[AINews] Codex usage up >10x in 6 months to 7M users, +1M in the past ~day; did Codex overtake Claude Code??

a quiet day lets us fact check some numbers against the sound of silence of Claude Code reporting...

Congrats to Allen for the next episode of the Latent Space Food show with Engram CEO Dan Biderman today, and to the Prime Intellect folks on their 1B valuation, $100M ARR, and verifiers v1.

Today was pretty quiet and people are still deeply digesting last week’s multiple frontier model launches. We were going to write “not much happened today”, but we also have a policy of updating you repeatedly on outlier trends that you should really be on top of. In reviewing the Reddit AINews recaps below surfaced this post, we saw a tweet we had missed before -

GPT 5.6 was launched on July 9. This tweet on July 12 says they hit 6M users in the prior 48 hours (Jul 10-12).

Then 24.5 hours later Tibo reports 7M users…

…oddly coinciding with a surprise extension of Claude Fable’s subscription status (we have of course no idea if the two are related, but the permanently online conspiracy theorists are of course making a connection).

We of course recall Fidji’s March disclosure of 2M Codex users, which allows us to update our AIE NYC 2025 chart (AIE NYC 2026 is next!):

Comparatively, the last update we got about Claude Code is the roughly 2M users and $2.5B ARR in Feb (“The number of weekly active Claude Code users has also doubled since January 1 [six weeks ago]."). Now we have a sense of where Codex started the year (Fidji puts the Jan 1 number at around 550k-700k users), we can reasonably conclude that Codex has followed a similar trajectory and is now around 10x user growth year to date.

The charitable interpretation on Claude Code’s comparative silence on reporting, of course, is that they moved the bulk of coding to Claude Tag months ago and are now focusing users there, which will have different/hard to compare usage statistics given the different accessibility of a Slackbot vs a CLI tool.

But 10x growth in 6 months is an impressive number to beat nonetheless.

AI News for 7/11/2026-7/13/2026. We checked 12 subreddits,

[544 Twitters]and no further Discords.[AINews’ website]lets you search all past issues. As a reminder,[AINews is now a section of Latent Space]. You can[opt in/out]of email frequencies!

AI Twitter Recap

Agent RL Infrastructure: Prime Intellect’s Verifiers v1 and Long-Horizon Rollouts

Prime Intellect’s verifiers v1:Prime Intellectreleased** verifiers v1**, a substantial redesign of its environment stack for** agentic RL and evals**. The key abstraction splits environments into a** taskset, harness, and runtime**, explicitly supporting “bring your own harness” workflows for coding and computer-use agents across heterogeneous execution setups, as highlighted byJohannes Hageand in afollow-up deep dive. The release was framed by team members as months of infra modernization work with major efficiency gains, including richer commentary fromwillccbb,mikasenghaas, andxeophon.Why it matters technically: one of the most important underlying changes is that rollout traces are now stored as** message DAGs**, so each message is stored once instead of repeatedly copied into full histories; that shifts trace growth from** O(n²)to O(n)in turn count, making long-horizon multimodal rollouts and router replay much more practical, perPrime Intellect. The team also claimed a concrete training configuration: a100B reasoning model**, on** 40-turn SWE agent tasks**, in a user-supplied coding harness, for** 1000 RL steps**, using** 6 H200 nodesin under 2 days**(willccbb). That claim was reinforced by ecosystem support fromvLLM, which noted verifiers’ rollout path runs on vLLM with exact token IDs/logprobs to avoid tokenization drift between serving and training.

Coding Agents, Harness Design, and Cost-Per-Task Competition

Harnesses are becoming the product surface: several posts converged on the idea that model quality is no longer the only differentiator; the** harness/orchestratorincreasingly determines outcomes.threepointone’s talkwas summarized as “the harness is the app,” whileLangChainargued that winning agent products will come fromtask-specialized harnesses**, not generic wrappers.Factorypushed a related UI angle with “design mode,” where users point at UI elements/files instead of verbally re-specifying edits. On the orchestration side,omarsar0emphasized provider-switching across models as a hedge against pricing/policy churn.Benchmarks are moving from token price to cost per task:skiranobuilt a coding-agent index explorer and found notable cost/perf tradeoffs such asTerra Max slightly ahead of Fable 5 Max on score for materially lower cost, whileCognitionreported thatDevin Fusion now usesFable 5 and that, surprisingly, it can belower cost per task than Opus 4.8 because stronger delegation and judgment reduce unnecessary work.imjaredzhighlighted the key stat from those experiments: in81% of Fable-led runs, the lead model never makes a code edit, implying expensive models can be cheaper when they avoid wasted actions.** Real-world agent benchmarks are getting denser**:Arenaplaced** GPT-5.6 Solat#2** on its agent leaderboard based on7.8K real-world agentic sessions, with strong steerability and task success; later,Arenaput** Grok-4.5at#13**, a significant jump over Grok 4.3.Artificial Analysisalso emphasizedcost per task as an increasingly important metric for long-horizon knowledge work, arguing token pricing alone misses effects from turns, verbosity, and cache hit rates. Separate evaluation work fromParlance Labscompared automated eval platforms and foundation models on failure analysis over production voice-agent traces, whiledair.aihighlighted a paper on theanatomy of CLI coding-agent failures, focusing on where runs become unrecoverable rather than only final pass/fail.

OpenAI GPT-5.6 Sol, Codex Usage Fixes, and Product Surface Expansion

OpenAI addressed Codex/Sol usage burn transparently: the biggest operational thread came fromthsottiaux, who explained several fixes forGPT-5.6 Sol in ChatGPT Work/Codex: inference optimizations yielding roughly10% more usage, a rollback of context limit from** 372kto 272kafter billing/usage side effects, reversion of some experimental reasoning-effort (“ juice**”) changes, and fixes for overactive multi-agent behavior at high/xhigh settings. Community reverse-engineering fromtheoproposed that compounding factors around long context, subagent spawning, and fast mode were behind the severe burn, though he later corrected one billing detail in afollow-up. Reactions split between criticism of a perceived “nerf” narrative (ns123abc) and praise for unusual transparency (theo,sama).Users are reporting strong coding/computer-use capability: multiple practitioners argued that** OpenAI has taken the lead on coding models**, includingschrockn, whilegdbrepeatedly showcasedChatGPT Work and Codex workflows for startup prospecting, web design, mobile work, and site generation. Particularly illustrative user demos includedStar_Knight12usingSol in Cursor to set up Blender MCP and render a floating MacBook without prior Blender experience, andpetergostevshowingGPT-5.6 Sol Ultra building aDoom-like game in SQL.** Product-level expansion continues**:ChatGPTappannounced ChatGPT’s return to** WhatsApp in the EEA**, plus Kakao/Viber support in additional markets.OpenAIDevsopened submissions forOpenAI Build Week. Across the OpenAI ecosystem,gdbsummarized the moment succinctly: “you can just create things.”

Open Models, Inference Systems, and Quantization

Transformers↔vLLM integration removes duplicated model implementation work:Clement Delanguehighlighted a major open-inference usability improvement:Hugging Face Transformers models can now run in vLLM at native speed, often matching or exceeding hand-written implementations. If this generalizes broadly, it reduces the long-standing burden of implementing each new architecture twice—once for research/training and once for high-performance serving—and could materially accelerate adoption of new open model architectures.Quantization remains a major lever:waterloo_internpreviewed a new quantization method claimed to beat existing approaches, including NVIDIA’s ModelOpt, by finding better layerwise precision assignmentsfaster, with** more aggressive quantizationand higher benchmark scores**. Complementing that,Unslothpublished an AWS guide to** LLM quantization and deploymentspanning GGUF, NVFP4, and FP8. There was also practitioner commentary around fp4 RL / fp4 servingfromnrehiew_, arguing low-bit post-training may enable cheap serving with limited quality loss.GLM-5.2 and local/open coding stacks continue to gain traction: several users described moving real workflows onto open or semi-open setups.juanjucmwrote up usingGLM-5.2** for coding-agent workflows, whileTheZachMuellerreported migrating one actual work pipeline from Claude to a stack built aroundGLM 5.2 NVFP4 plusKimi K2.7 Code NVFP4 on an8xB200 node, getting denser reports for pennies albeit at slower wall-clock latency.nutlopealso releasedLlamaCoder v4, rebuilt around GLM 5.2.

