Playground

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Features

• Multi-window comparison — Run up to 4 side-by-side prompt windows
• Multi-model support — Test across OpenAI, Anthropic, Google, xAI, Groq, and Together
• Streaming responses — Watch tokens arrive in real time
• Variables — Use {{variable}} placeholders for parameterized prompts
• Web search — Enable MetrixLLM Search or native provider web search
• Save and load prompts — Persist configurations to your Prompt Registry
• Code snippets — Export any playground run as Python or cURL code
• Run history — Replay previous runs with full context

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Opening the Playground

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Configuring a prompt

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Model selector

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Writing messages

• System — Sets the model’s behavior and persona (typically one message at the top)
• User — The human’s input to the model
• Assistant — Pre-filled assistant responses for few-shot prompting

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Using variables

You are a {{role}} assistant. Answer questions about {{topic}} in a {{tone}} tone.

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Running a prompt

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Response metadata

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Side-by-side comparison

• Maintains independent messages, model selection, and parameters
• Can run simultaneously with Run All
• Inherits settings from the most recent window when split

• Comparing the same prompt across different models
• Testing temperature variations side-by-side
• Evaluating system message phrasing options

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Web search

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Search depth (MetrixLLM Search)

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Saving prompts

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Loading prompts

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Code snippets

from openai import OpenAI

client = OpenAI(
    base_url="https://api.metrixllm.com/v1",
    api_key="YOUR_KEY",
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    temperature=0.7,
    max_tokens=1024,
    top_p=1,
)

print(response.choices[0].message.content)

curl -X POST https://api.metrixllm.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "Hello!"}
    ],
    "temperature": 0.7,
    "max_tokens": 1024,
    "top_p": 1
  }'

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Run history

• Model used
• Token count
• Latency
• Timestamp
• Preview of the response

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Replaying production requests

• Debugging failed or slow requests
• Iterating on prompts that underperformed in production
• Testing parameter changes against real request data

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State persistence

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Best practices

• Use variables for reusable templates — Avoid duplicating prompts; parameterize with {{variable}}.
• Compare at least 2 models before promoting a prompt to production.
• Check cost metadata — A prompt that costs $0.001 per call may be fine at 1K calls/day but expensive at 1M.
• Save winning prompts immediately — Use the Save button to persist to the Prompt Registry before moving on.
• Use web search judiciously — Native provider search adds latency; MetrixLLM Search is faster for simple lookups.

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Common pitfalls

• Temperature 0 is not deterministic across providers. For reproducibility, use temperature 0 and check finish_reason.
• Variables with no value remain as {{name}} in the request — ensure all detected variables have values before running.
• Model name vs display name — The model selector shows display names (e.g. “GPT-4o”) but the API uses internal names (e.g. gpt-4o). Code snippets use the correct internal name.