Muse Spark Open Meta
Open-weights version of Muse Spark; 816B mixture-of-experts with 42B active, 1M context, text, image, video and audio input.
- 1M context
- Open weights
Every notable model since ChatGPT, newest first: what shipped, when, and what it changed.
Open-weights version of Muse Spark; 816B mixture-of-experts with 42B active, 1M context, text, image, video and audio input.
New 120B (12B active) vision-language model in the Nemotron 3.5 family; up to 1M context, image and video input, open datasets and recipes.
New local tier of the Nemotron 3.5 family: 74B total, 9B active, 2-bit native quantisation to fit in 24GB.
Google's latest Gemini model; 1M unified input and output limit, 64K default output (up to 1M with long decode), low, medium and high thinking levels.
New flagship above Muse Spark; 1M context, text, image, video, audio and PDF input; sibling Muse Wonder Full is subscription-only in Muse Code.
Succeeds Haiku 4.5; 1M context, 128K max output, adaptive thinking.
Succeeds Mistral Large 3 with a 1T-parameter (52B active) multimodal MoE, a 1M context and built-in reasoning.
Succeeds GPT-6 Sol at the same price; cached input halved to $0.10/M.
Succeeds Sonnet 5 at the same price; 1M context, 128K max output.
Succeeds Grok 4.6 with a new, larger base model, a 500K context and four reasoning effort levels, at the same price.
Succeeds V4-Flash (retired) on a new encoder-decoder architecture: 552B total, 8B/16B active, 384K max output, thinking on by default, native vision. Refreshed as V4.1-Flash-1017 on 17 Oct 2026.
OpenAI's new flagship ("our most capable model"), above GPT-5.6.
Near-frontier reasoning at Flash speed, with an adjustable effort level for tool calling.
Succeeds Muse Spark 1.2; max reasoning mode, better long-horizon agent work, about 20% fewer tool calls and 25% fewer tokens in coding.
Went to a million-token context and made reasoning the default rather than an option.
Retrained for long-running agentic and visual work. Superseded by Grok 4.7 (21 Sep 2026).
Microsoft’s first reasoning model, trained without distilling from third parties.
Alibaba's new flagship after the Qwen3 line: 2.4T total / 95B active MoE, 1M context, 131K max output, thinking mode, image and video input.
Set a new bar on coding and knowledge work at roughly half the price of the tier above it.
A 2.8-trillion-parameter model that added native video understanding to the Kimi line.
Split the flagship into three tiers and added a mode that runs parallel agent workstreams.
Added a public Meta Model API and a thinking mode, with a million-token context. Superseded by Muse Spark 1.2 and then Muse Spark 1.3 (2 Sep 2026).
Brought near-Opus results down to Sonnet pricing, with a million-token context.
Turned the workhorse line toward long-horizon agentic tasks rather than raw benchmark scores.
Brought a million-token context and sparse attention to the open-weight world.
A large agentic jump for long-running coding and research work without the usual latency cost.
Meta dropped open weights for its flagship for a closed model from the new Superintelligence Labs.
Brought native audio input and agentic function calling to Google’s open weights.
More than doubled its ARC-AGI-2 reasoning score against Gemini 3 Pro.
Three times faster than the previous generation while beating 2.5 Pro on coding.
Hybrid latent mixture-of-experts; Nano gained four times the throughput and a 1M context.
Tripled its score on the ARC-AGI-2 reasoning benchmark and halved chart and interface vision errors.
Return to mixture-of-experts at 675B total parameters, released under Apache 2.0.
Added three levels of extended reasoning plus a built-in code interpreter.
Added automatic context summarisation, so long conversations stop hitting a wall.
Folded multimodality, thinking and tool use into a single flagship with agentic coding.
Made thinking: it spends effort in proportion to how hard the question is.
Sustained two to three hundred sequential tool calls without losing the thread.
Matched the previous Sonnet on coding at a third of the cost and twice the speed.
A 3B reasoning model for on-device use, with an eight-times smaller KV cache.
Extended context to 200K and sharpened real-world coding performance.
Held focus on autonomous work for 30 hours and more, a step change in agent endurance.
Microsoft’s first in-house foundation models, ending sole reliance on OpenAI.
ByteDance’s first broadly open model, with a 512K context and an adjustable thinking budget.
Merged the chat and reasoning lines into one model with a dial for how long it thinks.
