💨 smokebench

Rapid, interactive TUI smoke tests for OpenAI- and Anthropic-compatible LLM endpoints.

Measure response quality, schema compliance, token throughput, and more on your hardware before committing to long runs.

Why smokebench • Quick Start • TUI Walkthrough • Built-in Benchmarks • Development/Contribution

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Why smokebench?

Online leaderboards test LLMs on enterprise GPU clusters running unquantized weights. They don't tell you how a 4-bit quant actually performs on your machine, under your system load, against your specific schema constraints.

Heavy LLM evaluation frameworks (lm-eval-harness, Promptfoo) are great for research or CI/CD pipelines, but take heavy setup time and long runs.

smokebench fills a different niche: zero-config terminal UI designed for rapid, interactive smoke tests while iterating on prompts, models, or endpoints.

Key Features:

• 🎯 TUI-first, not CLI-first: Configure endpoints, select models, toggle benchmarks, and set judge options visually with keyboard shortcuts. No YAML files to edit by hand for a quick check.
• 🔌 Multi-protocol: Direct support for OpenAI and Anthropic API schemas out of the box (works with Ollama, vLLM, LM Studio, SGLang, or cloud proxies).
• 🧪 8 built-in benchmarks: math, code, summarization, JSON mode, long context, creative writing, streaming throughput — enough to surface regressions in minutes.
• 🏆 Recommendation chips: after a run, the Results screen highlights which model won for coding, reasoning, cost*, or speed.
• 📊 Exportable artifacts: Auto-generates clean Markdown reports and structured JSON logs for tracking runs over time.
• 🪶 Lightweight: Python 3.11+, a handful of pip packages, no database, no Kubernetes, no platform account.

If you need rigorous leaderboards with thousands of samples, use something else.
If you want to quickly check "is this tiny model smart enough?" or "did my fine-tune improve?", this is the tool.

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Requirements

• Python 3.11+
• Core: httpx, textual, pydantic, pyyaml, jsonschema, rich
• Optional: tiktoken, rouge-score (auto-fallback if missing)

Quick Start

Installation

Install via pip or uv:

# Using pip
pip install smokebench

# Using uv
uv tool install smokebench

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TUI Walkthrough

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│ 1. Endpoint │ ──> │  2. Models  │ ──> │3. Benchmarks│ ──> │  4. Judge   │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
                                                                   │
      ┌─────────────┐                   ┌─────────────┐            │
      │ 6. Results  │ <──────────────── │   5. Run    │ <──────────┘
      └─────────────┘                   └─────────────┘

Screen	Purpose	Keys
1. Endpoint	Base URL, API key, protocol (auto/openai/anthropic)	Ctrl+T test, Ctrl+N next
2. Models	Auto-fetched list with context/modalities/features; multi-select	Space toggle, A all, N none, / filter
3. Benchmarks	Toggle 8 built-in tasks; add custom YAML; "Advanced" for per-task sample counts	Ctrl+N next
4. Judge	Pick judge model: from selected / separate endpoint / skip	Ctrl+N next
5. Run	Live progress bars + streaming log; pause/resume/cancel	P pause, C cancel
6. Results	Sortable table + recommendation chips; export JSON/MD	E export, Esc new run

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Screenshots

Endpoint Configuration Model Selection Benchmark Toggle Judge Configuration Run Progress

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Built-in Benchmarks (8)

Task	Samples	Grader	Measures
Math / Reasoning (GSM8K-lite)	20	Numeric regex	Accuracy
Code Generation (HumanEval-lite)	15	Sandboxed subprocess exec	Pass@1
Code Explanation	10	LLM-judge rubric 1–5	Quality
Summarization	10	ROUGE-L F1	Fidelity
Instruction / JSON Mode	12	JSON Schema validation	Schema compliance
Long Context (Needle-in-haystack)	5 sizes (1k–128k)	Exact substring	Effective context
Creative Writing	8	LLM-judge rubric 1–5	Style & Prompt Following
Latency / Throughput	10	Streaming metrics	TTFT, tokens/sec (p50/p95)

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Custom Benchmarks

Extend smokebench by loading custom YAML definitions directly inside the UI (Benchmarks > Add custom benchmark):

name: my_custom_task
description: "Extract structured data"
samples:
  - id: sample_01
    prompt: "Parse: 'John, 30, NYC' --> JSON with name, age, city"
    grader: json_schema
    schema:
      type: object
      properties:
        name: {type: string}
        age: {type: integer}
        city: {type: string}
      required: [name, age, city]

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Recommendations Engine

After a run, the Results screen shows chips for:

• best_overall - mean pass-rate across deterministic tasks
• best_coding - code_gen + code_explain
• best_reasoning - math + instruction_json
• best_long_context - needle retrieval at max tested size
• best_json_mode - instruction_json pass-rate
• best_writing - creative_writing + code_explain (judge score)
• fastest - median tokens/sec (latency task)
• cheapest - lowest USD cost (requires pricing config)

Tie-breakers automatically prioritize lower latency, then lower cost.

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Pricing (Optional)

Add per-model pricing in smokebench.yaml (auto-created in the project directory):

pricing:
  entries:
    gpt-4o:
      input_per_million: 5.0
      output_per_million: 15.0
    gpt-4o-mini:
      input_per_million: 0.15
      output_per_million: 0.60

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Output Artifacts

Each run creates ./smokebench_results/run_YYYYMMDD_HHMMSS/:

run_20260719_143022/
├── summary.json      # models, tasks, errors
├── details.json      # per-task per-model scores
├── samples.jsonl     # Full input/output logs per sample (streamable)
├── report.md         # human-readable markdown summary

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Contributing

Feel free to open issues or PRs!

To set up for local development:

# Clone the repository
git clone https://github.com/Ninja-5000/smokebench.git
cd smokebench

# Create virtual env and install dependencies
make venv
source .venv/bin/activate

# Run test suite
make test

# Launch dev version
make run

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License

Distributed under the MIT License