Tinker Training Cost Calculator

Calculate training costs for Tinker fine-tuning jobs by tokenizing your dataset with the correct model tokenizer and applying current pricing.

Quick Start

Use the bundled script to calculate training costs:

# List available models and pricing
python scripts/calculate_cost.py --list-models

# Calculate cost for a JSONL dataset
python scripts/calculate_cost.py training_data.jsonl --model Qwen3-8B --epochs 3

# Output as JSON
python scripts/calculate_cost.py training_data.jsonl --model Llama-3.1-70B --json

The script:

1. Loads the correct tokenizer for the selected model
2. Counts tokens in your JSONL file (supports chat, text, and instruction formats)
3. Calculates the estimated training cost

Cost Formula

Training Cost = (total_tokens × epochs × train_price_per_million) / 1_000_000

Where:

• total_tokens = tokens in your training dataset (from tokenization)
• epochs = number of training passes (default: 3)
• train_price_per_million = model-specific training rate from pricing table

Tinker Pricing

All prices as of January 5, 2026 Source: https://thinkingmachines.ai/tinker/

All prices are in USD per million tokens.

Qwen Models

Llama Models

DeepSeek Models

GPT-OSS Models

Moonshot Models

Model-to-Tokenizer Mapping

Use the correct HuggingFace tokenizer for accurate token counting:

Tokenization

The bundled scripts/calculate_cost.py handles tokenization automatically. For custom use:

from transformers import AutoTokenizer

# Load the correct tokenizer for your model
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", trust_remote_code=True)

# Count tokens
token_count = len(tokenizer.encode("Your training text here"))

Supported JSONL Formats

The script handles these training data formats:

Chat format (recommended):

{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}

Text format:

{"text": "Your training text here"}

Instruction format (Alpaca-style):

{"instruction": "...", "input": "...", "output": "..."}

Quick Cost Examples

Example 1: Qwen3-8B on 1M tokens, 3 epochs

Dataset tokens: 1,000,000
Training tokens: 1,000,000 × 3 = 3,000,000
Cost: 3.0M × $0.40/M = $1.20

Example 2: Llama-3.1-70B on 5M tokens, 2 epochs

Dataset tokens: 5,000,000
Training tokens: 5,000,000 × 2 = 10,000,000
Cost: 10.0M × $3.16/M = $31.60

Example 3: Qwen3-235B on 2M tokens, 4 epochs

Dataset tokens: 2,000,000
Training tokens: 2,000,000 × 4 = 8,000,000
Cost: 8.0M × $2.04/M = $16.32

Important Notes

1. LoRA Fine-Tuning: Tinker uses Low-Rank Adaptation (LoRA), not full fine-tuning
2. Token Counting: Always use the model's native tokenizer for accurate counts - different tokenizers produce different token counts for the same text
3. Vision Models: VL models have higher costs due to image processing overhead
4. trust_remote_code: Required for some tokenizers (Qwen, DeepSeek)