What is GPT-6 Token Counter?
This GPT-6 token counter guide covers Sol and Luna, two members of the GPT-6 family. A family name does not identify a single tokenizer or API request: choose gpt-6-sol or gpt-6-luna when measuring the structured input you will send.
Sol targets complex coding and agent workflows. Luna targets focused, cost-sensitive work at high volume. Both model documents list a 1,050,000-token context window and 128,000 maximum output tokens. These limits describe the models, not the accuracy of this browser's reference count. OpenAI also lists a newer GPT-6.1 Sol model; the Sol prices and guidance here refer specifically to gpt-6-sol.
| Member | API model ID | Positioning |
|---|---|---|
| GPT-6 Sol | gpt-6-sol | Complex coding and agent workflows |
| GPT-6 Luna | gpt-6-luna | Focused, high-volume tasks |
GPT-6 Tokenizer Support in Tiktokenizer
Support level: reference-only. The existing browser runtime has no verified tokenizer configuration for either named GPT-6 model. It opens o200k_base so you can examine text boundaries and token IDs, but selecting this encoding does not establish GPT-6 compatibility.
An exact label requires the named tokenizer to be verified directly; compatible requires an established encoding mapping. Neither is claimed here. For a model-aware input count, use OpenAI's Responses input-token counting operation with the explicit model ID and the same structured payload you intend to send.
GPT-6 Pricing and Cost Notes
Facts checked on 2026-10-08. Prices below are USD per 1M tokens for Standard processing with up to 272,000 input tokens, verified against the official Sol and Luna model documents linked below.
For prompts above 272,000 input tokens, OpenAI lists 2x input and cache rates and 1.5x output rates for the full request. Batch, Flex, Fast and regional processing can use different rates. Cache reads cost 10% of ordinary input at the same tier; cache writes are a separate category. A local text count cannot determine a cache hit or predict generated reasoning and output.
| Model | Input / 1M | Cache read / 1M | Cache write / 1M | Output / 1M |
|---|---|---|---|---|
| GPT-6 Sol | $2.00 | $0.20 | $2.50 | $10.00 |
| GPT-6 Luna | $0.10 | $0.01 | $0.125 | $0.50 |
How to Count GPT-6 Tokens
- 1. Paste your text in the reference text explorer. Inspect the live count, colored segments and token IDs for the selected encoding.
- 2. Switch raw encodings to compare text splits. Keep these results labeled as reference counts for GPT-6.
- 3. Choose gpt-6-sol or gpt-6-luna in your own API request and submit the complete input to OpenAI's Responses input-token counting operation.
- 4. After generation, use the returned usage categories and your processing tier to calculate cost. The browser does not call OpenAI or request an API key.
Token Count vs Billed Usage
The editor measures only the text under a reference encoding. Model-specific chat templates, message boundaries, system instructions, tool schemas, images and files can contribute input tokens absent from that text. OpenAI's request-level counting endpoint handles supported structured inputs.
An input count still does not predict the final output, internal reasoning, tool activity or cache eligibility. Apply input, cached-input, cache-write and output rates to the usage categories actually returned by the provider. Do not add a cached token to the ordinary-input category again. ChatGPT subscription access is separate from API token pricing.
Tiktokenizer Reference Sources
Tokenizer behavior and model limits change. Verify production decisions with current provider documentation: