Models & Pricing

All prices are per million tokens. Checkpoint storage is charged at $0.10 per GB per month.

What's changing

We now provide an 80% discount on cached prefill tokens.

Due to rising compute costs, we are also increasing our prefill and sample prices by ~50% and our train prices by ~10% starting July 17.

Pricing Terms

• Prefill: Processing input/prompt tokens (forward pass only)
• Cached prefill: The smaller price under each prefill price; applies to input tokens that hit the prompt cache (80% off)
• Sample: Generating output tokens (forward pass + sampling)
• Train: Forward and backward pass for gradient computation
• Context: Maximum sequence length. Models with :peft: suffix support extended context at higher prices.
• Tinker ID: The exact string to pass to create_lora_training_client(base_model=...) or create_sampling_client(base_model=...)

MoE models are priced by active parameters, making them significantly more cost-effective than dense models of similar quality.

Model Types

• Base: Raw pretrained models with no chat or instruction tuning. Best for post-training research or running the full post-training pipeline yourself.
• Reasoning: Always produce chain-of-thought before their answer. Highest intelligence, higher latency and token cost.
• Hybrid: Run in both thinking and non-thinking modes. They reason by default, but chain-of-thought can be disabled via a renderer or argument for faster, cheaper direct answers.
• Vision: Vision-language models that accept images alongside text. Shown as a + Vision suffix on the underlying type (for example, Hybrid + Vision).
• Audio: Models that accept audio alongside text. Shown as a + Audio suffix on the underlying type.

Architecture is either Dense (all parameters active per token) or MoE (mixture-of-experts, only a subset of parameters active per token). MoE models are highlighted in amber.

Choosing a Model

• Cost-effective: Use MoE models (highlighted in amber)
• Research/post-training: Use Base models
• Task-specific fine-tuning: Start with a Hybrid model
• Low latency: Use a Hybrid model with chain-of-thought disabled
• High intelligence: Use Reasoning or Hybrid models (chain-of-thought)
• Vision tasks: Use models with Vision in the type

Retired Models

These models have been retired and can no longer be used for training or inference, grouped by retirement date.

June 12, 2026

• Qwen:Qwen3-235B-A22B-Instruct-2507, Qwen3-VL-235B-A22B-Instruct, Qwen3.5-35B-A3B, Qwen3.5-27B, Qwen3-32B, Qwen3-30B-A3B, Qwen3-30B-A3B-Instruct-2507, Qwen3-VL-30B-A3B-Instruct, Qwen3-30B-A3B-Base, Qwen3-8B-Base, Qwen3-4B-Instruct-2507
• Llama:Llama-3.3-70B-Instruct, Llama-3.1-70B, Llama-3.1-8B, Llama-3.1-8B-Instruct, Llama-3.2-3B, Llama-3.2-1B
• DeepSeek:DeepSeek-V3.1-Base
• Kimi:Kimi-K2-Thinking