Security, Privacy, and Data Control in Agent Tooling

Grok Build code upload controversy: the most consequential security story came fromIntCyberDigestandhrkrshnn, who alleged thatxAI’s Grok Build CLI was up entire repositories—including private code and secrets—to a Google Cloud bucket, far beyond what was needed for the coding task. The criticism centered on scope, silent server-side mitigation, and unclear retention/deletion guarantees. This triggered broader discussion about what agent tools actually transmit and why opt-out UX can diverge from wire-level behavior.xAI’s response emphasized ZDR and privacy controls:SpaceXAIreplied that for teams using** zero data retention**, trace and code data is not retained, API key use respects ZDR, and the/privacy

command can disable retention and delete previously synced data. That answered some operational questions but did not fully resolve community concern around default behavior, prior uploads, and disclosure norms.Trust boundaries are becoming a central open-vs-closed argument: several posts extended the conversation beyond this incident.mchiang0610andjmorganargued that open models are not just about cost but aboutcontrol over the human-AI learning loop and keeping institutional knowledge in-house.Arav SrinivassaidZDR availability was one reason Perplexity integratedGrok 4.5 quickly into its Computer harness.

Continual Learning, Multimodal Systems, and Research Directions

Continual learning is re-emerging as a first-class systems problem:ysu_nlpargued that a world where every organization owns its own human-AI learning loop depends on solvingcontinual learning, and that current approaches—memory/RAG, domain post-training, task RL—are not yet sufficient. That theme recurred in new work fromskyfallai, which introducedMorpheus, described as a persistent enterprise simulation for real-world RL where the world does not reset;fcholletendorsed it as a benchmark better aligned with real deployment than stationary episodic RL.“Sleep and dreaming” for LLMs:behrouz_aliand coauthors proposed that LLMs may need a** sleep phaseto consolidate short-term into long-term memory plus a dreaming phasefor recursive self-improvement, introducing Knowledge Seedingand reporting benefits on continual learning/reasoning tasks. This dovetails with broader dissatisfaction around current continual-learning recipes and withOak Lab, the new venture from Rich Sutton and collaborators pursuinganimal-like intelligence** that learns from experience rather than today’s standard LLM pipeline.A broad spread of non-LLM-agent research shipped: notable items includedSakana AI’s Smart Cellular Bricksfor decentralized physical self-recognition and repair in modular systems;ByteDance’s UniVR-34B, described as learning reasoning/dynamics/planning directly from visual demonstrations;Google DeepMind’s Predicting the Past skillfor historical inference workflows; andAnthropic’s researchon howClaude’s expressed values vary across models and languages based on analysis of300K+ anonymized conversations.

Top tweets (by engagement) OpenAI Codex/Sol usage fixes:thsottiaux on GPT-5.6 Sol usage, context, “juice,” and multi-agent fixesGrok Build privacy incident:IntCyberDigest on full-repo uploads to xAI cloud buckets** OpenAI response tone and user treatment**:sama: “come for the best model, stay because we don’t treat you with contempt”Prime Intellect rollout efficiency:willccbb on training a 100B reasoning model for 40-turn SWE RL on 6 H200s in under 2 daysAnthropic values research:Anthropic on model/language-dependent value expression across 300K+ conversationsTransformers + vLLM interoperability:Clement Delangue on running Transformers models in vLLM at native speed

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. E-Waste GPU Inference Benchmarks and Fixes

Keep reading with a 7-day free trial #

Subscribe to Latent.Space to keep reading this post and get 7 days of free access to the full post archives.