AI Coding Live News

Mistral launches its Responses API in public preview: OpenAI Responses-compatible, returning the answer, reasoning and tool calls as separate items for building agents

Mistral AI announced on X on Oct 28 that its Responses API is in public preview, positioned as the interface for building agents on Mistral. The official docs say it uses the OpenAI Responses format, and each response's output array carries the models messages, its reasoning and its function calls as separate items, which is easier to handle step by step than Chat Completions. The preview is stateless, so the app keeps the conversation history and sends it back; it supports text, image and file input, JSON Schema structured output, streaming events, client-executed function calls, reasoning.effort, and token counting via POST /v1/responses/input_tokens. It is served in the EU and US on Mistral AI Cloud Inference. The same thread says Mistral now also serves DeepSeek V4.1 Flash (public preview) and GLM 5.3 Flash (generally available).

Key Takeaways

  • The Responses API is in public preview and uses the OpenAI Responses format, so existing Responses integrations can move to Mistral more easily
  • Each response splits the answer, reasoning and function calls into separate output items, which suits step-by-step agent loops
  • The preview is stateless: the app keeps the history and sends earlier output items back along with function_call_output results
  • It supports image and file input, JSON Schema structured output, streaming, function calling (including namespaces), reasoning.effort and token counting via /v1/responses/input_tokens
  • It is served in the EU and US on Mistral AI Cloud Inference; Mistral also added DeepSeek V4.1 Flash (preview) and GLM 5.3 Flash (GA)
Read full story

llama.cpp v0.4.1 adds Maple/Hy4/Spark2.5, qwen3-coder parser fixes, and ggml 0.24.0

Official @ggml_org shipped llama.cpp v0.4.1: adds Maple 20B-A1B, Tencent Hy 4, and Spark2.5 support; improves Kimi-K3 rollback, JSON schema handling, qwen3-coder complex-type parsing, and MCP/multimodal fixes; updates ggml to v0.24.0. Release notes: ggml-org/llama.cpp v0.4.1.

Key Takeaways

  • Official llama.cpp v0.4.1 adds Maple 20B-A1B, Tencent Hy 4, and Spark2.5.
  • Improves qwen3-coder parsing, Kimi-K3 rollback, JSON schema, and multimodal/server stability.
  • ggml bumps to v0.24.0; notes on ggml-org/llama.cpp v0.4.1.
Read full story
Linked Models & Agents:

OpenAI enables Structured Outputs for o3-mini API with 120 tokens/s high-speed reasoning throughput

OpenAI rolled out strict JSON Schema Structured Outputs and function calling for the o3-mini API. Supported by new reasoning kernel optimizations, generation speed reaches 120 tokens/s, allowing coding agents to produce type-safe interfaces and AST payloads with zero JSON syntax drift at low latency.

Key Takeaways

  • Guarantees 100% JSON Schema adherence, eliminating syntax parsing failures during tool use.
  • Achieves up to 120 tokens/s decoding throughput, an 80% speedup over previous reasoning models.
  • Offers low, medium, and high reasoning effort toggles for varied latency and depth needs.
Read full story

Cohere boosts Command R+ code tool-use with nested multi-level JSON schema support and automatic retry loops

Enterprise AI platform Cohere hardened tool-use capabilities in Command R+. For intricate CI/CD pipelines and cloud orchestration, the model now natively handles deeply nested (5+ levels) JSON schemas with self-correcting retry loops upon parameter validation failure, cutting enterprise workflow aborts by 75%.

Key Takeaways

  • Outputs strictly formatted parameters for deeply nested (5+ levels) JSON schemas.
  • Built-in parameter validation self-correction reduces workflow aborts by 75%.
  • Available across Cohere API, AWS Bedrock, and Microsoft Azure AI.
Read full story
Linked Models & Agents:
ADSponsored