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Models & Aliases

llmist supports multiple LLM providers with convenient aliases for quick access.

Model Aliases

Section titled “Model Aliases”

Use short aliases instead of full model names:

| Alias | Full Model Name | Provider | |-------|----------------|----------| | gpt5 | openai:gpt-5.2 | OpenAI | | gpt5.2 | openai:gpt-5.2 | OpenAI | | gpt5.1 | openai:gpt-5.1 | OpenAI | | gpt5-mini | openai:gpt-5-mini | OpenAI | | gpt5-nano | openai:gpt-5-nano | OpenAI | | gpt5-codex | openai:gpt-5-codex | OpenAI | | gpt4o | openai:gpt-4o | OpenAI | | gpt4o-mini | openai:gpt-4o-mini | OpenAI | | gpt4 | openai:gpt-4o | OpenAI | | o1 | openai:o1 | OpenAI | | o3 | openai:o3 | OpenAI | | o4-mini | openai:o4-mini | OpenAI | | sonnet | anthropic:claude-sonnet-4-5 | Anthropic | | opus | anthropic:claude-opus-4-5 | Anthropic | | haiku | anthropic:claude-haiku-4-5 | Anthropic | | flash | gemini:gemini-2.5-flash | Google | | flash-lite | gemini:gemini-2.5-flash-lite | Google | | pro | gemini:gemini-3-pro-preview | Google |

Provider-Prefixed Models

Section titled “Provider-Prefixed Models”

For explicit provider selection, use the provider:model format:

Terminal window
    # Explicit provider selection
    npx @llmist/cli complete "Hello" --model openai:gpt-5
    npx @llmist/cli complete "Hello" --model anthropic:claude-sonnet-4-5
    npx @llmist/cli complete "Hello" --model gemini:gemini-2.5-flash
    npx @llmist/cli complete "Hello" --model huggingface:meta-llama/Llama-3.1-8B-Instruct

Model Capabilities

Section titled “Model Capabilities”

| Model | Vision | Streaming | Tool Use | Reasoning | Context | |-------|--------|-----------|----------|-----------|---------| | GPT-5 | ✓ | ✓ | ✓ | ✓ | 128K | | GPT-5 Mini | ✓ | ✓ | ✓ | ✗ | 128K | | GPT-4o | ✓ | ✓ | ✓ | ✗ | 128K | | o3 | ✗ | ✓ | ✓ | ✓ | 200K | | o4-mini | ✗ | ✓ | ✓ | ✓ | 200K | | Claude Opus 4.5 | ✓ | ✓ | ✓ | ✓ | 200K | | Claude Sonnet 4.5 | ✓ | ✓ | ✓ | ✓ | 200K | | Claude Haiku 4.5 | ✓ | ✓ | ✓ | ✓ | 200K | | Gemini Flash | ✓ | ✓ | ✓ | ✓ | 1M | | Gemini Pro | ✓ | ✓ | ✓ | ✓ | 1M | | DeepSeek V3.2 | ✗ | ✓ | ✓ | ✗ | 64K | | DeepSeek R1 | ✗ | ✓ | ✓ | ✗ | 64K | | Llama 3.3 70B | ✗ | ✓ | ✗ | ✗ | 128K | | Qwen 2.5 72B | ✗ | ✓ | ✗ | ✗ | 128K | | Mixtral 8x7B | ✗ | ✓ | ✗ | ✗ | 32K |

Section titled “Recommended Models by Use Case”

| Use Case | Recommended | Why | |----------|-------------|-----| | General tasks | sonnet | Best balance of quality and speed | | Complex reasoning | opus, o3, DeepSeek R1 | Highest capability; use .withReasoning("high") for o3/Claude | | High-volume tasks | haiku, flash | Fast and cost-effective | | Long documents | flash, pro | 1M token context | | Coding | sonnet, gpt5, DeepSeek V3.2 | Strong code understanding | | Vision tasks | gpt4o, flash | Excellent image analysis | | Open-source/self-hosted | Llama 3.3 70B, Qwen 2.5 72B | Free via HuggingFace |

HuggingFace Models

Section titled “HuggingFace Models”

llmist supports popular open-source models via HuggingFace's serverless inference:

DeepSeek Family

Section titled “DeepSeek Family”
  • deepseek-ai/DeepSeek-V3.2 - 685B MoE model for general reasoning and tool use
  • deepseek-ai/DeepSeek-R1 - Reasoning model excelling at math, logic, and coding
  • deepseek-ai/DeepSeek-Coder-V2-Instruct - Specialized for code generation

Meta Llama

Section titled “Meta Llama”
  • meta-llama/Llama-3.3-70B-Instruct - General-purpose flagship model
  • meta-llama/Llama-3.1-8B-Instruct - Efficient smaller variant
  • meta-llama/Llama-3.2-11B-Vision-Instruct - Vision-enabled model

Qwen (Alibaba)

Section titled “Qwen (Alibaba)”
  • Qwen/Qwen2.5-72B-Instruct - Strong general-purpose model
  • Qwen/Qwen2.5-Coder-32B-Instruct - Code-specialized
  • Qwen/Qwen2-VL-72B-Instruct - Vision-language model

Mistral AI

Section titled “Mistral AI”
  • mistralai/Mixtral-8x7B-Instruct-v0.1 - Mixture-of-experts architecture
  • mistralai/Mistral-Nemo-Instruct-2407 - 12B efficient model

Image Generation Models

Section titled “Image Generation Models”

| Model | Provider | Description | |-------|----------|-------------| | dall-e-3 | OpenAI | High-quality image generation | | dall-e-2 | OpenAI | Faster, lower cost | | imagen-3 | Google | Gemini image generation |

Speech Models

Section titled “Speech Models”

| Model | Provider | Description | |-------|----------|-------------| | tts-1 | OpenAI | Text-to-speech, standard quality | | tts-1-hd | OpenAI | Text-to-speech, high quality |

Usage Examples

Section titled “Usage Examples”
import { LLMist } from 'llmist';


// Using alias
const answer = await LLMist.createAgent()
  .withModel('sonnet')
  .askAndCollect('Hello!');


// Using full name
const answer2 = await LLMist.createAgent()
  .withModel('anthropic:claude-sonnet-4-5')
  .askAndCollect('Hello!');


// HuggingFace model
const answer3 = await LLMist.createAgent()
  .withModel('huggingface:meta-llama/Llama-3.1-8B-Instruct')
  .askAndCollect('Hello!');


// HuggingFace with provider routing
const answer4 = await LLMist.createAgent()
  .withModel('hf:deepseek-ai/DeepSeek-V3.2:fastest')
  .askAndCollect('Write a Python function to calculate factorial');


// OpenRouter model (access 400+ models)
const answer5 = await LLMist.createAgent()
  .withModel('openrouter:deepseek/deepseek-chat')
  .askAndCollect('Explain quantum computing');


// OpenRouter with alias
const answer6 = await LLMist.createAgent()
  .withModel('or:sonnet')
  .askAndCollect('Hello!');

Auto-Discovery

Section titled “Auto-Discovery”

llmist automatically discovers available providers based on environment variables:

| Variable | Provider | |----------|----------| | OPENAI_API_KEY | OpenAI | | ANTHROPIC_API_KEY | Anthropic | | GEMINI_API_KEY | Google Gemini | | OPENROUTER_API_KEY | OpenRouter | | HF_TOKEN | HuggingFace |

See Environment Variables for complete configuration.