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 every model as exact local, compatible, or API counting required. That distinction is the quickest way to decide whether the interactive browser result can be used directly or should be treated only as a comparison.
| Model or family | Support level | Authoritative counting method |
|---|---|---|
| GPT-5.6 | Compatible | o200k_base view; confirm complete request usage with OpenAI |
| DeepSeek V4 Flash | Exact base text | Published Hugging Face tokenizer; DeepSeek usage for the final request |
| Kimi K3 | Exact base text | Published K3 vocabulary; Kimi API usage for complete XTML and multimodal requests |
| Grok 4.5 | Exact via API | xAI TokenizeText endpoint for model grok-4.5 |
| Gemini 3.6 Flash | Exact via API | Gemini models.countTokens |
| Claude Sonnet 5 | Exact via API | Anthropic Messages count_tokens |
| DeepSeek R1 | Exact base text | Published Hugging Face tokenizer |
| GPT-OSS 20B & 120B | Compatible base text | o200k_base view; Harmony wrapper excluded |
| DeepSeek V4 Pro & Flash | Exact base text | Published V4 tokenizer; include the message encoder for structured requests |
| GPT-5.6 Sol | Compatible | o200k_base view; confirm complete request usage with OpenAI |
| GPT-5.6 Terra | Compatible | o200k_base view; confirm complete request usage with OpenAI |
| GPT-5.6 Luna | Compatible | o200k_base view; confirm complete request usage with OpenAI |
| Gemini 3.5 Flash | API counting required | Google models.countTokens for the exact model and request |
| Claude Fable 5 | API counting required | Anthropic Messages count_tokens for the exact model and request |
| Qwen3 | Reference only | Tokenizer and chat template shipped with the exact Qwen3 checkpoint |
| Mistral | Reference only | Match the Mistral checkpoint tokenizer and chat template; see the directory note below |
| Llama | Reference only | Match the Llama checkpoint tokenizer and chat template; see the directory note below |
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.
| Search intent | Recommended path | Accuracy boundary |
|---|---|---|
| Current GPT model | GPT-5.6 token counter | Compatible local view; confirm API usage |
| OpenAI encoding comparison | Tiktoken online | Exact for the selected raw encoding |
| Specific API model request | OpenAI input_tokens endpoint | Counts supported structured request input |
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.
- Exact via provider API: the model owner exposes a model-aware counting or tokenization endpoint, so the page documents that endpoint and treats the browser explorer as comparison-only.
- Reference only: no verified browser tokenizer or dedicated count endpoint is available; the final API usage is the source of truth.
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: