How do I count tokens for GPT-4 or ChatGPT?

Markdown Studio automatically counts tokens as you type using the cl100k_base tokenizer approximation (same as GPT-4, GPT-4o, and ChatGPT). Simply write or paste your text and see the token count update in real-time.

Does this token counter work for Claude?

Yes! Markdown Studio supports token counting for the full Claude family including Claude 4.5 and Claude Opus 4.6. Select any Claude model from the dropdown to see accurate token estimates. Also supports GPT-5, Gemini 3, and Llama 4.

Is this markdown editor free to use?

Yes, Markdown Studio is completely free with no login required. All features including token counting, PDF export, and Mermaid diagrams are available at no cost.

What is a context window in AI models?

A context window is the maximum number of tokens an AI model can process in a single request. Llama 4 Scout leads with 10M tokens, Gemini 3 offers 2M, GPT-4.1 and Grok-3 support 1M, and Claude 4.5 handles 200K. Markdown Studio shows your usage percentage for each model.

What are smart variables in Markdown Studio?

Smart variables let you create reusable placeholders in your markdown that can be filled dynamically. Perfect for AI prompts, templates, and documents with repeated values. Variables appear with visual gutter icons and support presets for common use cases.

Can I collaborate with AI agents in Markdown Studio?

Yes! Markdown Studio supports XML/AI prompt tag autocomplete with 23+ specialized tags for Claude, GPT, and other LLMs, smart variables with presets for dynamic templates, and smart copy formats optimized for AI consumption.

What tokenizer does GPT-4.1 use?

GPT-4.1 uses the o200k_base tokenizer, which has an extended vocabulary (~200,000 tokens) compared to cl100k_base (~100,000 tokens) used by GPT-4 and GPT-4 Turbo. The o200k_base tokenizer is also used by GPT-5, o1, o3, and o4 reasoning models. Markdown Studio supports both tokenizers for accurate token counting.

How do I count tokens for o1, o3, or o4 reasoning models?

Reasoning models (o1, o3, o4) use the o200k_base tokenizer and have two types of tokens: input tokens (your prompt) and thinking tokens (internal reasoning). Markdown Studio counts the input tokens using o200k_base. Note that thinking tokens are generated by the model and cannot be predicted beforehand. Select the appropriate reasoning model from the dropdown to see accurate input token counts.

Which AI model has the largest context window?

As of February 2026, Llama 4 Scout has the largest context window at 10M tokens, followed by Gemini 3 at 2M, GPT-4.1 and Grok-3 at 1M, and Claude 4.5 at 200K. Markdown Studio supports all these models and shows your context window usage percentage in real-time.

What's the best free markdown editor in 2026?

Markdown Studio is the top free markdown editor in 2026. It combines professional editing with AI prompt testing, token counting for 20+ models, Mermaid diagrams, LaTeX math, GitHub sync, and multi-format export — all with zero-knowledge privacy. No login or payment required.

Is there a free alternative to Typora?

Markdown Studio is a free alternative to Typora that adds AI-powered features. It includes live preview, code highlighting for 180+ languages, Mermaid diagrams, LaTeX math, and multi-format export. Plus AI token counting, smart variables, and GitHub sync — features Typora doesn't offer.

What is a privacy-focused prompt testing tool?

Markdown Studio is a privacy-focused prompt testing tool with zero-knowledge architecture. Your content never leaves your browser. Bring your own API keys (stored with AES-256-GCM encryption), test prompts across GPT-5, Claude 4.5, Gemini 3, and more — all 100% locally.

How do I get deterministic outputs from LLMs?

Use Markdown Studio's PromptOps locked mode. It enforces temperature=0, sets a fixed seed, pins the exact model version, and certifies snapshots with SHA-256 hashing. This ensures reproducible outputs across executions — critical for compliance, auditing, and regression testing.

Claude Tokenizer Explained 200K Context

Understanding token counting for Claude 3, Claude 4, and Claude 4.5

What is the Claude Tokenizer?

Anthropic's Claude models use a proprietary tokenizer based on Byte Pair Encoding (BPE). While Anthropic has not published the exact tokenizer specification, the Claude tokenizer is optimized for natural language understanding and efficiently handles structured formats like XML tags, markdown, and code.

Claude Tokenizer at a Glance

Vocabulary size: ~100,000 tokens (estimated)
Encoding method: Proprietary BPE variant
Context window: 200,000 tokens (all current models)
Average efficiency: ~4-5 characters per token (English)

Claude Models and Context Windows

All current Claude models share the same 200K-token context window, making it straightforward to plan your token budget regardless of which Claude variant you use.

Model	Context Window	Best For
Claude Opus 4.6	200,000 tokens	Agentic coding, complex analysis
Claude Sonnet 4.5	200,000 tokens	Balanced performance and speed
Claude Sonnet 4	200,000 tokens	General-purpose tasks
Claude Haiku 4	200,000 tokens	Fast, cost-effective responses
Claude 3.5 Sonnet	200,000 tokens	Coding, analysis, creative writing
Claude 3.5 Haiku	200,000 tokens	Quick tasks, high throughput
Claude 3 Opus	200,000 tokens	Complex reasoning, research
Claude 3 Sonnet	200,000 tokens	Balanced workloads
Claude 3 Haiku	200,000 tokens	Speed-critical applications
Claude 3 Opus (extended)	200,000 tokens	Long-document analysis

Note: All current Claude models share the same 200K-token context window. Unlike OpenAI models where context windows vary significantly between model tiers, Claude provides a consistent experience. Token costs differ by model tier, not context limits.

How Claude Token Counting Works

Claude uses a BPE variant that is optimized for the types of content it commonly processes. The tokenizer handles English text at approximately 4-5 characters per token, which is comparable to OpenAI's cl100k_base tokenizer.

Token Count Examples

Approximate token counts for common content types with the Claude tokenizer:

Hello, world! → ~4 tokens
The quick brown fox jumps over the lazy dog → ~10 tokens
Explain quantum computing in simple terms → ~7 tokens
A typical email (~200 words) → ~250-300 tokens
A full page of text (~500 words) → ~625-750 tokens

Tokenization Efficiency

Claude's tokenizer is particularly efficient with structured content formats that are common in AI workflows:

XML tags: Claude's native prompt format uses XML, and the tokenizer handles tags efficiently
Markdown: Formatting syntax is tokenized compactly
Code: Common programming patterns are well-represented in the vocabulary
JSON/YAML: Structural tokens are handled efficiently

Claude vs GPT Tokenizer Comparison

Comparing Claude's tokenizer with OpenAI's tokenizers helps when you are choosing between providers or estimating costs across platforms.

Aspect	Claude	cl100k_base (GPT-4)	o200k_base (GPT-5)
Vocabulary size	~100K (estimated)	~100,256	~200,019
Chars per token	~4-5	~4	~5
Max context	200K	128K	1M (GPT-4.1)
Tokenizer access	Estimation only	Open (tiktoken)	Open (tiktoken)
XML handling	Optimized	Standard	Standard
Multilingual	Good	Good	Better

Best Practices for Claude Token Management

Leverage XML Tags

Claude is specifically trained to work with XML-structured prompts. Using tags like <context>, <instructions>, and <examples> not only improves response quality but is also tokenized efficiently by Claude's tokenizer.

Make the Most of the 200K Window

With 200,000 tokens of context, Claude can process approximately 150,000 words or 300+ pages of text in a single request. This enables use cases like:

Analyzing entire codebases or documentation sets
Processing long legal or financial documents
Maintaining extended multi-turn conversations
Few-shot prompting with many examples

Accuracy Tips

Since Anthropic does not publish the exact tokenizer, token counts for Claude are always estimates. For precise budgeting:

Use the Anthropic API's token counting endpoint for exact counts
Add a 10-15% buffer when estimating token usage
Monitor actual usage through the Anthropic dashboard
Use our token counter tool for quick approximations

Try the Claude Token Counter

Estimate token counts for Claude models in real time. Paste your text and see approximate token usage for any Claude model.

When to Choose Claude Over GPT

Claude excels at:

Long-document analysis and summarization (200K context)
Tasks requiring careful instruction following
Content that benefits from XML-structured prompts
Applications where safety and helpfulness are top priorities
Multi-step reasoning with detailed explanations

Consider GPT when:

You need exact token counts (OpenAI's tiktoken is open-source)
Your application requires very large context (GPT-4.1 supports 1M tokens)
You need reasoning-specific models (o1, o3, o4)
Your workflow depends on OpenAI-specific features (function calling, assistants API)
Cost optimization is critical (o200k_base offers ~25% token savings)

Common Questions

Is the Claude tokenizer the same as OpenAI's?

No. Claude uses a proprietary tokenizer developed by Anthropic. While it is similar in concept to OpenAI's BPE-based tokenizers (cl100k_base and o200k_base), the specific vocabulary and merge rules are different. Token counts between Claude and GPT models will differ slightly for the same text.

Can I get exact token counts for Claude?

Anthropic provides a token counting endpoint in their API that returns exact counts. For estimation purposes, tools like our token counter provide close approximations based on similar BPE tokenization. For production budgeting, always use the official API endpoint.

Why does Claude use 200K tokens for all models?

Anthropic chose a uniform 200K-token context window across all Claude models to simplify development. This means you can switch between Claude tiers (Haiku, Sonnet, Opus) without worrying about context length limits, only adjusting for cost and capability differences.

How do Claude tokens compare in cost?

Claude's per-token pricing varies by model tier. Haiku is the most affordable, Sonnet offers mid-range pricing, and Opus is the most expensive but most capable. Because token counts are similar to cl100k_base, you can roughly compare costs by looking at per-token rates between providers.