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103 lines
3.0 KiB
Markdown
103 lines
3.0 KiB
Markdown
---
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title: MiniMax Provider
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sidebar_label: MiniMax
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sidebar_position: 50
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description: Configure MiniMax's OpenAI-compatible API with the flagship M3 model and prior M2.7 routes, featuring large context windows and prompt caching for LLM testing.
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---
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# MiniMax
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[MiniMax](https://platform.minimax.io/) provides an OpenAI-compatible API for their language models. The MiniMax provider follows the [OpenAI provider](/docs/providers/openai/) chat configuration pattern, with the MiniMax-specific parameter differences described below.
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## Setup
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1. Get an API key from the [MiniMax Platform](https://platform.minimax.io/)
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2. Set `MINIMAX_API_KEY` environment variable or specify `apiKey` in your config
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## Configuration
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Basic configuration example:
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```yaml
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providers:
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- id: minimax:MiniMax-M3
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config:
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temperature: 0.7
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max_completion_tokens: 2048
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apiKey: YOUR_MINIMAX_API_KEY
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- id: minimax:MiniMax-M2.7
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config:
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max_completion_tokens: 2048
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```
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### Configuration Options
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- `temperature` - Range `(0.0, 1.0]`, cannot be 0
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- `max_completion_tokens` - Maximum completion tokens; the OpenAI-compatible API currently allows up to `2048`. Legacy `max_tokens` config is translated to this field for compatibility.
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- `apiBaseUrl` - Optional custom MiniMax-compatible proxy endpoint
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- `top_p`
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- `tools` and `tool_choice` - Use these for tool calling. MiniMax rejects the deprecated `function_call` parameter.
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When MiniMax reports prompt-cache reads, promptfoo calculates cost using the returned cached token count and the model's cache-read rate.
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## Available Models
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### MiniMax-M3 (Default)
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- Latest flagship model with up to a 1M token context window (512K guaranteed minimum) and up to 128K output
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- Multimodal: supports text, image, and video input
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- Input: $0.12/1M (cache hit), $0.6/1M (cache miss)
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- Output: $2.4/1M
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:::note
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M3 is the default and costs roughly 2x M2.7 per token. For cost-sensitive workloads, pin `minimax:MiniMax-M2.7` (or `MiniMax-M2.7-highspeed`) explicitly.
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:::
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### MiniMax-M2.7
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- Previous-generation flagship model
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- 204,800 token context window
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- Input: $0.06/1M (cache hit), $0.3/1M (cache miss)
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- Output: $1.2/1M
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### MiniMax-M2.7-highspeed
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- High-speed version of M2.7 for low-latency scenarios
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- 204,800 token context window
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- Input: $0.06/1M (cache hit), $0.6/1M (cache miss)
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- Output: $2.4/1M
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## Example Usage
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Here's an example comparing MiniMax with OpenAI:
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```yaml
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providers:
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- id: minimax:MiniMax-M3
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config:
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temperature: 0.7
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max_completion_tokens: 2048
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- id: openai:gpt-4o
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config:
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temperature: 0.7
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max_tokens: 4000
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prompts:
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- 'Answer the following question: {{question}}'
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tests:
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- vars:
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question: 'What is the capital of France?'
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assert:
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- type: contains
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value: 'Paris'
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```
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## API Documentation
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- [OpenAI Compatible API](https://platform.minimax.io/docs/api-reference/text-openai-api)
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- [Anthropic Compatible API](https://platform.minimax.io/docs/api-reference/text-anthropic-api)
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