AIModelsTester
Openroutertext->text

Auto Router

Your prompt will be processed by a meta-model and routed to one of dozens of models (see below), optimizing for the best possible output. To see which model was used, visit [Activity](/activity), or read the `model` attribute of the response. Your response will be priced at the same rate as the routed model. Learn more, including how to customize the models for routing, in our [docs](/docs/guides/routing/routers/auto-router). Requests will be routed to the following models: - [openai/gpt-5.1](/openai/gpt-5.1) - [openai/gpt-5](/openai/gpt-5) - [openai/gpt-5-mini](/openai/gpt-5-mini) - [openai/gpt-5-nano](/openai/gpt-5-nano) - [openai/gpt-4.1](/openai/gpt-4.1) - [openai/gpt-4.1-mini](/openai/gpt-4.1-mini) - [openai/gpt-4.1-nano](/openai/gpt-4.1-nano) - [openai/gpt-4o](/openai/gpt-4o) - [openai/gpt-4o-2024-05-13](/openai/gpt-4o-2024-05-13) - [openai/gpt-4o-2024-08-06](/openai/gpt-4o-2024-08-06) - [openai/gpt-4o-2024-11-20](/openai/gpt-4o-2024-11-20) - [openai/gpt-4o-mini](/openai/gpt-4o-mini) - [openai/gpt-4o-mini-2024-07-18](/openai/gpt-4o-mini-2024-07-18) - [openai/gpt-4-turbo](/openai/gpt-4-turbo) - [openai/gpt-4-turbo-preview](/openai/gpt-4-turbo-preview) - [openai/gpt-4-1106-preview](/openai/gpt-4-1106-preview) - [openai/gpt-4](/openai/gpt-4) - [openai/gpt-3.5-turbo](/openai/gpt-3.5-turbo) - [openai/gpt-oss-120b](/openai/gpt-oss-120b) - [anthropic/claude-opus-4.5](/anthropic/claude-opus-4.5) - [anthropic/claude-opus-4.1](/anthropic/claude-opus-4.1) - [anthropic/claude-opus-4](/anthropic/claude-opus-4) - [anthropic/claude-sonnet-4.5](/anthropic/claude-sonnet-4.5) - [anthropic/claude-sonnet-4](/anthropic/claude-sonnet-4) - [anthropic/claude-3.7-sonnet](/anthropic/claude-3.7-sonnet) - [anthropic/claude-haiku-4.5](/anthropic/claude-haiku-4.5) - [anthropic/claude-3.5-haiku](/anthropic/claude-3.5-haiku) - [anthropic/claude-3-haiku](/anthropic/claude-3-haiku) - [google/gemini-3-pro-preview](/google/gemini-3-pro-preview) - [google/gemini-2.5-pro](/google/gemini-2.5-pro) - [google/gemini-2.0-flash-001](/google/gemini-2.0-flash-001) - [google/gemini-2.5-flash](/google/gemini-2.5-flash) - [mistralai/mistral-large](/mistralai/mistral-large) - [mistralai/mistral-large-2407](/mistralai/mistral-large-2407) - [mistralai/mistral-large-2411](/mistralai/mistral-large-2411) - [mistralai/mistral-medium-3.1](/mistralai/mistral-medium-3.1) - [mistralai/mistral-nemo](/mistralai/mistral-nemo) - [mistralai/mistral-7b-instruct](/mistralai/mistral-7b-instruct) - [mistralai/mixtral-8x7b-instruct](/mistralai/mixtral-8x7b-instruct) - [mistralai/mixtral-8x22b-instruct](/mistralai/mixtral-8x22b-instruct) - [mistralai/codestral-2508](/mistralai/codestral-2508) - [x-ai/grok-4](/x-ai/grok-4) - [x-ai/grok-3](/x-ai/grok-3) - [x-ai/grok-3-mini](/x-ai/grok-3-mini) - [deepseek/deepseek-r1](/deepseek/deepseek-r1) - [meta-llama/llama-3.3-70b-instruct](/meta-llama/llama-3.3-70b-instruct) - [meta-llama/llama-3.1-405b-instruct](/meta-llama/llama-3.1-405b-instruct) - [meta-llama/llama-3.1-70b-instruct](/meta-llama/llama-3.1-70b-instruct) - [meta-llama/llama-3.1-8b-instruct](/meta-llama/llama-3.1-8b-instruct) - [meta-llama/llama-3-70b-instruct](/meta-llama/llama-3-70b-instruct) - [meta-llama/llama-3-8b-instruct](/meta-llama/llama-3-8b-instruct) - [qwen/qwen3-235b-a22b](/qwen/qwen3-235b-a22b) - [qwen/qwen3-32b](/qwen/qwen3-32b) - [qwen/qwen3-14b](/qwen/qwen3-14b) - [cohere/command-r-plus-08-2024](/cohere/command-r-plus-08-2024) - [cohere/command-r-08-2024](/cohere/command-r-08-2024) - [moonshotai/kimi-k2-thinking](/moonshotai/kimi-k2-thinking) - [perplexity/sonar](/perplexity/sonar)

Context Window

2.0M

Input Price

$-1000000.0000/M tokens

Output Price

$-1000000.0000/M tokens

Modality

text->text

Pricing Breakdown
Cost per million tokens for input and output
Input$-1000000.0000/M tokens
Output$-1000000.0000/M tokens

Cost Example: Processing 10,000 input tokens and generating 1,000 output tokens would cost approximately $-11000.000000

Context Window Size
Maximum tokens the model can process in a single request
Context Window2.0M tokens
100% of 1M max

~1500K

Words Equivalent

~1000

Pages of Text

~20000

Code Functions

Best Use Cases
What this model excels at
General-purpose

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