# Three New Models Join SageMaker JumpStart: LocateAnything, Qwen-AgentWorld, and Qwen3.5!

Hey, it's me! Today three new models joined SageMaker JumpStart all at once.

## What was announced?

According to AWS's What's New, three new foundation models were added to Amazon SageMaker JumpStart: NVIDIA's "LocateAnything-3B," Qwen's "Qwen-AgentWorld-35B-A3B," and Qwen's "Qwen3.5-122B-A10B." Each one specializes in something different.

## Why it matters

Instead of one generalist model trying to do everything, these three are each specialized: visual object localization, agent environment simulation, and large-scale multimodal reasoning. That's great news if you want to pick a model that fits your specific enterprise use case instead of settling for a jack-of-all-trades.

## What changes

You can deploy any of them right away from the SageMaker JumpStart model catalog or the Python SDK. Here's what each one brings:

- **LocateAnything-3B** (NVIDIA): fast, high-quality visual grounding — locating objects from natural language instructions
- **Qwen-AgentWorld-35B-A3B** (Qwen): a "language world model" that predicts agent behavior and environment states
- **Qwen3.5-122B-A10B** (Qwen): a large sparse MoE model built for high-performance multimodal reasoning

## Dive Deep

Let's go a bit deeper on each one.

**LocateAnything-3B** uses a "Parallel Box Decoding (PBD)" framework that decodes bounding boxes and points as atomic units in a single step, preserving geometric coherence while unlocking substantial parallelism. It's built for precise object localization, dense detection, and point-based localization across enterprise intelligence and physical AI applications.

**Qwen-AgentWorld-35B-A3B** is the first language world model to cover all seven interaction domains — tool calling, search, terminal, software engineering, Android, web, and OS interaction — in a single model. It predicts the next environment state given an agent's action and interaction history via long chain-of-thought reasoning, trained on over 10 million real-world interaction trajectories.

**Qwen3.5-122B-A10B** has 122B total parameters but activates only 10B per token, using a sparse MoE design with 256 experts and a 262K context window — aiming for strong reasoning performance while staying efficient.

## Wrap-up

- LocateAnything-3B, Qwen-AgentWorld-35B-A3B, and Qwen3.5-122B-A10B all joined Amazon SageMaker JumpStart
- LocateAnything-3B: a lightweight model specialized in visual object localization
- Qwen-AgentWorld-35B-A3B: a language world model simulating agent environments across seven domains
- Qwen3.5-122B-A10B: a sparse MoE model with 256 experts and a 262K context window

If you need to pick a model for visual recognition, agent simulation, or large-scale multimodal reasoning, this update is for you!