Benchmarks you can feel

We all love benchmarks, but there's nothing like a hands on vibe check. What if we could meet somewhere in the middle?

Enter BENCHY. A chill, live benchmark tool that lets you see the performance, price, and speed of LLMs in a side by side comparison for SPECIFIC use cases.

Watch the latest development video here

• Thought Bench
  • Goal: Compare multiple reasoning models (Deepseek R1, Gemini 2.0 Flash Thinking, OpenAI o1, ,etc) thoughts side by side in parallel.
  • Watch the walk through video here
  • Front end: src/pages/ThoughtBench.vue
• BIG AI Coding Updates to Benchy
  • Watch the walk through video here
• Iso Speed Bench
  • Goal: Create a unified, config file based, multi-llm provider, yes/no evaluation based benchmark for high quality insights and iteration.
  • Watch o3-mini vibe check, comparison, and benchmark video here
  • Watch the M4 Unboxing and benchmark video here
  • Front end: src/pages/IsoSpeedBench.vue
• Long Tool Calling
  • Goal: Understand the best LLMs and techniques for LONG chains of tool calls / function calls (15+).
  • Watch the walk through video here
  • Front end: src/pages/AppMultiToolCall.vue
• Multi Autocomplete
  • Goal: Understand claude 3.5 haiku & GPT-4o predictive outputs compared to existing models.
  • Watch the walk through video here
  • Front end: src/pages/AppMultiAutocomplete.vue

• .env - Environment variables for API keys
• server/.env - Environment variables for API keys
• package.json - Front end dependencies
• server/pyproject.toml - Server dependencies
• src/store/* - Stores all front end state and prompt
• src/api/* - API layer for all requests
• src/pages/* - Front end per app pages
• src/components/* - Front end components
• server/server.py - Server routes
• server/modules/llm_models.py - All LLM models
• server/modules/openai_llm.py - OpenAI LLM
• server/modules/anthropic_llm.py - Anthropic LLM
• server/modules/gemini_llm.py - Gemini LLM
• server/modules/ollama_llm.py - Ollama LLM
• server/modules/deepseek_llm.py - Deepseek LLM
• server/benchmark_data/* - Benchmark data
• server/reports/* - Benchmark results

• Anthropic
• Google Cloud
• OpenAI
• Deepseek
• Ollama
  • After installing Ollama, pull the required models:

  # Pull Llama 3.2 1B model
  ollama pull llama3.2:1b
  
  # Pull Llama 3.2 latest (3B) model
  ollama pull llama3.2:latest
  
  # Pull Qwen2.5 Coder 14B model
  ollama pull qwen2.5-coder:14b
  
  # Pull Deepseek R1 1.5B, 7b, 8b, 14b, 32b, 70b models
  ollama pull deepseek-r1:1.5b
  ollama pull deepseek-r1:latest
  ollama pull deepseek-r1:8b
  ollama pull deepseek-r1:14b
  ollama pull deepseek-r1:32b
  ollama pull deepseek-r1:70b
  
  # Pull mistral-small 3
  ollama pull mistral-small:latest

# Install dependencies using bun (recommended)
bun install

# Or using npm
npm install

# Or using yarn
yarn install

# Start development server
bun dev  # or npm run dev / yarn dev

# Move into server directory
cd server

# Create and activate virtual environment using uv
uv sync

# Set up environment variables
cp .env.sample .env (client)
cp server/.env.sample server/.env (server)

# Set EVERY .env key with your API keys and settings
ANTHROPIC_API_KEY=
OPENAI_API_KEY=
GEMINI_API_KEY=
DEEPSEEK_API_KEY=
FIREWORKS_API_KEY=

# Start server
uv run python server.py

# Run tests
uv run pytest (**beware will hit APIs and cost money**)

• https://github.com/simonw/llm?tab=readme-ov-file
• https://github.com/openai/openai-python
• https://platform.openai.com/docs/guides/predicted-outputs
• https://community.openai.com/t/introducing-predicted-outputs/1004502
• https://unocss.dev/integrations/vite
• https://www.npmjs.com/package/vue-codemirror6
• https://vuejs.org/guide/scaling-up/state-management
• https://www.ag-grid.com/vue-data-grid/getting-started/
• https://www.ag-grid.com/vue-data-grid/value-formatters/
• https://llm.datasette.io/en/stable/index.html
• https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/get-token-count
• https://ai.google.dev/gemini-api/docs/tokens?lang=python
• https://ai.google.dev/pricing#1_5flash
• https://ai.google.dev/gemini-api/docs/structured-output?lang=python
• https://platform.openai.com/docs/guides/structured-outputs
• https://docs.anthropic.com/en/docs/build-with-claude/tool-use
• https://ai.google.dev/gemini-api/docs/models/experimental-models
• https://sqlparse.readthedocs.io/en/latest/intro.html
• mlx: https://huggingface.co/mlx-community
• ollama docs: https://github.com/ollama/ollama/blob/main/docs/api.md#examples
• deepseek docs: https://platform.deepseek.com/usage