Alpha

WorkBench is an AI Workbench built to bring the most powerful models from across the web into a seamless user experience.

WorkBench

MCP HTTP

Arxiv

Perplexity Search

Web Page Scraper

Youtube Transcriber

Image Search

Interactive Canvas

Diagram

Medical

Financial News

Cooking

Academic Research

Chart

Math

Wikipedia Article Extraction

Firecrawl Web Page Reader

Presentation Generator (Text to PPTX)

CSV Generator

Grok Image Generation

Generate PDF

Image Generation

Stable Diffusion - Ultra

Google Calendar

Google Calendar - Create Event

Google Calendar - Update Event

Google Calendar - Delete Event

Google Calendar - Search Events

Google Drive - Search Files

Google Drive - Create Blank Doc

Google Drive - Delete File/Folder

Google Drive - Add Content to Doc

Gmail - Search Emails

Gmail - Modify Email Labels

Gmail - Get Email Details

Grok Image Generator

Todo List

Research Plan

Use this tool to create and manage a structured task list for your current research session. This helps track progress, organize complex investigations, and demonstrate thoroughness.

### When to Use This Tool

Use proactively for:
1. Complex multi-step research (3+ distinct phases)
2. Non-trivial investigations requiring careful planning
3. User explicitly requests research todo list
4. User provides multiple research topics (numbered/comma-separated)
5. After receiving new research instructions - capture requirements as todos (use merge=false to add new ones)
6. After completing research phases - mark complete with merge=true and add follow-ups
7. When starting new research areas - mark as in_progress (ideally only one at a time)

### When NOT to Use

Skip for:
1. Single, straightforward questions
2. Trivial lookups with no organizational benefit
3. Research completable in < 3 trivial steps
4. Purely conversational/clarification requests
5. Don't add a task to verify findings unless asked, or you'll overfocus on validation

### Examples

<example>
  User: Research the impact of climate change on Arctic wildlife
  Assistant: *Creates todo list:*
  1. Analyze temperature trends in Arctic regions
  2. Identify key Arctic species and habitats
  3. Review scientific studies on wildlife impacts
  4. Synthesize findings and correlations
<reasoning>
  Multi-phase research with dependencies requiring systematic investigation.
</reasoning>
</example>

<example>
  User: Compare healthcare systems across Nordic countries
  Assistant: *Creates todo list with specific items for each country's system, funding models, outcomes, and comparative analysis*

<reasoning>
  Complex comparative research requiring systematic tracking across multiple systems.
</reasoning>
</example>

<example>
  User: Investigate renewable energy adoption, economic impacts, policy frameworks, and future projections.
  Assistant: *Creates todo list breaking down each research area into specific investigation tasks*

<reasoning>
  Multiple complex research areas provided as list requiring organized investigation management.
</reasoning>
</example>

<example>
  User: Research why my company's market share is declining - analyze competitors, market trends, customer feedback.
  Assistant: *Creates todo list: 1) Competitor analysis, 2) Market trend research, 3) Customer sentiment analysis, 4) Internal factor review, 5) Strategic recommendations*

<reasoning>
  Business analysis requires multiple research phases across different data sources.
</reasoning>
</example>

### Examples of When NOT to Use the Todo List

<example>
  User: What's the capital of France?
  Assistant: The capital of France is Paris.

<reasoning>
  Single trivial factual lookup completed in one step.
</reasoning>
</example>

<example>
  User: Explain what photosynthesis is.
  Assistant: Photosynthesis is the process by which plants convert sunlight into energy...

<reasoning>
  Informational request with no complex research investigation needed.
</reasoning>
</example>

<example>
  User: Find the current stock price of Apple.
  Assistant: *Looks up current AAPL price* Apple (AAPL) is currently trading at...

<reasoning>
  Single straightforward data lookup with immediate results.
</reasoning>
</example>

<example>
  User: What does GDP stand for?
  Assistant: GDP stands for Gross Domestic Product...

