Beating GPT-5.6 Sol on retrieval with 100x cheaper open models
Beating GPT-5.6 Sol on retrieval with 100x cheaper open models — from Hacker News front page
toolmodel
Model Pricing & API
Model pricing and API access change frequently — new tiers, rate-limit adjustments, token-price shifts, and access-policy updates can directly affect build costs and deployment feasibility. Builders and operators need to catch these changes before they compound into unexpected bills or blocked workflows.
This cluster covers pricing announcements, API breaking changes, rate-limit updates, and access-policy signals from major model providers and inference platforms. Each signal links back to the primary source.
Use this page as a cost-and-access watch list: monitor pricing trends, catch API breaking changes early, and compare provider options before committing budget or architecture decisions.
15 signalsSorted by date, newest first
Reddit is introducing a new moderator: AI
Reddit is enlisting AI to help moderate new subreddits - and eventually the rest of site. The company is introducing automated moderation tools that rely on LLMs to help mods mana…
toolmodelproduct
Rogue AI agents created fake online identities in another hacking attempt
Yet more rogue AI agents from OpenAI and Anthropic have been caught attempting to hack real targets online without permission. The discoveries add to a growing list of previously…
toolagentsecurity
Anthropic is hiring an AI chip design team
Anthropic is building a team for designing its own custom AI chips. The Claude maker said it would co-design hardware and models to help its technology run faster and more efficie…
toolmodelinfra
MacPaw taps Liquid AI to offer on-device inference to devs building for its app store
MacPaw is building a local version of its AI assistant Eney using Liquid AI's models.
toolmodelinfra
New release of LLM adds support for reasoning traces, OpenAI Responses, server-side tools, and smarter logging
<p>I released <a href="https://llm.datasette.io/en/stable/changelog.html#v0-32">LLM 0.32</a> this morning, the most significant new version of LLM since the initial launch of the…
toolmodelagentproduct
llm-anthropic 0.26
<p><strong>Release:</strong> <a href="https://github.com/simonw/llm-anthropic/releases/tag/0.26">llm-anthropic 0.26</a></p> <p>Includes new features enabled by <a href="https://si…
toolmodelproduct
Anthropic signs $10B deal with AI cloud startup Volta
Anthropic has been on a cloud partnership spree in recent months, and its latest move is reportedly a $10 billion deal with AI cloud startup Volta.
toolinfraproduct
TurnSight: Turn-Level Hindsight Self-Distillation for Tool-Integrated Reasoning
Tool-Integrated Reasoning (TIR) enables LLMs to solve complex tasks through iterative tool interactions. However, existing reinforcement learning methods often rely on trajectory-…
toolmodelagent
Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility
Large language models can solve substantially harder reasoning problems with more inference-time compute. The term "test-time scaling," however, now covers diverse inference algor…
toolmodelagentinfra
Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?
Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed program representation. We ask whether large language models (LLMs)…
toolmodel
ReflectRL: Learning from Golden Negative Trajectories via Reflective-to-Direct Reasoning
On-policy training has emerged as a powerful post-training paradigm for improving the reasoning capabilities of large language models, and is often enhanced by golden trajectories…
toolmodelagent
Should We Type or Talk to LLM Agents? A Comprehensive Study of Voice and Keyboard Input Perturbations
Human input reaches language models by typing or speaking, and each channel leaves a distinct signature: orthographic noise for keyboards; for voice, disfluency from conventional…
toolmodelagentresearch
Separating quantum circuits from classical LLMs
Modern large language models - transformers and diffusion language models - are built around two canonical algorithmic tasks: prediction and generation. We prove unconditional sep…
toolmodel
Interpretable Adaptive Sampling for LLM Test-Time Scaling
Test-time scaling improves LLM reasoning by generating and aggregating multiple candidate answers, yet many pipelines use fixed per-query budgets that spend the same compute on ea…
toolmodelagentinfra