tinker-training-cost

# 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: ```bash # 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**. | 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: ```python 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): ```json {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]} ``` **Text format**: ```json {"text": "Your training text here"} ``` **Instruction format** (Alpaca-style): ```json {"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)