docs/customize/model-roles/chat.mdx
import { ModelRecommendations } from '/snippets/ModelRecommendations.jsx'
A "chat model" is an LLM that is trained to respond in a conversational format. Because they should be able to answer general questions and generate complex code, the best chat models are typically large, often 405B+ parameters.
In Continue, these models are used for normal Chat. The selected chat model will also be used for Edit and Apply if no edit or apply models are specified, respectively.
For the best overall Chat experience, you will want to use a 400B+ parameter model or one of the frontier models.
Our current top recommendations are Claude Opus 4.6 and Claude Sonnet 4 from Anthropic.
<Tabs> <Tab title="Hub"> View the [Claude Opus 4.6 model block](https://continue.dev/anthropic/claude-opus-4-6) or [Claude Sonnet 4 model block](https://continue.dev/anthropic/claude-4-sonnet) on the hub. </Tab> <Tab title="YAML"> ```yaml title="config.yaml" name: My Config version: 0.0.1 schema: v1models: - name: Claude Opus 4.6 provider: anthropic model: claude-opus-4-6 apiKey: <YOUR_ANTHROPIC_API_KEY>
</Tab>
</Tabs>
### Gemma from Google DeepMind
If you prefer to use an open-weight model, then the Gemma family of Models from Google DeepMind is a good choice. You will need to decide if you use it through a SaaS model provider, e.g. [Together](../model-providers/more/together), or self-host it, e.g. [Ollama](../model-providers/top-level/ollama).
<Tabs>
<Tab title="Hub">
<Tabs>
<Tab title="Ollama">
Add the [Ollama Gemma 3 27B block](https://continue.dev/ollama/gemma3-27b) from the hub
</Tab>
<Tab title="Together">
Add the [Together Gemma 2 27B Instruct block](https://continue.dev/togetherai/gemma-2-instruct-27b) from the hub
</Tab>
</Tabs>
</Tab>
<Tab title="YAML">
<Tabs>
<Tab title="Ollama">
```yaml title="config.yaml"
name: My Config
version: 0.0.1
schema: v1
models:
- name: "Gemma 3 27B"
provider: "ollama"
model: "gemma3:27b"
```
</Tab>
<Tab title="Together">
```yaml title="config.yaml"
name: My Config
version: 0.0.1
schema: v1
models:
- name: "Gemma 3 27B"
provider: "together"
model: "google/gemma-2-27b-it"
apiKey: <YOUR_TOGETHER_API_KEY>
```
</Tab>
</Tabs>
</Tab>
</Tabs>
### GPT-5.1 from OpenAI
If you prefer to use a model from [OpenAI](../model-providers/top-level/openai), then we recommend GPT-5.1.
<Tabs>
<Tab title="Hub">
Add the [OpenAI GPT-5.1 block](https://continue.dev/openai/gpt-5.1) from the hub
</Tab>
<Tab title="YAML">
```yaml title="config.yaml"
name: My Config
version: 0.0.1
schema: v1
models:
- name: GPT-5.1
provider: openai
model: gpt-5.1
apiKey: <YOUR_OPENAI_API_KEY>
If you prefer to use a model from xAI, then we recommend Grok-4.
<Tabs> <Tab title="Hub"> Add the [xAI Grok-4.1 block](https://continue.dev/xai/grok-4-1-fast-non-reasoning) from the hub </Tab> <Tab title="YAML"> ```yaml title="config.yaml" name: My Config version: 0.0.1 schema: v1models: - name: Grok-4.1 provider: xAI model: grok-4-1-fast-non-reasoning apiKey: <YOUR_XAI_API_KEY>
</Tab>
</Tabs>
### Gemini 3 Pro from Google
If you prefer to use a model from [Google](../model-providers/top-level/gemini), then we recommend Gemini 3 Pro.
<Tabs>
<Tab title="Hub">
Add the [Gemini 3 Pro block](https://continue.dev/google/gemini-3-pro-preview) from the hub
</Tab>
<Tab title="YAML">
```yaml title="config.yaml"
name: My Config
version: 0.0.1
schema: v1
models:
- name: Gemini 3 Pro
provider: gemini
model: gemini-3-pro-preview
apiKey: <YOUR_GEMINI_API_KEY>
For the best local, offline Chat experience, you will want to use a model that is large but fast enough on your machine.
If your local machine can run an 8B parameter model, then we recommend running Llama 3.1 8B on your machine (e.g. using Ollama or LM Studio).
<Tabs> <Tab title="Hub"> <Tabs> <Tab title="Ollama"> Add the [Ollama Llama 3.1 8b block](https://continue.dev/ollama/llama3.1-8b) from the hub </Tab> {/* <Tab title="LM Studio"> Add the [LM Studio Llama 3.1 8b block](https://continue.dev/explore/models) from the hub </Tab> */} </Tabs> </Tab> <Tab title="YAML"> <Tabs> <Tab title="Ollama"> ```yaml title="config.yaml" name: My Config version: 0.0.1 schema: v1 models:
- name: Llama 3.1 8B
provider: ollama
model: llama3.1:8b
```
</Tab>
<Tab title="LM Studio">
```yaml title="config.yaml"
name: My Config
version: 0.0.1
schema: v1
models:
- name: Llama 3.1 8B
provider: lmstudio
model: llama3.1:8b
```
</Tab>
<Tab title="Msty">
```yaml title="config.yaml"
name: My Config
version: 0.0.1
schema: v1
models:
- name: Llama 3.1 8B
provider: msty
model: llama3.1:8b
```
</Tab>
</Tabs>
If your local machine can run a 16B parameter model, then we recommend running DeepSeek Coder 2 16B (e.g. using Ollama or LM Studio).
<Tabs> {/* <Tab title="Hub"> <Tabs> <Tab title="Ollama"> Add the [Ollama Deepseek Coder 2 16B block](https://continue.dev/explore/models) from the hub </Tab> <Tab title="LM Studio"> Add the [LM Studio Deepseek Coder 2 16B block](https://continue.dev/explore/models) from the hub </Tab> </Tabs> </Tab> */} <Tab title="YAML"> <Tabs> <Tab title="Ollama"> ```yaml title="config.yaml" name: My Config version: 0.0.1 schema: v1 models:
- name: DeepSeek Coder 2 16B
provider: ollama
model: deepseek-coder-v2:16b
```
</Tab>
<Tab title="LM Studio">
```yaml title="config.yaml"
name: My Config
version: 0.0.1
schema: v1
models:
- name: DeepSeek Coder 2 16B
provider: lmstudio
model: deepseek-coder-v2:16b
```
</Tab>
<Tab title="Msty">
```yaml title="config.yaml"
name: My Config
version: 0.0.1
schema: v1
models:
- name: DeepSeek Coder 2 16B
provider: msty
model: deepseek-coder-v2:16b
```
</Tab>
</Tabs>
There are many more models and providers you can use with Chat beyond those mentioned above. Read more here