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Which language model works best for your prompt? What are the biases inherent in each? Compare LLaMA3, Gemini, Mistral, OpenAI GPT-4o engines with more LLMs being added each month.
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GPT-4 Turbo (openai) | Llama 3 8b (Meta AI) | Mixtral 8x7b Instruct v0.1 (Mistral) | Gemini 1.0 Pro (Google) | Claude 3 Opus [L] (Anthropic)
Variables let you pass custom parameters to your workflow. Access a variable in your instruction prompt with Jinja, e.g. {{ my_variable }} Learn more.
{{ my_variable }}
product
⚙️ Settings
Avoid Repetition
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The maximum number of tokens to generate in the completion. Increase to generate longer responses.
Higher values allow the LLM to take more risks. Try values larger than 1 for more creative applications or 0 to ensure that LLM gives the same answer when given the same user input.
How many answers should the copilot generate? Additional answer outputs increase the cost of each run.
Run cost = 2 credits
Breakdown: 1Cr for Claude 3 Opus [L] (Anthropic) + 1Cr for Gemini 1.0 Pro (Google) + 1Cr for GPT-4 Turbo (openai) + 1Cr/run
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GPT-4 Turbo (openai)
Llama 3 8b (Meta AI)
Mixtral 8x7b Instruct v0.1 (Mistral)
Gemini 1.0 Pro (Google)
Claude 3 Opus [L] (Anthropic)
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