Using Pydantic with Qwen3 Max Instruct on OpenRouter
This tutorial explains how to use a Pydantic schema to extract structured data using Qwen3 Max. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
This tutorial explains how to use a Pydantic schema to extract structured data using Qwen3 Max. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
This tutorial explains how to use a Pydantic schema to extract structured data using Qwen3 VL 235B A22B Instruct. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
This tutorial explains how to use a Pydantic schema to extract structured data using Qwen3 VL 235B A22B Thinking. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
No matter how much money you are spending on OpenRouter, it is possible to increase the RoI by writing some additional Python code. Why? Because no matter which LLM you use, some percentage of responses will return invalid JSON. Usually, you will do a retry, which costs (say) $X Most of the time, the invalid…
This tutorial explains how to use a Pydantic schema to extract structured data using DeepSeek V3.1 Terminus. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
This tutorial explains how to use a Pydantic schema to extract structured data using OpenGVLab: InternVL3 78B. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
This tutorial explains how to use a Pydantic schema to extract structured data using Arcee AI: AFM 4.5B. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
This tutorial explains how to use a Pydantic schema to extract structured data using Qwen3 Coder Plus. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
This tutorial explains how to use a Pydantic schema to extract structured data using Qwen3 Coder Flash. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
This tutorial explains how to use a Pydantic schema to extract structured data using xAI Grok 4 Fast. The code uses the free model, but you can just change the model name to the paid one once it becomes available. Please read this tutorial first to understand the basic setup Code: LLM Response: Metrics
Cogito V2 Preview Llama 109B is less expensive than Deep Cogito Created Sep 2, 202532,767 context$0.18/M input tokens$0.59/M output tokens And in fact, it was able to provide schema compatible JSON for 84 out of the 99 test VAERS reports. One file did not return a response, and I had to stop the program after…
Deep Cogito V2 Preview Deepseek 671B is an interesting LLM simply because it is as expensive as GPT5-Full for input token pricing Created Sep 2, 2025163,840 context$1.25/M input tokens$1.25/M output tokens Current prices for GPT-5 Full: Created Aug 7, 2025400,000 context$1.25/M input tokens$10/M output tokens However, when I tried to get it to extract structured…