LLM Tokenizer Support by Model

Which LLM Token Counter Should You Use?

Use the tokenizer or counting endpoint tied to the exact model and request structure you plan to send. A raw encoding can explain text boundaries, but chat roles, tool schemas, images, documents, cached content, and provider-side formatting can change the number reported by an API.

Tiktokenizer labels browser results as exact base text, compatible encoding, local estimate or reference encoding. No API key is needed for these local modes. Character estimates are oriented toward English text and can vary substantially for other languages, code and emoji.

GPT-5 and GPT-5.1 Tokenizer Searches

Searches for a GPT-5 tokenizer or GPT-5.1 tokenizer often omit the exact model snapshot and request format. Tiktokenizer does not treat the family name alone as a billing-exact specification. Use the current GPT-5.6 page for its labeled compatible encoding view, or select a raw encoding when you are intentionally comparing vocabularies.

For an API request tied to a particular OpenAI model ID, confirm the model in the current catalog and use the Responses input-token counting operation with the same structured input. This avoids implying that a generic GPT-5 label, a raw encoding, and complete API usage are interchangeable.

How Tiktokenizer Defines Accuracy

• Exact local tokenizer: the page loads the tokenizer artifacts published for the named open model and counts base text in the browser.
• Compatible encoding: the page uses a known base encoding that is useful for inspection but does not claim to reproduce every provider-side wrapper.
• Local estimate: a documented character heuristic estimates plain-text size without an API key. It does not reproduce token boundaries or IDs, and accuracy varies by language and content.
• Provider API counting: use the named model's counting or tokenization endpoint and check its guarantees. Claude count_tokens returns an input estimate that can differ from actual Messages usage.
• Reference encoding: a named raw encoding supplies a comparison count and IDs, without claiming to reproduce the selected model. Provider usage is the source of truth for the actual request.

Why Model-Specific Token Counts Differ

Token IDs belong to a vocabulary. The same text can produce different boundaries and counts across OpenAI, DeepSeek, Kimi, Grok, Gemini, Claude, Qwen, Mistral, and Llama models. A tokenizer update can also change the amount of text represented by a fixed context window.

Structured requests introduce another layer. System instructions, prior messages, function definitions, media inputs, reasoning, and special tokens may not appear in the text editor even though they contribute to request usage. For hard limits, cost estimates, or migrations, compare the local analysis with the usage returned by the target provider.

Mistral Token Counting

Choose the tokenizer supplied with your exact Mistral model checkpoint. The Mistral family name alone does not identify the tokenizer configuration or the chat template used by an inference service. This directory groups the counting guidance in one place; the browser's OpenAI encoding is only a comparison and is not an exact Mistral count.

For plain text, encode the unmodified prompt with the checkpoint tokenizer. For a conversation, apply that checkpoint's chat template to the complete message list, including roles and any supported tool definitions. Compare the resulting input length with the serving provider's reported usage before relying on it for a context limit.

Llama Token Counting

Use the tokenizer configuration shipped with the specific Llama checkpoint or fine-tune you run. A Llama family label does not guarantee identical token IDs, special-token settings, or conversation formatting across releases. The browser's comparison encoding does not provide an exact Llama count.

Keep the tokenizer revision and serving chat template aligned. Count raw text without adding a conversation wrapper only when that is the input you intend to measure. For chat, tokenize the rendered message list with the expected generation prefix, then check the provider's final usage for any additional request processing.

Tiktokenizer Reference Sources

Tokenizer behavior and model limits change. Verify production decisions with current provider documentation:

• OpenAI API: Count input tokens
• OpenAI: Compare current models
• DeepSeek: Token and token usage
• xAI: Tokenize API
• Google: Understand and count tokens
• OpenAI: Tokens and English character estimates
• Anthropic: Tokens and tokenizer changes
• Qwen: Qwen3-32B tokenizer
• Anthropic: Token counting