Workbooks | Batch API | Doubleword Docs

Production-ready examples demonstrating what becomes possible when LLM inference is cheap enough to use at scale. Each example includes working code, real-world data, and measured costs.

To get started, sign up at app.doubleword.ai and generate an API key.

CLI Examples

Example	What It Does	Cost	Key Insight
async-agents	Deep research with recursive agent trees	$0.34 for 47 agents	1-hour SLA enables multi-round agentic workflows
synthetic-data-generation	Generate training data with quality filtering	$3.21 for 10K samples	3-stage pipeline in 3 hours, not 3 days
data-processing-pipelines	Clean and enrich messy records	$0.80 for 50K records	LLM-powered normalization at pipeline scale
embeddings	Semantic search over document corpus	$0.03 for 1.6M tokens	70% cheaper than OpenAI for same quality
model-evals	Benchmark models on GSM8K	$0.21 for 1,319 questions	Comprehensive evaluation becomes routine
bug-detection-ensemble	Classify security vulnerabilities	$0.40 for 4,642 samples	Run twice for calibration, still under $1
dataset-compilation	Build company datasets via search + LLM	$1.05 for 188 companies	100% recall vs Gartner Magic Quadrant
structured-extraction	Extract fields from scanned receipts	$0.12 for 626 receipts	Qwen3-30B beats GPT-5.2 at 1/12th cost
image-summarization	Caption images for social media	$0.10 for 1,000 images	Vision batch makes captioning automatic

Why These Examples Matter

Each example demonstrates a "more is different" capability: something that becomes qualitatively new when inference costs drop by 10-50x.

Multi-stage pipelines benefit from the 1-hour SLA. Synthetic data generation and data processing both run three sequential batches. With a 24-hour SLA, that's three days minimum. With Doubleword's 1-hour SLA, the same pipelines complete in 3 hours. This changes iteration speed: you can refine prompts and re-run the full pipeline multiple times in a single day.

Agentic workflows compound the SLA advantage. The async-agents example spawns recursive agent trees that require 20+ batch rounds. At 24 hours per round, that's potentially a month of wall-clock time. At 1 hour per round, it completes in a day.

Single-batch workloads are about cost. Embeddings, model evals, and image summarization process everything in one batch. The 24-hour SLA is fine; what matters is the 70-95% cost reduction versus real-time APIs. At these prices, you can embed your entire corpus, evaluate every model on every benchmark, and caption your whole image library.

Cost Comparison

Across all examples, Doubleword's batch pricing delivers 10-50x cost savings versus real-time APIs from OpenAI and Anthropic:

Task	Doubleword	OpenAI GPT-4o	Anthropic Sonnet 4.5	Savings
Deep research (2M tokens)	$0.34	$5.81	$7.25	17-21x
Synthetic data (20M tokens)	$3.21	$108.83	$154.62	34-48x
Data cleaning (6M tokens)	$0.80	$27.40	$38.15	34-48x
Model evaluation (400K tokens)	$0.21	$1.06	-	5x
Document embeddings (1.6M tokens)	$0.03	$0.10	-	3x

Pricing sources: Doubleword, OpenAI, Anthropic.

Running the Examples

Each example follows the same structure:

cd <example-name>
uv sync
export DOUBLEWORD_API_KEY="your-key"
uv run <example-name> run --help

Common CLI patterns across all examples:

Flag	Meaning
`-m`, `--model`	Model alias (`30b`, `235b`) or full name
`-n`, `--limit`	Number of items to process
`-o`, `--output`	Output directory (default: `results/`)
`--dry-run`	Prepare batch file without submitting
`status`	Check batch progress
`analyze`	Generate accuracy/cost analysis

Models

All examples support these models with consistent aliases:

Alias	Model	Use Case
`30b`	Qwen3-VL-30B-A3B-Instruct-FP8	Best value for most tasks
`235b`	Qwen3-VL-235B-A22B-Instruct-FP8	Maximum accuracy
`gpt5-nano`	gpt-5-nano	OpenAI budget tier
`gpt5-mini`	gpt-5-mini	OpenAI mid-tier
`gpt5.2`	gpt-5.2	OpenAI flagship

The Qwen models are available through Doubleword's batch API; OpenAI models use their API directly.

Project Structure

Each example follows this layout:

example-name/
├── README.md          # Results, methodology, replication instructions
├── pyproject.toml     # Dependencies and CLI entry point
├── src/
│   ├── cli.py         # Click CLI with run/status/analyze commands
│   ├── batch.py       # Batch API utilities
│   └── ...            # Task-specific modules
├── data/              # Sample data or scripts to fetch it
└── results/           # Output artifacts (gitignored)

Requirements

Python 3.11+
uv for dependency management
Doubleword API key from app.doubleword.ai
Some examples require additional API keys (Serper for web search, OpenAI for comparison runs)