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.
GLM-4.6 API: Get fast first tokens at the best $/M from Deepinfra's API - Deep Infra<p>GLM-4.6 is a high-capacity, “reasoning”-tuned model that shows up in coding copilots, long-context RAG, and multi-tool agent loops. With this class of workload, provider infrastructure determines perceived speed (first-token time), tail stability, and your unit economics. Using ArtificialAnalysis (AA) provider charts for GLM-4.6 (Reasoning), DeepInfra (FP8) pairs a sub-second Time-to-First-Token (TTFT) (0.51 s) with the […]</p>
Kimi K2 0905 API from Deepinfra: Practical Speed, Predictable Costs, Built for Devs - Deep Infra<p>Kimi K2 0905 is Moonshot’s long-context Mixture-of-Experts update designed for agentic and coding workflows. With a context window up to ~256K tokens, it can ingest large codebases, multi-file documents, or long conversations and still deliver structured, high-quality outputs. But real-world performance isn’t defined by the model alone—it’s determined by the inference provider that serves it: […]</p>
© 2026 Deep Infra. All rights reserved.