Meta AI model releases

Meta's successor to Muse Spark 1.2, released Sep 2, 2026. A multimodal reasoning model built for long-running agentic, multi-agent, and coding workflows: it is designed to keep track of information across extended tasks, work through conflicting inputs, and request clarification or confirmation when needed, with an emphasis on concise execution. Accepts text and image input over a 1M-token (1,048,576) context and returns text. Standard-tier API pricing is $1.25 / $4.25 per 1M input/output tokens ($0.15 cached input); as with prior Muse Spark releases a lower-cost muse-spark-1.3-contributor tier is offered in exchange for permission to train future Meta models on prompts and completions. Hosted by Meta and served via OpenRouter (meta/muse-spark-1.3).

Meta's first open-weight agentic model, released Aug 10, 2026 under an Apache 2.0 license — Meta's return to open weights after the closed Muse Spark line. A ~30B-parameter dense causal transformer (about 29.6B parameters across 52 layers) paired with a ~1.8B ViT-G/14 perception encoder, so it accepts interleaved text and images and returns text across more than 100 languages. Carries a 131,072-token context, a 202,048-token vocabulary, and a Jan 4, 2026 knowledge cutoff. Uses grouped-query attention (32 query heads, 2 KV heads) in a local/local/local/global pattern with a 2,048-token sliding window, plus speculative decoding for throughput. Quantized to roughly 4-bit it fits inside a ~24GB memory envelope, running on a single consumer GPU or an Apple-silicon Mac — the model is tuned for on-device agent workloads. Shipped the same week as the closed-weight Muse Spark 1.2 coding flagship; Meta's first agentic model to ship with both open weights and a permissive commercial-use license.

Meta's flagship coding model, released 2026-08-05 and purpose-built for complex software engineering: debugging sprawling codebases, validating changes across thousands of files, and multi-step reasoning, with deep integration into persistent asynchronous background agents. Accepts text, image, video, audio, and PDF input over a 1M-token context and returns text. Standard-tier API pricing is $1.25 / $4.25 per 1M input/output tokens ($0.15 cached input); a new muse-spark-1.2-contributor tier drops to $0.10 / $0.20 in exchange for permission to train future Meta models on your prompts and completions. Shipped alongside Muse Code, a terminal-based coding agent powered by the model. Meta has signaled open weights are coming.

Meta Superintelligence Labs' first paid model, released July 9, 2026 in US public preview on the Meta Model API. A natively multimodal reasoning model (text, image, video, PDF, and audio input; text output) with explicit chain-of-thought reasoning and a 1M-token context window that the model actively compacts. Positioned for agentic work and coding — tool use, multi-step workflow coordination, and long-horizon autonomous tasks — and pitched by Meta as roughly a quarter of the price of comparable Anthropic and OpenAI models at $1.25 in / $4.25 out per Mtok (with $20 in free credits per new API account). Marks the first time Meta has charged businesses for one of its models, a departure from the open-weight Llama strategy. Closed weights, undisclosed size. Vendor and third-party benchmarks place it around the Opus 4.8 / GPT-5.5 tier — strongest as an agent/workflow model and in tool-augmented reasoning, competitive but not dominant on coding and multimodal tasks.

Meta's new frontier model behind Meta AI for U.S. users, identified in reporting as the public release of the former Avocado effort and positioned to compete with Gemini, GPT, and Claude on multimodal assistant tasks.

Historical rumor/codename row retained for provenance. Recent reporting identifies the public productized model as Muse Spark, now tracked separately in the catalog.

Meta's flagship open-weight MoE; highest MMLU among open models at release.

Efficient open-weight MoE designed for very long context on modest hardware.

Meta's announced but unreleased Llama 4 teacher model: a multimodal MoE with 288B active parameters and nearly 2T total parameters. Meta says it was still training when Scout and Maverick shipped and that those released models were distilled from Behemoth.

Late-2024 70B Llama update delivering much of the 405B instruction-following quality at lower serving cost.

First Llama family release with native vision models, alongside smaller edge-oriented 1B and 3B text models.

Meta's first frontier-scale open Llama model, with 405B parameters, 128K context, multilingual support, and tool-use improvements.

First Llama 3 release, with 8B and 70B open models and a stronger tokenizer, data mix, and post-training stack.

Meta's first code-specialized Llama model family, released in base, Python, and instruction-tuned variants.

The release that made capable open-weight models genuinely usable for production.

Meta's first LLaMA, released to researchers; its leak catalyzed the open-weight movement.

A science-focused model whose public demo was withdrawn after just three days over confidently wrong outputs — an early, instructive retraction.