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

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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.

Model	Prefill	Sample	Train
thinkingmachines/Inkling*	$1.87	$4.68	$5.61
thinkingmachines/Inkling:peft:262144*	$3.74	$9.36	$11.23
nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16*	$2.49	$6.225	$5.478
nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16:peft:262144*	$3.32	$8.30	$9.96
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16*	$0.57	$1.44	$1.276
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16:peft:262144*	$0.76	$1.92	$2.32
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16*	$0.195	$0.495	$0.44
moonshotai/Kimi-K2.6	$2.205	$5.49	$4.84
moonshotai/Kimi-K2.6:peft:131072	$5.15	$12.81	$15.40
Qwen/Qwen3.6-35B-A3B	$0.54	$1.335	$1.177
Qwen/Qwen3.6-27B	$1.86	$5.595	$4.103
Qwen/Qwen3.5-397B-A17B	$3.00	$7.50	$6.60
Qwen/Qwen3.5-397B-A17B:peft:262144	$4.00	$10.00	$12.00
Qwen/Qwen3.5-35B-A3B-Base	$0.54	$1.335	$1.177
Qwen/Qwen3.5-9B (+ -Base)	$0.66	$1.995	$1.463
Qwen/Qwen3.5-4B	$0.33	$1.005	$0.737
Qwen/Qwen3-8B	$0.195	$0.60	$0.44
openai/gpt-oss-120b	$0.33	$0.84	$0.737
openai/gpt-oss-120b:peft:131072	$0.78	$1.94	$2.33
openai/gpt-oss-20b	$0.18	$0.45	$0.396
deepseek-ai/DeepSeek-V3.1	$1.695	$4.215	$3.718

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

Checkpoint storage: $0.10 per GB per month.

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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": "..."}

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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

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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