GLM-5.1 - state-of-the-art agentic engineering, now available on DeepInfra!

Note: The list of supported base models is listed on the same page. If you need a base model that is not listed, please contact us at feedback@deepinfra.com
Rate limit will apply on combined traffic of all LoRA adapter models with the same base model. For example, if you have 2 LoRA adapter models with the same base model, and have rate limit of 200. Those 2 LoRA adapter models combined will have rate limit of 200.
Pricing is 50% higher than base model.
LoRA adapter model speed is lower than base model, because there is additional compute and memory overhead to apply the LoRA adapter. From our benchmarks, the LoRA adapter model speed is about 50-60% slower than base model.
You could merge the LoRA adapter with the base model to reduce the overhead. And use custom deployment, the speed will be close to the base model.
NVIDIA Nemotron 3 Nano 30B API Benchmarks: Latency & Cost<p>About NVIDIA Nemotron 3 Nano 30B A3B NVIDIA Nemotron 3 Nano 30B A3B is a large language model trained from scratch by NVIDIA, designed as a unified model for both reasoning and non-reasoning tasks. It is part of the Nemotron 3 family — NVIDIA’s most efficient family of open models, built for agentic AI applications. […]</p>
Llama 3.1 70B Instruct API from DeepInfra: Snappy Starts, Fair Pricing, Production Fit - Deep Infra<p>Llama 3.1 70B Instruct is Meta’s widely-used, instruction-tuned model for high-quality dialogue and tool use. With a ~131K-token context window, it can read long prompts and multi-file inputs—great for agents, RAG, and IDE assistants. But how “good” it feels in practice depends just as much on the inference provider as on the model: infra, batching, […]</p>
Qwen3 Coder 480B A35B API Benchmarks: Latency & Cost<p>About Qwen3 Coder 480B A35B Instruct Qwen3 Coder 480B A35B Instruct is a state-of-the-art large language model developed by the Qwen team at Alibaba Cloud, specifically designed for code generation and agentic coding tasks. It is a Mixture-of-Experts (MoE) model with 480 billion total parameters and 35 billion active parameters per inference, enabling high performance […]</p>
© 2026 Deep Infra. All rights reserved.