synthetic-sciences 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:
- Loads the correct tokenizer for the selected model
- Counts tokens in your JSONL file (supports chat, text, and instruction formats)
- 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.
| Category | Description |
|---|---|
| Prefill | Processing input context (inference) |
| Sample | Generating output tokens (inference) |
| Train | Training/fine-tuning tokens |
Qwen Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| Qwen3-4B-Instruct-2507 | $0.07 | $0.22 | $0.22 |
| Qwen3-8B | $0.13 | $0.40 | $0.40 |
| Qwen3-30B-A3B | $0.12 | $0.30 | $0.36 |
| Qwen3-VL-30B-A3B-Instruct | $0.18 | $0.44 | $0.53 |
| Qwen3-32B | $0.49 | $1.47 | $1.47 |
| Qwen3-235B-Instruct-2507 | $0.68 | $1.70 | $2.04 |
| Qwen3-VL-235B-A22B-Instruct | $1.02 | $2.56 | $3.07 |
Llama Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| Llama-3.2-1B | $0.03 | $0.09 | $0.09 |
| Llama-3.2-3B | $0.06 | $0.18 | $0.18 |
| Llama-3.1-8B | $0.13 | $0.40 | $0.40 |
| Llama-3.1-70B | $1.05 | $3.16 | $3.16 |
DeepSeek Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| DeepSeek-V3.1 | $1.13 | $2.81 | $3.38 |
GPT-OSS Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| GPT-OSS-120B | $0.18 | $0.44 | $0.52 |
| GPT-OSS-20B | $0.12 | $0.30 | $0.36 |
Moonshot Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| Kimi-K2-Thinking | $0.98 | $2.44 | $2.93 |
Model-to-Tokenizer Mapping
Use the correct HuggingFace tokenizer for accurate token counting:
| Model | HuggingFace Tokenizer |
|---|---|
| Qwen3-4B-Instruct-2507 | Qwen/Qwen3-4B |
| Qwen3-8B | Qwen/Qwen3-8B |
| Qwen3-30B-A3B | Qwen/Qwen3-30B-A3B |
| Qwen3-32B | Qwen/Qwen3-32B |
| Qwen3-235B-Instruct-2507 | Qwen/Qwen3-235B-A22B-Instruct |
| Qwen3-VL-* | Qwen/Qwen2.5-VL-7B-Instruct (shared VL tokenizer) |
| Llama-3.2-1B | meta-llama/Llama-3.2-1B-Instruct |
| Llama-3.2-3B | meta-llama/Llama-3.2-3B-Instruct |
| Llama-3.1-8B | meta-llama/Llama-3.1-8B-Instruct |
| Llama-3.1-70B | meta-llama/Llama-3.1-70B-Instruct |
| DeepSeek-V3.1 | deepseek-ai/DeepSeek-V3 |
| GPT-OSS-* | Qwen/Qwen3-8B (compatible tokenizer) |
| Kimi-K2-Thinking | moonshotai/Kimi-K2-Instruct |
Tokenization
The bundled scripts/calculate_cost.py handles tokenization automatically. For custom use:
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
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
- LoRA Fine-Tuning: Tinker uses Low-Rank Adaptation (LoRA), not full fine-tuning
- Token Counting: Always use the model's native tokenizer for accurate counts - different tokenizers produce different token counts for the same text
- Vision Models: VL models have higher costs due to image processing overhead
- trust_remote_code: Required for some tokenizers (Qwen, DeepSeek)