OpenAI’s first open-weight release since GPT-2, Apache-licensed and runnable on one GPU.
A coding and agentic-research refresh, taking SWE-bench Verified to 74.5%.
MIT-licensed 355B agentic model, among the most permissive frontier-scale releases.
A trillion-parameter open mixture-of-experts model built specifically for agentic tool use.
Native tool use, with reinforcement learning applied at pretraining scale.
First open large-scale hybrid-attention reasoning model, at a third of R1’s compute.
Added tool use during extended thinking, plus memory files that persist across sessions.
Hybrid thinking and non-thinking modes in one model, trained across 119 languages.
First reasoning models that agentically use every tool (browsing, code, files) and reason over images.
Took context to a million tokens and posted a large coding gain over GPT-4o.
First natively multimodal Llama, mixture-of-experts, with a ten-million-token context on Scout.
Google’s first. It reasons through steps before it answers.
Matched GPT-4o on agentic tasks while running on just two GPUs.
First Gemma with vision and a 128K context, covering 140-plus languages.
The largest non-reasoning chat model OpenAI shipped; big factuality gain, short commercial life.
First hybrid model with extended thinking you can switch on; Claude Code shipped alongside it.
Eight times the context, a Think reasoning mode and the DeepSearch agent.
First open-weight reasoning model to match o1, the release that moved markets.
A four-million-token context via lightning attention, far beyond anything else open at the time.
Reached GPT-4o-class benchmarks on a reported training budget under six million dollars.
Added native image and audio output plus built-in tool use, aimed squarely at agents.
Matched the 405B model’s benchmarks at a sixth of the size, through post-training alone.
Replaced Titan with a multimodal family spanning text, image and video.
First frontier model given direct control of a desktop, moving the cursor and clicking.
Added vision at 11B and 90B, plus 1B and 3B models small enough for phones.
A very wide release: 0.5B to 72B, plus dedicated coding and maths models.
The first. It thinks in a hidden chain of thought before answering.
123B parameters and a 128K context, closing much of the gap to GPT-4o.
First open-weight model Meta claimed was competitive with closed frontier models.
Replaced GPT-3.5 Turbo as the cheap default, at close to GPT-4o quality.
Its 27B model matched systems twice its size and ran on a single host.
A mid-tier model that beat the previous generation’s largest model on most benchmarks.
An open family built to generate synthetic training data for other people’s models.
Added grouped-query attention across all sizes and training in 27 more languages.
First natively multimodal model: one network for text, audio and vision, with near-instant voice.
A 236B mixture-of-experts model trained at a fraction of Western cost, and priced accordingly.
Showed a small model trained on carefully curated data could punch far above its size.
New tokeniser and architecture at 8B and 70B, with a 400B model still training.
First production-grade hybrid of Mamba state-space layers and transformers.
Built for retrieval-augmented generation at production scale rather than for chat.
Introduced the Haiku, Sonnet and Opus tiers and gave Claude vision for the first time.
Mistral’s first closed flagship, sold through an API rather than given away.
Google’s first open-weight models, built from the same research as Gemini.
Jumped to a million-token context using a mixture-of-experts design, an order-of-magnitude leap.
First open mixture-of-experts model: 47B total, only 13B active per token.
First model pre-trained multimodally from the start rather than bolting vision onto a text model.
Doubled context to 200K, cut hallucination rates and added tool use in beta.
Added vision input and pushed context from 8K to 128K at roughly a third of the price.
A seven-billion-parameter debut that beat 13B rivals, under a permissive Apache licence.
First Llama licensed for commercial use, which made open weights a business option.
First Claude anyone could sign up for, with a 100K context window when rivals offered 8K.
A four-size family with much stronger multilingual, reasoning and coding ability than LaMDA.
Google’s answer to ChatGPT, built on a lightweight LaMDA and later folded into Gemini.
A large jump in accuracy and reliability over GPT-3.5, and the first to pass professional exams at scale.
Anthropic’s first public model, launched the same day as GPT-4.
Meta’s first foundation family,, research-licensed and promptly leaked, seeding the open-model scene.
The first public chat product: GPT-3 tuned with RLHF for conversation rather than raw completion.
Dates are the public announcement date, which sometimes ran ahead of general availability. Model names link to the vendor’s own announcement. Prices, where shown, are per million tokens and carry the date we last checked them.