# Tool calling

OpenRouter compared three ways to get reliable JSON out of models, using 24 messy support messages across nine models that support structured outputs: send a strict JSON Schema, force a single tool call, or switch to a decision model that only returns labels. Across the nine models, 209 of 216 replies matched a strict schema, and all 7 misses came from one provider that ignores the schema; asking for JSON only in the prompt produced 18 failures, each from a code fence. For GLM 5.3 FlashX, which only supports tools, forcing the tool call gave 22 valid outputs out of 24, against 0 when the schema was sent directly.

OpenRouter compared three ways to get reliable JSON out of models, using 24 messy support messages across nine models that support structured outputs: send a strict JSON Schema, force a single tool call, or switch to a decision model that only returns labels. Across the nine models, 209 of 216 replies matched a strict schema, and all 7 misses came from one provider that ignores the schema; asking for JSON only in the prompt produced 18 failures, each from a code fence. For GLM 5.3 FlashX, which only supports tools, forcing the tool call gave 22 valid outputs out of 24, against 0 when the schema was sent directly.

AnyLanguageModel 1.0 turns Apple's Foundation Models interface into a replaceable layer: an app changes its import from FoundationModels to AnyLanguageModel, and the same session code can call the system model, open-weight models run through llama.cpp, MLX and Core ML, and several hosted providers. Before this, developers curious about local models were unsure how to start and wanted to avoid the added complexity of supporting multiple inference providers. Over the year the library aligned tool calling and guided generation across providers and added image input and Linux support.

AnyLanguageModel 1.0 turns Apple's Foundation Models interface into a replaceable layer: an app changes its import from FoundationModels to AnyLanguageModel, and the same session code can call the system model, open-weight models run through llama.cpp, MLX and Core ML, and several hosted providers. Before this, developers curious about local models were unsure how to start and wanted to avoid the added complexity of supporting multiple inference providers. Over the year the library aligned tool calling and guided generation across providers and added image input and Linux support.

Reflection AI's Beam reasoning model has left stealth testing under the codename stealth/cat-jam and is now available on AI Gateway under its own name, free for a limited launch period when served by Reflection AI. It targets coding and agentic tasks, ships weights under Apache 2.0, supports tool calling and structured outputs, and carries a 1M-token context window. The model has 501B total parameters but activates only 23B per token and skips tokens a task does not need, so it tends to answer sooner than larger open models.

Reflection AI's Beam reasoning model has left stealth testing under the codename stealth/cat-jam and is now available on AI Gateway under its own name, free for a limited launch period when served by Reflection AI. It targets coding and agentic tasks, ships weights under Apache 2.0, supports tool calling and structured outputs, and carries a 1M-token context window. The model has 501B total parameters but activates only 23B per token and skips tokens a task does not need, so it tends to answer sooner than larger open models.