Getting an AI agent to start a task is easy. Getting it to finish one properly is a different story. Most agents fall apart somewhere in the middle. A tool returns unexpected output, the model misreads it, and everything that follows builds on that mistake. By step thirty you are looking at something that has completely lost track of what it was supposed to do.
The five AI models here were built with that specific problem in mind. They handle complex multi-step tasks, real browser control, deep research and coding workflows. All open source & self hostable.
Table of contents
1. MiroThinker 1.7
Most research agents break somewhere around step twenty. The tool returns something unexpected, the model misreads it, and the whole chain falls apart. MiroThinker was built specifically for the tasks where that usually happens.
It handles up to 300 tool calls in a single task while keeping the reasoning chain intact. That is not a small number. Most agents are not designed to stay coherent across that many sequential decisions. MiroThinker is, and the architecture is built around verifying each step rather than just generating output and hoping it holds together.
It comes in two sizes. The 30B mini version is the accessible entry point. The 235B full version is for serious research workloads with the compute to match. Both support a 256K context window and both cap at 300 tool calls.
On benchmarks it hits 74.0% on BrowseComp, 82.7% on GAIA-Val-165 and 42.9% on HLE-Text. It achieves top performance among open source models on BrowseComp-ZH. Apache 2.0 licensed. Both sizes on HuggingFace. We have a full breakdown of MiroThinker 1.7 here. If you’re interested, here is the complete breakdown of MiroThinker 1.7
Best for
- Deep research tasks requiring hundreds of sequential steps
- Long document analysis and cross referencing
- Workflows where step by step verification matters
Limitations
- 235B full version needs serious multi GPU setup
- 30B mini is more accessible but still needs capable hardware
- Technical report not yet released so some claims are hard to independently verify
2. MolmoWeb
There is a quiet assumption baked into most web agents. That the best way to understand a webpage is to read its code. Parse the HTML & extract the structure. It works until a website redesigns itself and the whole thing breaks.
MolmoWeb skips that entirely. It looks at a screenshot of the page the same way you would, figures out what is there, and acts on it. Click, type, scroll, switch tabs, navigate. No API needed for any specific website.
It comes in 4B and 8B sizes. The 8B version scores 78.2% on WebVoyager and with parallel rollouts that number jumps to 94.7% pass@4. It also beats GPT-4o based agents that use structured page data, which is genuinely surprising for something this size running fully open. The training data, evaluation tools and pipeline are all released alongside the model. That level of transparency is rare in this space. Apache 2.0 licensed. Weights on HuggingFace for both sizes.
Best for
- Automating browser tasks without custom APIs
- Web research and information retrieval
- Building and fine tuning custom web agents on your own data
Limitations
- Can misread text from screenshots occasionally
- Drag and drop and scrolling within specific elements still challenging
- Not trained on tasks requiring login or financial transactions
3. GLM-5
GLM 5 is built for the kind of engineering work that actually breaks agents, complex systems or long running tasks.
This one has 744B total parameters with 40B active at any time. The gap between those numbers is the point. You get the reasoning depth of a massive model without paying the full inference cost for every token.
The benchmark that stands out is Terminal-Bench 2.0. Real terminal tasks, real system complexity, the kind of work where most models either give up or make things worse. GLM-5 scores 60.7% on the verified version, putting it ahead of DeepSeek-V3.2 at 39.3% and competitive with Claude Opus 4.5 at 59.3%. On SWE-bench Verified it hits 77.8%, ahead of GPT-5.2 at 80.0% and close enough that the gap is not the story anymore.
The tool use numbers are also worth noting. HLE with tools jumps to 50.4% compared to 30.5% without. That delta tells you the model actually knows how to use external tools rather than just tolerating them.
Its MIT licensed. FP8 quantized version also available. Supports vLLM, SGLang, KTransformers and xLLM for local deployment.
Best for
- Complex systems engineering and production debugging
- Long running agentic coding tasks
- Terminal and infrastructure level automation
Limitations
- 744B total parameters means serious infrastructure required
- FP8 quantization helps but multi GPU setup still needed
Related: Open Source LLMs That Rival ChatGPT and Claude
4. Qwen3.5-35B-A3B
35B total parameters. 3B active. That math is the whole story. Qwen3.5 is not just a language model. It handles text, images and video natively in a single model. You do not need separate pipelines for different input types. Give it a screenshot, a document, a video clip, or plain text and it processes all of them the same way.
The 35B-A3B version has more than 3 million downloads on HuggingFace. That is the community voting clearly on which size hits the right balance between capability and deployability.
What makes it genuinely different from the other models on this list is the multimodal agentic angle. On AndroidWorld it scores 71.1%, on ScreenSpot Pro 68.6% for visual grounding, and on OSWorld-Verified 54.5%. These are benchmarks where the model has to look at a screen and actually interact with it.
Context window is 262K natively and extensible to 1 million tokens with YaRN scaling. Thinking mode is on by default and can be switched off for simpler tasks without changing models. It supports 201 languages. Sizes range from 0.8B all the way to 397B depending on your compute budget. Apache 2.0 licensed across the entire family.
Best for
- Multimodal agentic workflows combining text, image and video
- Visual agent tasks like screen interaction and UI navigation
- Multilingual deployments across 201 languages
- Teams wanting one model for multiple input types
Limitations
- Benchmark details for some agentic tasks still incomplete
- YaRN scaling needed for contexts beyond 262K
- Thinking mode can increase latency on simpler tasks
5. Nemotron 3 Super
Nemotron 3 Super supports up to 1 million tokens natively, no scaling tricks needed. Most models on this list cap at 256K or need additional configuration to go beyond that.
Its an open weight agentic model, 120B total parameters with 12B active at any time. The hybrid architecture is worth understanding. It combines Mamba-2 layers with Mixture of Experts and standard attention, which is not a common combination. The Mamba layers handle long sequences efficiently, the MoE handles reasoning, and together they let the model process genuinely massive contexts without the usual performance cliff.
Reasoning is configurable. You can turn it on for complex tasks and off for simpler ones without switching models. That flexibility matters in production where you do not always need full chain of thought for every query. On SWE-bench Verified it scores 60.47% with OpenHands. RULER at 1M tokens hits 91.75%, which is the benchmark that actually matters here. Most models fall apart well before that context length. This one holds up.
It is built for collaborative agents and high volume workloads. IT ticket automation is specifically called out in the model card. The reasoning can be tuned with budget controls, low effort mode for quick tasks, full thinking mode for complex ones.
Note that this model is under NVIDIA’s own open model license rather than Apache 2.0 or MIT. Commercial use is allowed, derivative works are allowed, and NVIDIA does not claim ownership of outputs. Worth reading the license before building on it.
Best for
- High volume agentic workloads requiring long context
- IT automation and collaborative agent systems
- RAG applications with massive document sets
- Production deployments needing configurable reasoning
Limitations
- 8x H100 minimum requirement for the BF16 version
- NVIDIA custom license, not Apache 2.0 or MIT
- HLE scores are lower than some competitors on the list
Also Read: Open-Source AI Text-to-Speech Generators You Can Run Locally for Natural, Human-Like Voice
Different model, different capabilities
Six months ago this list would have been half as long. The open source agentic space is moving fast and these five models are the clearest proof of that. Pick the one that matches your use case. Run it. See what it actually does.
