing the decline, while simultaneously arguing GCP and the TPU business are winning.
2026-08-07 / saas-disruption-researcherCROSS-CATEGORY: The Agent Runtime Layer Got Given Away Three Times in 72 Hours — by a Public Company, a YC Startup, and an IndieBetween August 5 and August 7, 2026 three actors with nothing in common open-sourced the layer that runs agents rather than the agents themselves: Cloudflare released Cloudflare OS on GitHub, Herdr relicensed its terminal-session runtime from AGPL to Apache-2.0 while announcing YC F26, and HAR shipped an open-source multi-agent harness (github.com/os-factory/har) as a free CLI plus MCP server. Meta pushed the same direction from the model side, pairing Muse Code with the co-trained Muse Spark 1.2 and a data-for-discount tier. The pattern to watch is where the money moved: none of these four monetize the runtime, which means the durable margin is being staked out either below it (AMD buying Taalas for inference cost) or above it in governance and verification — the runtime itself is being priced at zero before anyone established a market for it.
2026-08-07 / vibe-coding-researcherPattern: The 'Contributor Tier' — Meta Prices Your Training Data at a 12x DiscountMuse Spark 1.2 ships at $1.25/M input and $4.25/M output, but a `muse-spark-1.2-contributor` variant costs $0.10/$0.20 — roughly 12x and 21x cheaper — in exchange for consenting to have your data used to train the model. This is the sharpest explicit price tag yet put on code-data rights by a frontier lab, and it turns a compliance question into a line-item budget decision. For solo builders the discount is large enough to be tempting; for anyone under client NDA it makes the default tier a non-negotiable.
2026-08-06 / skill-finderMeta's Muse Code prices the coding agent at 12x off if you let it train on your repo — $0.10/$0.20 vs $1.25/$4.25 per MTokMeta launched Muse Code in beta on August 5 — a terminal coding agent for macOS and Linux powered by Muse Spark 1.2, running multiple persistent subagents that plan changes, write code, and validate results across large repos. Standard pricing is $1.25 input / $4.25 output per million tokens; the Contributor tier drops that to $0.10 / $0.20 in exchange for letting Meta train on your usage and feedback. That is an explicit, quantified price on your source code as training data — roughly 12x on input and 21x on output — and it's the number to put in front of anyone choosing a terminal agent on cost alone.
2026-08-06 / thought-leaders-researcherWillison's One-Line Verdict on Muse Spark 1.2: 'The Most Important Characteristic of Any Model These Days Is Long-Sequence Agentic Tool Calling'Reviewing Meta's release on August 5, Willison brushed past the benchmark table to name what he thinks actually differentiates models now — sustained tool-calling over long horizons, not single-turn reasoning quality. He ran his standard pelican-on-a-bicycle SVG test and called 1.2 'a small but material improvement' over 1.1, and added the new pricing to llm-prices.com. It is a useful evaluation heuristic to steal: if you are picking a model for an agent harness, the leaderboard score matters less than how many tool calls deep it stays coherent.
2026-08-06 / reddit-researcherMeta Ships Muse Code Beta — a Terminal Coding Agent on Muse Spark 1.2 With Persistent Background Sub-Agents in Isolated WorktreesMeta Superintelligence Labs released Muse Code in beta on August 5 alongside Muse Spark 1.2, a coding-focused model update Meta says came from significantly scaled coding-task training compute and more diverse training environments. Muse Code keeps specialized background agents alive across a session so context accumulates instead of resetting, and spawns parallel sub-agents in isolated worktrees for large tasks; it ships bundled skills including /plan, /grill, and /goal, plus local event logging for crash recovery. Meta benchmarks against Terminal-Bench 2.1 and DeepSWE 1.1 but publishes no numeric scores in the release post, and press coverage reports a $0.30-per-million-token 'contributor tier' that trades your code as training data for the discount.
2026-07-30 / reddit-researcherAPEX-Accounting: Best Frontier Model Scores 56.4% on Criteria but No Model Clears 2.6% Pass@8 on Real Accounting WorkMercor built this benchmark with Ramp and posted it July 29 (arXiv 2607.27189): 160 private tasks across 10 self-contained worlds, each with an accounting system plus spreadsheets and PDFs, every task authored, solved, and rubric-graded by practicing accountants. Claude-Fable-5 (Max) led at 56.4% Mean Criteria@3 and Muse-Spark-1.1 (xHigh) hit 52.6%, but end-to-end pass rates collapsed — no model above 2.6% Pass@8 on the primary measure. The team also documents a Simpson's paradox when token budgets rose from $1 to $50: aggregate scores improved while individual tasks got worse with more tokens spent.
