⚡ PromptBench

A/B test prompts across LLM providers from your terminal.

Compare how different LLMs respond to the same prompt — see latency, token usage, cost, and responses side by side in one command.

$ promptbench "Explain quantum computing in one sentence"

  ⚡ PROMPTBENCH RESULTS
  ────────────────────────────────────────────────────────────

  Prompt: Explain quantum computing in one sentence

  Model                               Latency     Tokens         Cost
  ─────────────────────────────────── ────────── ────────── ────────────
  claude-sonnet-4-20250514                 1.24s        142     $0.0006
  gemini-2.0-flash                    ⚡💰 312ms         98     $0.0000
  gpt-4o                                  845ms        127     $0.0010

  ┌─ claude-sonnet-4-20250514
  │ Quantum computing uses qubits that can exist in superpositions of
  │ 0 and 1 simultaneously, enabling parallel computation that can
  │ solve certain problems exponentially faster than classical computers.
  └───────────────────────────────────────────────────────────

  ┌─ gemini-2.0-flash
  │ Quantum computing harnesses quantum mechanical phenomena like
  │ superposition and entanglement to process information in ways
  │ impossible for traditional computers.
  └───────────────────────────────────────────────────────────

  ┌─ gpt-4o
  │ Quantum computing leverages the principles of quantum mechanics —
  │ superposition and entanglement — to perform computations that
  │ would be infeasible for classical computers.
  └───────────────────────────────────────────────────────────

  Total cost: $0.0016 · Avg latency: 799ms · 3 model(s)

Why PromptBench?

• One command, multiple models — no switching between playgrounds
• Side-by-side comparison — latency, tokens, cost, and full responses
• Supports major providers — OpenAI, Anthropic, Google Gemini
• Multiple output formats — terminal table, JSON, CSV, Markdown
• Fast parallel execution — all models run concurrently by default
• Batch mode — test multiple prompts from a file
• Zero required dependencies — install only the providers you need
• Pipe-friendly — works with stdin for scripting workflows

Install

pip install promptbench-cli

Install with provider SDKs:

# Individual providers
pip install "promptbench-cli[openai]"
pip install "promptbench-cli[anthropic]"
pip install "promptbench-cli[google]"

# All providers at once
pip install "promptbench-cli[all]"

Setup

Export your API keys:

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export GOOGLE_API_KEY="AI..."

You only need keys for the providers you want to use. PromptBench will warn you if a key is missing.

Usage

Basic comparison (default: GPT-4o, Claude Sonnet, Gemini Flash)

promptbench "Explain quantum computing in one sentence"

Pick specific models

promptbench "Write a haiku about coding" -m gpt4o sonnet flash

With a system prompt

promptbench "Summarize this text" -s "You are a concise technical writer" -m gpt4mini haiku

Batch prompts from a file

# prompts.txt — one prompt per line
promptbench -f prompts.txt -m gpt4o sonnet

JSON output

promptbench "What is Python?" -o json

CSV output

promptbench "What is Python?" -o csv > results.csv

Save results to file

promptbench "Compare REST vs GraphQL" --save results.json

Pipe from stdin

echo "What is the meaning of life?" | promptbench -m gpt4o sonnet

List all supported models

promptbench --list-models

Supported Models

Alias	Model	Provider
gpt4o / gpt4	gpt-4o	OpenAI
gpt4mini	gpt-4o-mini	OpenAI
gpt3.5	gpt-3.5-turbo	OpenAI
sonnet / claude-sonnet	claude-sonnet-4-20250514	Anthropic
haiku / claude-haiku	claude-haiku-4-5-20251001	Anthropic
flash / gemini-flash	gemini-2.0-flash	Google
gemini-pro	gemini-1.5-pro	Google

You can also use full model names directly (e.g., gpt-4-turbo, gemini-1.5-flash).

Python Library Usage

from promptbench.runner import run_bench
from promptbench.display import display_comparison

run = run_bench(
    prompt="Explain recursion simply",
    models=["gpt4o", "sonnet", "flash"],
    temperature=0.5,
)

print(display_comparison(run))

# Access individual results
for result in run.results:
    print(f"{result.model}: {result.latency_ms:.0f}ms, {result.cost_usd:.6f} USD")

Configuration Flags

Flag	Description	Default
-m, --models	Models to test	gpt4o sonnet flash
-s, --system	System prompt	None
-t, --temperature	Sampling temperature	0.7
--max-tokens	Max output tokens	1024
-f, --file	Prompts file path	None
-o, --output	Output format: table, json, csv, markdown	table
--full	Show full responses (no truncation)	Off
--no-parallel	Run models sequentially	Off
--save	Save results to JSON file	None
--list-models	List supported models	—
--version	Show version	—

Contributing

See CONTRIBUTING.md for development setup, how to add providers, and PR guidelines.

License

MIT — see LICENSE for details.