Practical tools for AI engineers, ML researchers, and LLM developers. Token counting, model comparison, cost estimation, and more. No signup, no tracking — your prompts and data never leave your browser.
Paste any text and instantly see the token count for GPT-4, Claude, Llama 3, Mistral, and Gemini. Calculate API costs before you make the call. Supports all major models and pricing tiers.
Compare large language models side by side — context windows, pricing, speed benchmarks, capabilities, and real-world performance. Make informed decisions about which model to use for your use case.
Build, test, and version prompt templates with variables. Export for LangChain, OpenAI, Anthropic, and other frameworks. Coming soon.
Calculate cosine similarity between text pairs, visualize embedding spaces, and debug your RAG pipeline. Coming soon.
We run JobsByCulture — the culture-first job board for AI and tech. We profile 118 companies including Anthropic, OpenAI, Mistral, and DeepMind. These tools are built for the same community: AI engineers who care about doing great work at great companies.
Your prompts, tokens, and data never leave your browser. We don't log, store, or train on anything you paste.
Not generic tools with AI bolted on. Purpose-built for the workflows AI engineers use every day.
Model pricing, benchmarks, and capabilities updated regularly. No stale data from 2023.
Browse open jobs at 118 AI and tech companies — or take the culture quiz to find your match.
AI engineering is not traditional software engineering. The daily workflow involves managing token budgets across multiple LLM providers, optimizing prompt costs that can scale from pennies to thousands of dollars per hour, and making model selection decisions that directly impact both quality and latency. These are problems that generic developer tools were never designed to solve.
Token management alone is a discipline unto itself. Different models use different tokenizers — the same 1,000-word document might produce 1,200 tokens on GPT-4o but 1,350 on Claude or 1,100 on Llama 3. When you're building production systems that process millions of requests, those differences compound into real cost differences. Having a reliable, multi-model token counter that runs locally means you can estimate costs accurately before committing to an API call, without sending proprietary prompts to yet another third-party service.
Model selection is equally nuanced. The LLM landscape changes monthly — new models launch, pricing shifts, benchmarks are updated, and context windows expand. A model comparison tool that stays current saves hours of research when you need to decide between GPT-4o, Claude Opus, Gemini 2.5 Pro, or an open-weight alternative like Llama 3 or Mistral Large. The right choice depends on your specific use case: cost sensitivity, latency requirements, context length needs, and whether you need features like vision, tool use, or structured output.
Privacy matters more here than in any other engineering domain. When you paste a prompt into an online tool, you might be sharing proprietary system prompts, customer data embedded in few-shot examples, or trade-secret reasoning chains. Every tool on this page runs entirely in your browser — no server calls, no logging, no data collection. Your prompts and data stay on your machine.
We built these tools because we work with the AI engineering community every day. JobsByCulture profiles 118 AI and tech companies — from frontier labs like Anthropic and OpenAI to fast-growing startups building with LLMs. We understand what AI engineers need because we talk to them constantly, and we built the tools we wished existed.