Which LLM works best with GPT Researcher?

Choose from models that pass both research planning and summarization in the live application, then compare their measured per-run cost. A strong chat model can still fail an agent pipeline because structured output or a long request breaks.

GPT Researcher 应该配哪个模型？

先筛出在真实应用中同时通过研究规划与总结任务的模型，再比较单轮实测成本。聊天能力强并不代表能稳定跑通 Agent 流水线。

Measured evidence: gpt-researcher

Updated 2026-08-01; 4 models measured, 2 passed every task; the lowest measured cost among complete passes was gemini-3.5-flash at $0.012642 per benchmark run.

Human-readable benchmark · Raw JSON data

Agent workflow

  1. Read the current live-app benchmark.
  2. Choose a model with a complete pass rate.
  3. Start deep_research with a specific question.
  4. Poll check_job until a cited report is returned.

Recommended tool: deep_research

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "deep_research",
    "arguments": {
      "topic": "How has remote MCP adoption changed in 2026?",
      "depth": "standard"
    }
  }
}

MCP endpoint: https://ainetcafe.com/mcp · Machine-readable guide

Method and reuse

Live application tasks are run repeatedly; pass/fail, actual billed USD cost, and latency are recorded. Upstream-unavailable models are excluded from quality conclusions.

Data license: CC BY 4.0. Cite the page URL and measurement date.

Updated 2026-08-01 · All benchmark data · Agent capability index