AI Coding Live News

Independent benchmarker Artificial Analysis announced on X on November 4 (17:34 UTC) a new version of its API performance benchmarking: the headline metric moves from output speed to Time per Task, the time to finish one Intelligence Index task (output tokens per task divided by end-to-end output speed). It covers about 1,000 serverless endpoints, measures speed from sending the request to the final token, and adds multi-turn agentic coding and knowledge-work workloads. Per its data, GPT-6 Astra at max effort runs at 51 tokens/s yet finishes a task in about 17 minutes, against about 26 for Claude Opus 5.5 (max) and about 29 for Gemini 4 Carbon (high).

Fireworks said DeepSeek V4 Flash now includes native vision on serverless and priority tiers. It reports better benchmarks than the text-only 0731 checkpoint at the same $0.22 input / $0.66 output per 1M tokens. The model page lists a 305B MoE with about 1040K context, function calling, image input, and a speculative-decoding module.

Fireworks announced general availability of its Training API and Fireworks Lab, aiming to make model specialization accessible beyond labs that already own training clusters. The API lets teams connect a custom Python training loop to Fireworks-managed distributed training and rollout infrastructure, keeping control of loss, reward, data, and environment. Compute is split into Serverless mode for fast experiments and Dedicated mode when scale, control, and GPU economics matter. The stack is built for reinforcement learning as a single train-sample-serve-retrain loop, with three hard requirements: rollout throughput, weight sync so fresh weights land on the rollout deployment every step, and correctness so trainer and inference engine agree on the same model. Fireworks Lab additionally embeds forward-deployed researchers and engineers to co-design or build a model. Named customers include Cursor, Harvey, Figma, Vercel, Cognition, Factory, and Heidi.

Replit partnered with Neon to bring instant serverless Postgres database branching directly into Replit Agent. When the agent tests schema migrations or executes destructive seed operations, it automatically spawns an ephemeral Postgres branch in 500ms, safeguarding production data until changes are verified.

Fireworks AI launched day-zero US-hosted serverless inference for GLM-5.3, highlighting a 50% improvement on Code Bench and state-of-the-art results in open-weight cyber defense and long-horizon agents.

Together AI launched hosted serverless API serving for GLM-5.3 Flash, highlighting its 320B-A18B multimodal MoE architecture, 1M context, and high DeepSWE efficiency compared to proprietary frontier models.

Modal Labs announced Auto Endpoints for GLM-5.3-Flash, enabling developers to deploy and query the 320B-A18B MoE model via OpenAI-compatible endpoints with automated serverless cold-start management.