Intelligent model routing that cuts LLM token costs and latency
Python SDK for LoRA fine-tuning with supervised fine-tuning and reinforcement learning
Stage
Not given
Not given
Founded
Not given
Not given
Based in
Not given
Not given
Pricing model
Paid
Not given
Pricing
Pay-as-you-go with 1% service fee. Cheap Tasks: $0.000043–$0.000194 per reference request. Standard Tasks: $0.000225–$0.000838 per reference request. Reasoning Tasks: $0.00066–$0.0023 per reference request. Frontier Tasks: $0.0023–$0.0354 per reference request. Agentic Tasks: $0.0071–$0.0354 per ref
Not given
Key features
Automatic task classification with Jev decision model
Multi-provider model access (OpenRouter, Kie.ai, Replicate, fal.ai)
Lowest-price eligible model selection
Support for text, image, video and audio modalities
Budget limits and provider filtering
Transparent billing and cost tracking
Three question types: Choice, Score and Noul
Pay-as-you-go pricing with no subscription
forward_backward() operation for computing loss and accumulating gradients
optim_step() for updating the adapter
sample() for generating from current or saved policy
save_state() to save and resume training state
Tinker API compatibility
Separate trainer and sampler resources
Synchronous and asynchronous RL patterns
Frozen base model weights with LoRA adapter updates
Support for open models from Qwen, Google, DeepSeek, Moonshot AI, NVIDIA, and Z.ai
What makes it different
Jev decision model automatically matches tasks to appropriate capability tiers instead of fixed frontier model routing
Reduces costs by up to 43.9% on illustrative workloads compared to fixed-model routing
Single API key for four different providers with unified billing
Works with multiple modalities and use cases like triage, classification and gating
Managed training and sampling infrastructure, no setup required
Tinker-compatible API for easy migration
Ready-made SFT and RL recipes through Loops cookbook
Token Cut
Loops