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Documentation Index

Fetch the complete documentation index at: https://developers.t2000.ai/llms.txt

Use this file to discover all available pages before exploring further.

You want to know which model is best for your prompt — not a leaderboard, your actual task. Your agent sends the same prompt to five models in parallel, then has Claude judge them blind on accuracy, completeness, and tone. ~$0.14, one wallet — no juggling five provider accounts.
This is a durable MPP demo — the headline is one wallet replacing five API keys. A sandboxed client can’t reach five real frontier models at once; here it’s one pay per model, gasless, no signup.

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The prompt

Run a bake-off on this prompt across Claude, GPT, Gemini, Groq's Llama, and
DeepSeek: "Explain why USDC depegs happen, in 3 sentences a beginner gets."
Then judge them on accuracy, clarity, and which you'd ship.

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What runs

  1. POST /anthropic/v1/messages — Claude’s answer (~$0.02)
  2. POST /openai/v1/chat/completions — GPT’s answer (~$0.02)
  3. POST /gemini/v1beta/models/gemini-2.5-pro — Gemini’s answer (~$0.04)
  4. POST /groq/v1/chat/completions — Llama (on Groq) answer (~$0.02)
  5. POST /deepseek/v1/chat/completions — DeepSeek’s answer (~$0.02)
  6. POST /anthropic/v1/messages — Claude judges all five (~$0.02)

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Run it

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Claude Desktop (MCP)

npm install -g @t2000/cli && t2 init && t2 receive && t2 mcp install
Paste the prompt with any task. The agent fans out to all five, then scores them.

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SDK

import { T2000 } from '@t2000/sdk';

const agent = await T2000.create();
const prompt = 'Explain why USDC depegs happen, in 3 sentences a beginner gets.';

const chat = (path: string, model: string) =>
  agent.pay({
    url: `https://mpp.t2000.ai/${path}`,
    method: 'POST',
    body: JSON.stringify({ model, messages: [{ role: 'user', content: prompt }], max_tokens: 300 }),
  });

const [claude, gpt, gemini, llama, deepseek] = await Promise.all([
  agent.pay({
    url: 'https://mpp.t2000.ai/anthropic/v1/messages',
    method: 'POST',
    headers: { 'anthropic-version': '2023-06-01' },
    body: JSON.stringify({ model: 'claude-sonnet-4-5', max_tokens: 300, messages: [{ role: 'user', content: prompt }] }),
  }),
  chat('openai/v1/chat/completions', 'gpt-4o'),
  agent.pay({
    url: 'https://mpp.t2000.ai/gemini/v1beta/models/gemini-2.5-pro',
    method: 'POST',
    body: JSON.stringify({ contents: [{ parts: [{ text: prompt }] }] }),
  }),
  chat('groq/v1/chat/completions', 'llama-3.3-70b-versatile'),
  chat('deepseek/v1/chat/completions', 'deepseek-chat'),
]);

const judgment = await agent.pay({
  url: 'https://mpp.t2000.ai/anthropic/v1/messages',
  method: 'POST',
  headers: { 'anthropic-version': '2023-06-01' },
  body: JSON.stringify({
    model: 'claude-sonnet-4-5',
    max_tokens: 600,
    messages: [{
      role: 'user',
      content:
        `Judge these 5 answers to "${prompt}" on accuracy, clarity, and which you'd ship. ` +
        `Score each 1-5 and pick a winner.\n\n` +
        `CLAUDE: ${JSON.stringify(claude.body)}\n\nGPT: ${JSON.stringify(gpt.body)}\n\n` +
        `GEMINI: ${JSON.stringify(gemini.body)}\n\nLLAMA: ${JSON.stringify(llama.body)}\n\nDEEPSEEK: ${JSON.stringify(deepseek.body)}`,
    }],
  }),
});

console.log((judgment.body as { content: { text: string }[] }).content[0].text);

​
Expected output

6 calls · ~$0.14 · ~8s · 0 taps
Five answers side by side + a scored verdict and a winner

​
Extend it

  • Add Mistral (/mistral/v1/chat/completions) or Cohere (/cohere/v1/chat) to widen the field
  • Swap Gemini 2.5 Pro for Flash (/gemini/v1beta/models/gemini-2.5-flash) to bake off on cost too
  • Time each call to compare latency, not just quality — Groq usually wins that one
  • Render the scorecard to a PDF with PDFShift (/pdfshift/v1/convert) for a shareable eval