<reasoning>
  Simple definition request with no multi-step research required.
</reasoning>
</example>

### Task States and Management

1. **Research States:**
  - pending: Not yet started
  - in_progress: Currently investigating
  - completed: Research finished successfully
  - cancelled: No longer needed

2. **Research Management:**
  - Update status in real-time
  - Mark complete IMMEDIATELY after finishing each phase
  - Only ONE research area in_progress at a time
  - Complete current investigations before starting new ones

3. **Research Breakdown:**
  - Create specific, actionable investigation items
  - Break complex research into manageable phases
  - Use clear, descriptive research objectives

When in doubt, use this tool. Proactive research management demonstrates thoroughness and ensures complete investigation of requirements.

update_research_plan

Search Web

Search the web using SERP API (Google search engine) for real-time information about any topic. Use this tool when you need up-to-date information that might not be available in your training data, or when you need to verify current facts. The search results will include relevant snippets and URLs from web pages. This is particularly useful for questions about current events, technology updates, or any topic that requires recent information.

research_tool_search_web

Extract Web Page

Retrieves the text content of a web page in markdown format. When in Lightweight Mode, this tool returns an extracted content with only the answer based on the extractInstructions. When in Comprehensive Mode, this tool returns the full content of the web page.

extract_web_page

Read Full Web Page

Retrieves the full text content of a web page in markdown format, this will return the full content of the website that may be extremely long.

read_full_web_page_content

Deep Research

List Email Templates

List all available Stensul email templates and layouts. Can filter by name, type, tags, or library. Returns email IDs, names, types, preview URLs, and module information that can be used to create new emails.

list_stensul_templates

Stensul Email Template Browser

Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost compared to traditional large models, making it suitable for scalable deployments across professional and industrial use cases.  The model excels in domains such as coding, STEM reasoning, and enterprise adaptation. It supports hybrid, on-prem, and in-VPC deployments and is optimized for integration into custom workflows. Mistral Medium 3.1 offers competitive accuracy relative to larger models like Claude Sonnet 3.5/3.7, Llama 4 Maverick, and Command R+, while maintaining broad compatibility across cloud environments.

Mistral: Mistral Medium 3.1

Mill Pond Research's proprietary & secure LLM system

Xilos v1.0 App

Xilos v1.0 Demo

Xilos v1.0 Staging

Grok 4

Qwen 3 32B is the latest generation of large language models in the Qwen series, offering groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support. It uniquely supports seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within a single model. The model excels in human preference alignment, creative writing, role-playing, and multi-turn dialogues, while supporting 100+ languages and dialects.

Qwen3-32b

Kimi K2 is Moonshot AI's state-of-the-art Mixture-of-Experts (MoE) language model with 1 trillion total parameters and 32 billion activated parameters. Designed for agentic intelligence, it excels at tool use, coding, and autonomous problem-solving across diverse domains.

Kimi K2

Llama 3.1 8B on Groq provides low-latency, high-quality responses suitable for real-time conversational interfaces, content filtering systems, and data analysis applications. This model offers a balance of speed and performance with significant cost savings compared to larger models. Technical capabilities include native function calling support, JSON mode for structured output generation, and a 128K token context window for handling large documents.

Llama 3.1 8B Instant

GPT-OSS 120B is OpenAI's flagship open source model, built on a Mixture-of-Experts (MoE) architecture with 20 billion parameters and 128 experts.

GPT OSS 120b

Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. It introduces a hybrid reasoning mode, where the model can choose to deliberate internally with <think>...</think> traces or respond directly, offering flexibility between speed and depth. 

Hermes 4 405B

Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking. The model is designed specifically for “thinking mode,” where internal reasoning traces are separated from final answers.  Compared to earlier Qwen3-30B releases, this version improves performance across logical reasoning, mathematics, science, coding, and multilingual benchmarks. It also demonstrates stronger instruction following, tool use, and alignment with human preferences. With higher reasoning efficiency and extended output budgets, it is best suited for advanced research, competitive problem solving, and agentic applications requiring structured long-context reasoning.

Qwen3 30B A3B Thinking 2507

Grok 4 Fast is xAI's latest multimodal model with SOTA cost-efficiency and a 2M token context window. It comes in two flavors: non-reasoning and reasoning.

Grok 4 Fast

Compound system integrates OpenAI's GPT-OSS 120B and Llama 4 models with external tools like web search and code execution. This allows applications to access real-time data and interact with external environments, providing more accurate and current responses than standalone LLMs. Instead of managing separate tools and APIs, Compound systems offer a unified interface that handles tool integration and orchestration, letting you focus on application logic rather than infrastructure complexity.

Login to WorkBench