cupel

separates precious LLMs from base LLMs

score local and cloud LLMs with custom prompts and a configurable judge

a cupel is the small dish used in a fire assay to separate precious metal from base metal

install

curl -fsSL https://cupel.run/install | bash

or

pip install cupel

the UI is bundled in the package

quick start

cupel

opens a browser at localhost:8042

ships with example data (8 models scored by Claude Opus 4.6 on 8 prompts) — the dashboard is populated on first launch

• LLM-assisted authoring — describe what you want to test, an LLM drafts the prompt and 0–3 rubric
• local + cloud — oMLX, Ollama, LM Studio, SGLang, OpenRouter, Anthropic, OpenAI
• configurable judge — any model can score responses on a 0–3 rubric with reasoning
• thinking model support — separates <think> blocks from answers, only judges the response
• multi-turn + tool calling — multi-step conversations with injected tool results
• speed tracking — tok/s and response times per model
• auto-discovery — probes known ports for local inference servers

leaderboard

leaderboard with score vs. speed, overall accuracy, and per-category breakdowns

score models

select models from discovered providers, filter by prompt category, choose a judge model, and start the run

progress updates via SSE as each prompt completes

author prompts

describe what to test, select a category and difficulty — an LLM generates the title, prompt text, and 0–3 rubric. edit before saving

results

each run is saved as JSON with the model, judge, timestamp, and per-prompt scores with judge reasoning
results can be sorted, tagged, muted and expanded to inspect individual evaluations

judge

set a default judge in UI settings or in config.yml:

judge:
  model: claude-opus-4-6

scores are 0–3:

score	meaning
3	correct and insightful
2	correct but shallow
1	partially correct
0	wrong or hallucinated

prompt format

{
  "id": 14,
  "category": "math_estimation",
  "title": "Model Memory from Quantization",
  "prompt": "A model has 70B parameters. Estimate memory for FP16, 8-bit, and 4-bit.",
  "rubric": {
    "3": "FP16: ~140GB, 8-bit: ~70GB, 4-bit: ~35GB. Shows the math.",
    "2": "Correct for 2 of 3, or all correct but no explanation.",
    "1": "Gets the direction right but wrong numbers.",
    "0": "Wrong math or doesn't understand quantization."
  }
}

multi-turn prompts

for tool calling and conversations, use turns instead of prompt:

{
  "id": 21,
  "title": "Tool Calling — School Status Check",
  "turns": [
    {
      "messages": [
        {"role": "system", "content": "You have tools: get_grades(name), ..."},
        {"role": "user", "content": "How are both kids doing?"}
      ]
    },
    {
      "inject_after": [
        {"role": "user", "content": "Tool results: get_grades(\"phoebe\") => ..."}
      ]
    }
  ],
  "rubric": { "3": "Emits correct tool calls, synthesizes results...", "..." : "..." }
}

thinking models

cupel handles <think> blocks automatically — separates thinking from the answer, only judges the response:

thinking: null   # model default (recommended)
thinking: 0      # disable
thinking: 4096   # explicit budget

providers

cloud providers can be added from presets (Anthropic, OpenRouter, OpenAI) or as custom endpoints. the settings page fetches model lists from a provider's API (includes per-token pricing for OpenRouter), validates API keys, and tests connections

cupel auto-discovers local servers on known ports:

port	server
8000	oMLX / vLLM
11434	Ollama
1234	LM Studio
30000	SGLang
8080	llama.cpp

API keys

each provider gets its own env var. put them in .env or ~/.cupel/.env:

OMLX_API_KEY=4242
ANTHROPIC_API_KEY=sk-ant-...
OPENROUTER_API_KEY=sk-or-...
OPENAI_API_KEY=sk-proj-...

or configure in config.yml:

providers:
  - name: openrouter
    api_url: https://openrouter.ai/api/v1/chat/completions
    api_key_env: OPENROUTER_API_KEY
    models: [google/gemini-2.5-pro, deepseek/deepseek-r1]

  - name: anthropic
    api_url: https://api.anthropic.com/v1/messages
    api_key_env: ANTHROPIC_API_KEY
    models: [claude-opus-4-6, claude-sonnet-4-6]

CLI

cupel                                  # open dashboard
cupel run                              # collect responses
cupel run --models "Qwen3.5-27B-8bit"  # specific model
cupel run --prompts 18-22              # specific prompts
cupel judge eval-results/*.json        # score with judge
cupel judge eval-results/*.json --judge-model gemma-4-26b-a4b-it-4bit
cupel init                             # create config.yml + eval-set

development

git clone https://github.com/tolitius/cupel.git && cd cupel
pip install -e .
uvicorn cupel.server:app --reload --port 8042

vanilla JS frontend (Preact + HTM from CDN). no build step.

license

Copyright © 2026 tolitius

Distributed under the Apache 2.0 License.