Introducing Macaron-V1<br>Today, we are releasing Macaron-V1, the first official release of our agent model following Macaron-V1-Preview. V1 is available in two variants:

Macaron-V1-Venti: A 748B-parameter flagship model, consisting of a 744B base model and four 1B-parameter LoRA specialists. It is the first model to be post-trained on GLM-5.2.

Macaron-V1-Tall: A 35B-parameter model post-trained from Qwen 3.6 and designed for local deployment.

In Macaron-V1-Preview, we introduced the Mixture-of-LoRA (MoL) architecture, with separate LoRA specialists for agent and coding tasks. V1 retains that core design while advancing it across five areas:

A stronger base model: GLM-5.2 replaces GLM-5.1.

More data and broader benchmarking: We improve what already worked while making the results more transparent, reproducible, and measurable.

A better harness: V1 introduces a smarter and cleaner agent harness for both Generative UI and coding workflows.

Long-context post-training infrastructure: LongStraw enables training at context lengths of up to 2 million tokens.

Broader frontier-model evaluation: We evaluate V1 against a wider range of leading general-purpose models.

Macaron-V1 reflects our bet on two ideas: adaptation and collaboration . We believe the next era of AI will be defined less by scaling a single monolithic model and more by two complementary capabilities. The first is post-training through recursive self-improvement, where models repeatedly generate experience, learn from it, and improve their own behavior. The second is multi-agent collaboration, where specialized models and agents compose on top of shared infrastructure. V1 is designed as our milestone toward this future.

Updates and Evaluation

Preview was scored against a handful of public benchmarks. For V1 we expanded coverage in three directions.

Personal Intelligence Benchmarks

Personal Intelligence is a capability Macaron-V1 deeply cares about: whether the model genuinely helps people in their daily lives, and grows more helpful the longer it works with them. One of the biggest challenges on that path is how to evaluate a model's Personal Intelligence in the first place.

Unlike general-purpose tasks, Personal Intelligence tasks often require task-specific rubrics and benchmarks. To address this, we built two of our own:

Macaron ChatBench: Chat is one of an agent’s most fundamental capabilities, and we take it seriously. Good conversation in an agent setting is not about elegant phrasing, emojis, or performative empathy. It is about remaining honest with the user across long and complex agent contexts: knowing what to say, what not to say, and when to proactively acknowledge an error instead of smoothly continuing as though nothing happened. ChatBench captures these qualities in its evaluation rubric.

Macaron LivingBench: A long-horizon benchmark designed to evaluate an agent across weeks of interaction. As promised, the benchmark itself continues to evolve. Since the Preview release, it has expanded to cover different scenarios of daily life and users with different cultural contexts.

Broader Evaluation

Beyond our Personal Intelligence benchmarks, we evaluate V1 across the broader capabilities that a general agent must maintain: personal-life agentic behavior with VitaBench and PinchBench; coding and terminal use with SWE-Verified, DeepSWE, SWE Atlas QnA and TerminalBench 2.1; and Generative UI with UI4A-Bench.

Because V1 is intended to compete with the strongest general-purpose models, we run the same evaluation suite on frontier baselines, including Opus 4.8, GPT-5.5, and Gemini 3.1 Pro.

Venti matches or outperforms the frontier baselines across most tracks while remaining open and self-hostable. Coding is where we currently sit close to, rather than ahead of, the frontier, and it is a direction we will continue to push. Personal intelligence, where Macaron is uniquely positioned, remains a key direction we are iterating on release after release.

Model and Harness Co-design

V1’s harness is not an afterthought. It is co-designed and trained with the model. Three components are particularly worth highlighting.

UI4A: Generative UI

UI4A is our code-native Generative UI solution, combining native HTML flexibility with direct imports of libraries and components from NPM registries or arbitrary URLs to let models instantly deliver rich, interactive content.

UI4A is designed to work even with models that have not been fine-tuned specifically for it, which makes it a general-purpose interface layer rather than Macaron-specific capability. However, we did train Macaron V1 to align its interfaces with human preferences.

REPL Harness

The REPL harness gives the model a persistent Python namespace built around a ToolProxy pattern. Instead of round-tripping every intermediate result through the model, it writes code that composes dependent steps directly: save_tool packages a working routine into a reusable tool, and promote_tool...