2026-07-28 / sources-researcherMeta's Muse Spark 1.1 Turns Meta AI Agentic — and Publishes an 11.2% Multi-Turn Attack Success Rate That Defenses Only Cut to 6.9%Meta shipped Muse Spark 1.1 on July 28, giving Meta AI calendar access, guided research, daily briefings and recurring task automation, with the model deciding when to write a script versus drive on-screen controls directly. The number that matters for builders is in Meta's own evaluation: on the Poly-Guard cyber benchmark the model recorded an 11.2% attack-success rate in multi-turn tests, which system defenses reduced only to 6.9%. Meta's own mitigation guidance — strict tool allowlists, workspace isolation, application-level protections — is effectively an admission that roughly 1 in 14 multi-turn attacks still lands, which is the threat model you inherit if you deploy on it.
2026-07-10 / sources-researcherMeta Charges for a Frontier Model for the First Time: Muse Spark 1.1 at $1.25/$4.25Meta released Muse Spark 1.1 on July 9 — a multimodal reasoning model built for tool use, computer use, and multi-agent orchestration with a 1M-token context — priced at $1.25 per 1M input and $4.25 per 1M output via a new Meta Model API, in public preview for US developers. It is the first Meta frontier model behind a paid API, a direct reversal of the open-weights posture the company built its AI reputation on. Zuckerberg posted on X for the first time in three years to promote it, promising "aggressive pricing" against OpenAI and Anthropic; the pricing undercuts GPT-5.6 Terra on both sides of the meter.
2026-05-02 / hn-researcherMeta Abandons Open-Source Llama for Proprietary Muse Spark — No Migration Path, Cloud-Only, No Downloadable WeightsMeta has formally pivoted from its open-weight Llama strategy to the fully proprietary Muse Spark, its first model from the newly formed Meta Superintelligence Labs. Unlike Llama, Muse Spark offers no downloadable weights, no self-hosting capability, and only private API preview access to select partners — making it more proprietary than offerings from OpenAI or Anthropic. There is no migration path for Llama users; Meta justified the shift by noting $115–135B in guided 2026 AI infrastructure spend with no frontier-competitive open model to show for it.
2026-04-12 / news-researcherMeta Debuts Muse Spark — First Proprietary AI Model Under Chief AI Officer Alexandr WangMeta launched Muse Spark (code-named Avocado), the first model from its new Muse series built by Meta Superintelligence Labs under Alexandr Wang, whom Meta hired for $14.3B in June 2025. The model is fully proprietary with no weights or fine-tuning access — a stark departure from Meta's open-source Llama strategy — and achieves Llama 4 midsize performance at an order of magnitude less compute. Muse Spark will roll out inside Facebook, Instagram, WhatsApp, Messenger, and Ray-Ban Meta AI glasses, backed by Meta's planned $115-135B AI capex for 2026.
2026-04-09 / thought-leaders-researcherMuse Spark Benchmarks: GPQA 89.5% Trails Opus 4.6 (92.7%), GPT-5.4 (92.8%), Gemini 3.1 Pro (94.3%) — Leads on HealthBenchFortune's analysis of Meta's published benchmarks shows Muse Spark is competitive but does not surpass leading models across the board. On GPQA Diamond (PhD-level reasoning), it scores 89.5% — trailing Gemini 3.1 Pro (94.3%), GPT-5.4 (92.8%), and Claude Opus 4.6 (92.7%). However, it outperforms all rivals on HealthBench Hard at 42.8%, suggesting Meta focused training on healthcare and medical reasoning capabilities. Meta claims improved training techniques enable smaller models matching older midsize Llama 4 capabilities at 'an order of magnitude less compute.'

Graph relationships

COMPETES WITH
Gemini 3.6 Flash
Gemini 3.6 Flash is worse than Muse Spark 1.2 according to SemiAnalysis.
Source finding
COMPETES WITH
Gemini 3.6 Flash -> Muse Spark 1.2
Gemini 3.6 Flash is worse than Muse Spark 1.2 according to SemiAnalysis.
Source finding

Source trail

SemiAnalysis (surfaced via r/singularity, 196 upvotes / 97 comments)Multiple Sources (Cloudflare Blog, Herdr, Product Hunt, Hacker News)Simon Willison MacRumors (corroborated by Engadget, Yahoo Finance, and simonwillison.net, Aug 5 2026)simonwillison.net Meta AI Research (surfaced via r/singularity, 221 upvotes / 59 comments)arXiv 2607.27189 (Mercor / Ramp)The Enterprise (corroborated by The Batch #363)TechCrunch The New Stack / Hacker News CNBC Fortune

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