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 (model matches on unambiguous fragment)
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 Inkling --json

The script:

1. Loads the correct tokenizer for the selected model (via tinker-cookbook if installed, else transformers)
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

Tinker Pricing

Prices effective July 17, 2026 (prefill/sample rose ~50%, train ~10% on that date)
Source: https://tinker-docs.thinkingmachines.ai/tinker/models/

All prices in USD per million tokens. Prefill = input context (inference), Sample = output tokens (inference), Train = training tokens. Cached prefill tokens get an 80% discount. :peft:<context> = extended-context variant.

\* Inkling and Nemotron prices reflect a limited-time 50% discount.

Checkpoint storage: $0.10 per GB per month.

Tokenization

Every model's tokenizer resolves from its Tinker model ID (verified for all models above):

from tinker_cookbook.tokenizer_utils import get_tokenizer  # preferred
tokenizer = get_tokenizer("Qwen/Qwen3-8B")

# Or with plain transformers (same IDs, minus any :peft: suffix)
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", trust_remote_code=True)

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

Supported JSONL 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

Training tokens: 1,000,000 × 3 = 3,000,000
Cost: 3.0M × $0.44/M = $1.32

Example 2: Qwen3.6-35B-A3B on 5M tokens, 2 epochs

Training tokens: 5,000,000 × 2 = 10,000,000
Cost: 10.0M × $1.177/M = $11.77

Example 3: Inkling on 2M tokens, 4 epochs

Training tokens: 2,000,000 × 4 = 8,000,000
Cost: 8.0M × $5.61/M = $44.88

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 - different tokenizers produce different counts for the same text
3. RL costs more than the train rate alone: rollouts are billed at sample/prefill rates on top of training tokens
4. Multimodal inputs (Inkling images/audio) add tokens beyond text tokenization