> ## Documentation Index > Fetch the complete documentation index at: https://autonomy.computer/docs/llms.txt > Use this file to discover all available pages before exploring further. # Models > Choose language models for your agents. Large language models give agents the ability to make autonomous decisions. The [Autonomy Computer](/docs/what-is-autonomy#autonomy-computer) provides a **Model Gateway** that gives your apps access to a wide range of models from different providers, each optimized for different use cases and cost profiles. You specify which model an agent should use by passing the `model` argument to `Agent.start()`: ```python images/main/main.py theme={null} from autonomy import Agent, Model, Node async def main(node): await Agent.start( node=node, name="henry", instructions="You are Henry, an expert legal assistant", model=Model("claude-sonnet-4") ) Node.start(main) ``` You can easily switch models by changing the model name in your `Model()` constructor. ## Supported Models Autonomy supports a curated set of models from leading providers. All models are accessed through the Model Gateway, which handles routing, load balancing, and failover automatically. *** ## Chat Models Models for conversational AI and text generation, sorted by price from highest to lowest. | Model | Provider | Input (per 1M tokens) | Output (per 1M tokens) | | - | - | - | - | | `claude-opus-4`, `claude-opus-4-v1` | Anthropic | \$15.00 | \$75.00 | | `o1` | OpenAI | \$15.00 | \$60.00 | | `claude-opus-4-5`, `claude-opus-4-5-v1` | Anthropic | \$5.00 | \$25.00 | | `claude-sonnet-4-5` | Anthropic | \$3.00 | \$15.00 | | `claude-sonnet-4`, `claude-sonnet-4-v1` | Anthropic | \$3.00 | \$15.00 | | `nova-premier`, `nova-premier-v1` | Amazon | \$2.50 | \$12.50 | | `gpt-4o` | OpenAI | \$2.50 | \$10.00 | | `gpt-4.1` | OpenAI | \$2.00 | \$8.00 | | `o3` | OpenAI | \$2.00 | \$8.00 | | `gpt-5.1` | OpenAI | \$1.25 | \$10.00 | | `gpt-5` | OpenAI | \$1.25 | \$10.00 | | `o3-mini` | OpenAI | \$1.10 | \$4.40 | | `o4-mini` | OpenAI | \$1.10 | \$4.40 | | `claude-haiku-4-5` | Anthropic | \$1.00 | \$5.00 | | `nova-pro`, `nova-pro-v1` | Amazon | \$0.80 | \$3.20 | | `gpt-4.1-mini` | OpenAI | \$0.40 | \$1.60 | | `gpt-5-mini` | OpenAI | \$0.25 | \$2.00 | | `gpt-4o-mini` | OpenAI | \$0.15 | \$0.60 | | `gpt-4.1-nano` | OpenAI | \$0.10 | \$0.40 | | `nova-lite`, `nova-lite-v1` | Amazon | \$0.06 | \$0.24 | | `gpt-5-nano` | OpenAI | \$0.05 | \$0.40 | | `nova-micro`, `nova-micro-v1` | Amazon | \$0.035 | \$0.14 | *** ## Embedding Models For text embeddings and semantic search. | Model | Provider | Input (per 1M tokens) | | - | - | - | | `titan-embed` | Amazon | \$0.20 | | `text-embedding-3-large` | OpenAI | \$0.13 | | `embed-english`, `embed-english-v3` | Cohere | \$0.10 | | `embed-multilingual`, `embed-multilingual-v3` | Cohere | \$0.10 | | `text-embedding-3-small` | OpenAI | \$0.02 | *** ## Realtime Models For real-time voice conversations. | Model | Provider | Text Input (per 1M) | Text Output (per 1M) | Audio Input (per 1M) | Audio Output (per 1M) | | - | - | - | - | - | - | | `gpt-4o-realtime-preview` | OpenAI | \$5.00 | \$20.00 | \$40.00 | \$80.00 | | `gpt-realtime` | OpenAI | \$4.00 | \$16.00 | \$32.00 | \$64.00 | | `gpt-realtime-mini` | OpenAI | \$0.60 | \$2.40 | \$10.00 | \$20.00 | *** ## Audio Models For text-to-speech and speech-to-text. | Model | Provider | Type | Price | | - | - | - | - | | `tts-1-hd` | OpenAI | Text-to-Speech | \$30.00 / 1M characters | | `tts-1` | OpenAI | Text-to-Speech | \$15.00 / 1M characters | | `whisper-1` | OpenAI | Speech-to-Text | \$0.006 / minute | *** ## Parameters The `Model()` constructor accepts additional parameters that control the model's behavior. * **`temperature`**: The sampling temperature to use, between 0 and 2. Higher values like 0.8 produce more random outputs, while lower values like 0.2 make outputs more focused and deterministic. * **`top_p`**: An alternative to sampling with temperature. It instructs the model to consider the results of the tokens with top\_p probability. For example, 0.1 means only the tokens comprising the top 10% probability mass are considered. ```python images/main/main.py theme={null} from autonomy import Agent, Model, Node async def main(node): # Use a lower temperature for more focused responses await Agent.start( node=node, name="analyst", instructions="You are a financial analyst", model=Model("claude-sonnet-4", temperature=0.2) ) Node.start(main) ``` ## Invoke Models Directly For simple use cases, you can also invoke models directly. This is useful when you need to make one-off completions without the full features of an agent. ```python images/main/main.py theme={null} from autonomy import Model, Node, SystemMessage, UserMessage async def main(node): model = Model("claude-sonnet-4") response = model.complete_chat([ SystemMessage("You are a helpful assistant."), UserMessage("Explain gravity in simple terms") ]) print(response) Node.start(main) ``` ### Streaming Responses You can also stream responses from models by setting `stream=True`: ```python images/main/main.py theme={null} from autonomy import Model, Node, SystemMessage, UserMessage async def main(node): model = Model("claude-sonnet-4") streaming_response = model.complete_chat([ SystemMessage("You are a helpful assistant."), UserMessage("Explain gravity") ], stream=True) async for chunk in streaming_response: if hasattr(chunk, "choices") and chunk.choices and chunk.choices[0].delta.content: content = chunk.choices[0].delta.content print(content, end="") Node.start(main) ``` ## Embeddings Generate embeddings for semantic search and similarity: ```python images/main/main.py theme={null} from autonomy import Model, Node async def main(node): model = Model("embed-english") embeddings = await model.embeddings([ "Hello world", "How are you?" ]) print(f"Embedding dimension: {len(embeddings[0])}") Node.start(main) ``` This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.