Cost of Labeling Datasets with an LLM
Labeling millions of short items is a pure throughput problem where per-token price decides everything.
Typical monthly workload
- Input tokens
- 60,000,000
- Output tokens
- 3,000,000
- Requests / day
- 50,000
Illustrative estimate. Adjust the sliders below to match your own volume.
Recommended setup
Label with the cheapest acceptable model, then audit a random sample with a stronger model to estimate error rate.
Monthly cost by model for this workload
| Model | Input / 1M | Output / 1M | Est. monthly cost |
|---|---|---|---|
| Gemini 1.5 Flash | $0.075 | $0.30 | $5.40 |
| DeepSeek V3 / V2.5 | $0.140 | $0.28 | $9.24 |
| GPT-4o Mini | $0.150 | $0.60 | $10.80 |
| Llama 3.3 70B (Groq/Together) | $0.590 | $0.79 | $37.77 |
| Claude 3.5 Haiku | $0.800 | $4.00 | $60.00 |
| o1-mini | $1.100 | $4.40 | $79.20 |
| Gemini 1.5 Pro | $1.250 | $5.00 | $90.00 |
| Mistral Large 2 | $2.000 | $6.00 | $138.00 |
| GPT-4o (Omni) | $2.500 | $10.00 | $180.00 |
| Claude 3.5 Sonnet | $3.000 | $15.00 | $225.00 |
| o1 (Reasoning) | $15.000 | $60.00 | $1080.00 |
Computed from list prices and the workload above (60,000,000 input and 3,000,000 output tokens per month), with no caching or batch discounts. Real bills vary.
How to cut this cost
- Measure agreement
Compare model labels with a human-labeled sample before scaling up.
- Use batch endpoints
Where providers offer batch pricing, use it for non-urgent labeling jobs.
Simulate your own spend
Quick Workload Presets
Pick a ready-made scenario or customize your token volume below.
Token & Usage Parameters
By switching or routing background tasks from GPT-4o to Gemini 1.5 Flash, your team saves up to $2,270 annually.
Lowest cost per token, high-frequency tasks, real-time audio/video
State-of-the-art open economics, high-performance general intelligence
High-volume text processing, fast chatbots, classification
Open-weights privacy, self-hosting or ultra-cheap hosted inferencing
Rapid code completion, customer service, structured data extraction
Fast STEM reasoning, competitive programming at lower cost
Massive context (2M tokens), full video/audio analysis, large codebases
Multilingual tasks, European compliance, enterprise code generation
Complex multimodal reasoning, coding, and production apps
Software engineering, nuanced writing, complex agentic workflows
Advanced mathematics, algorithmic coding, PhD-level scientific logic
Frequently asked questions
Is LLM labeling as accurate as humans?
It depends on the task. It is often good for clear categories and weaker for subjective or specialized labels, so always validate on a human-labeled sample.