OpenAI models on Tokenator

Every OpenAI model available through Tokenator: API ID, context window, usage multiplier and current availability. The key and the base URL stay the same for every model in the catalog — switching models takes one request parameter.

OpenAIonline
GPT-5.6 Sol
gpt-5.6-sol
×1.8–×1.9

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks and long-horizon problem solving.

Context 1M tokens
OpenAIonline
Free GPT-6 Astra
free-gpt-6-astra
×6

This model is available on the free tier with predefined usage limits. GPT-6 Astra is OpenAI's flagship model for demanding end-to-end work. It is suited for advanced analysis, software engineering, deep research, scientific work, and document creation, with particular strengths in long-horizon agentic tasks that involve computer and browser use.

FreeContext 1M tokens
OpenAIonline
GPT-6 Astra
gpt-6-astra
×5–×6

GPT-6 Astra is OpenAI's flagship model for demanding end-to-end work. It is suited for advanced analysis, software engineering, deep research, scientific work, and document creation, with particular strengths in long-horizon agentic tasks that involve computer and browser use.

Context 1M tokens
OpenAIonline
GPT-5.6 Terra
gpt-5.6-terra
×1.7–×1.8

GPT-5.6 Terra is a balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is suited for everyday coding, reasoning, and agentic tasks where capability and cost need to be balanced, offering strong performance at roughly half the cost of Sol.

Context 1M tokens
OpenAIonline
Gemini Embedding 001
gemini-embedding-001
×1.35

gemini-embedding-001 provides a unified cutting edge experience across domains, including science, legal, finance, and coding. This embedding model has consistently held a top spot on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard since the experimental launch in March.

EmbeddingsContext 20K tokens
OpenAIonline
GPT 5.5
gpt-5.5
×1.8

GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks.

Context 1.05M tokens
OpenAIonline
GPT Image 2
gpt-image-2
×1–×2

GPT Image 2 combines OpenAI's GPT-5.4 model with state-of-the-art image generation capabilities from GPT Image 2.

Image genEditing
OpenAIonline
GPT Image 2.5
gpt-image-2.5
×1

GPT Image 2.5 is an image generation and editing model from OpenAI, positioned as the precision-oriented tier of the GPT Image 2.5 series. It is suited to detailed creative work where editing accuracy matters more than generation speed, via the dedicated Images API.

Image genEditing
OpenAIonline
GPT-4o mini
gpt-4o-mini
×1.5

GPT-4o mini is OpenAI's newest model after GPT-4 Omni, supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than GPT-3.5 Turbo. It maintains SOTA intelligence, while being significantly more cost-effective.

Context 128K tokens
OpenAIonline
GPT-5.6 Luna
gpt-5.6-luna
×1.6

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for its price tier.

Context 1M tokens
OpenAIonline
Text Embedding 3 Large
text-embedding-3-large
×1.2

text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

EmbeddingsContext 8.2K tokens
OpenAIonline
Text Embedding 3 Small
text-embedding-3-small
×1.0

text-embedding-3-small is OpenAI's improved, more performant version of the ada embedding model. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

EmbeddingsContext 8.2K tokens

12 OpenAI models in the catalog, 12 available right now, 2 for image generation, context up to 1.05M tokens, multiplier ×1–×6