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.

o200k_base Tokenizer Explained Extended

The next-generation tokenizer for GPT-4.1, GPT-5, and reasoning models

What is o200k_base?

The o200k_base tokenizer is OpenAI's next-generation encoding scheme, nearly doubling the vocabulary of its predecessor cl100k_base. It powers the latest GPT-4.1, GPT-5, and reasoning model families (o1, o3, o4), delivering better token efficiency and improved multilingual support.

o200k_base at a Glance

Vocabulary size: ~200,019 tokens
Encoding method: Advanced Byte Pair Encoding (BPE)
Average efficiency: ~5 characters per token (English)
Improvement: ~25% fewer tokens than cl100k_base for typical English text

Key improvement over cl100k_base:

With nearly double the vocabulary, o200k_base can represent more common words and subword patterns as single tokens. This means the same text requires fewer tokens, reducing both latency and API costs across all models that use it.

Models Using o200k_base

The o200k_base tokenizer is used by all of OpenAI's latest models, including the reasoning model family that introduces a new "thinking tokens" concept.

Model	Context Window	Token Type
GPT-4.1	1,000,000 tokens	Standard
GPT-4.1 mini	1,000,000 tokens	Standard
GPT-4.1 nano	1,000,000 tokens	Standard
GPT-5	256,000 tokens	Standard
o1	200,000 tokens	Input + Thinking
o1-mini	128,000 tokens	Input + Thinking
o3	200,000 tokens	Input + Thinking
o3-mini	200,000 tokens	Input + Thinking
o4-mini	200,000 tokens	Input + Thinking

o200k_base vs cl100k_base: Direct Comparison

Understanding the differences between these two tokenizers helps you make informed decisions about model selection and cost optimization.

Aspect	cl100k_base	o200k_base
Vocabulary size	~100,256	~200,019
Chars per token (English)	~4	~5
Multilingual efficiency	Good	Significantly better
Code tokenization	Good	Improved
Token count for same text	Baseline	~20-25% fewer
Supported models	GPT-3.5, GPT-4, GPT-4o	GPT-4.1, GPT-5, o1, o3, o4

How o200k_base Token Counting Works

Like cl100k_base, o200k_base uses Byte Pair Encoding (BPE), but with a significantly expanded merge table. The larger vocabulary means more common words and phrases are represented as single tokens.

Enhanced BPE Process

The o200k_base tokenizer was trained on a larger and more diverse corpus, allowing it to capture more linguistic patterns. The result is fewer tokens for the same input text, which directly translates to lower API costs and faster processing.

Token Count Comparison Examples

Here is how o200k_base compares to cl100k_base on the same inputs:

Hello, world! → cl100k: 4 tokens, o200k: 3 tokens
The quick brown fox jumps over the lazy dog → cl100k: 9 tokens, o200k: 8 tokens
Machine learning is transforming industries → cl100k: 5 tokens, o200k: 4 tokens
Artificial intelligence → cl100k: 2 tokens, o200k: 2 tokens
supercalifragilisticexpialidocious → cl100k: 7 tokens, o200k: 5 tokens

Try the o200k_base Token Counter

Count tokens in real time using the o200k_base tokenizer. Compare results with cl100k_base to see the efficiency gains for yourself.

When to Use o200k_base

Use o200k_base for:

All new projects targeting GPT-4.1, GPT-5, or reasoning models
Applications where token efficiency and cost savings matter
Multilingual content that benefits from better non-English tokenization
Large-context applications leveraging 200K-1M token windows
Reasoning tasks requiring o1, o3, or o4 models

Stick with cl100k_base for:

Existing applications deployed on GPT-3.5 or GPT-4
Systems that depend on exact cl100k token counts for caching or deduplication
Backward-compatible integrations with older OpenAI APIs
Testing or benchmarking against GPT-4 or GPT-4 Turbo baselines

Practical Benefits

Lower API Costs

Because o200k_base uses ~20-25% fewer tokens for the same text, your API costs drop proportionally. For a document that costs $1.00 with cl100k_base, the same document tokenized with o200k_base costs roughly $0.75-$0.80 in token charges (before any per-token price differences between models).

Better Multilingual Support

The expanded vocabulary includes more tokens for non-English languages, which means Chinese, Japanese, Korean, Arabic, and other scripts are tokenized more efficiently. This is particularly important for global applications where multilingual content is common.