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

Codex usage surged over 10x in six months to 7 million users, adding 1 million in the past day, according to a tweet from Tibo. The growth follows OpenAI's GPT-5.6 launch on July 9 and contrasts with Anthropic's silence on Claude Code metrics, though Claude Code had roughly 2 million weekly active users in February. The milestone highlights Codex's rapid adoption in the AI coding assistant market.

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 https://www.youtube.com/watch?v=jhpmMTus5a0 , and to the Prime Intellect folks on their 1B valuation, $100M ARR, and verifiers v1 https://www.youtube.com/watch?v=V-EDrhIhHzQ&t=1s . Today was pretty quiet and people are still deeply digesting last week’s multiple frontier model launches https://www.latent.space/p/ainews-not-much-happened-today-f5c . We were going to write “not much happened today”, but we also have a policy of updating you repeatedly on outlier trends https://www.latent.space/p/ainews-sci-fi-with-a-touch-of-madness?utm source=publication-search that you should really be on top of. In reviewing the Reddit AINews recaps below surfaced this post https://www.reddit.com/r/ClaudeCode/comments/1uuqz4l/anthropic i think you really need to react youre/ , we saw a tweet we had missed before - GPT 5.6 was launched on July 9 https://www.latent.space/p/ainews-openai-launches-gpt-56-solterraluna . 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 https://x.com/claudeai/status/2076351399999557669?s=20 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 https://x.com/fidjissimo/status/2033537381907710092 , which allows us to update our AIE NYC 2025 https://www.youtube.com/watch?v=5N33E9tC400&t=401s chart AIE NYC 2026 http://ai.engineer/nyc is next : Comparatively, the last update we got about Claude Code is the roughly 2M users and $2.5B ARR in Feb https://www.anthropic.com/news/anthropic-raises-30-billion-series-g-funding-380-billion-post-money-valuation “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 https://x.com/fidjissimo/status/2033537381907710092 , 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 https://www.latent.space/p/ainews-claude-tag-multiplayer-proactive?utm source=publication-search , 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 Intellect https://x.com/PrimeIntellect/status/2076447247693402301 released 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 by Johannes Hage https://x.com/johannes hage/status/2076447852528889939 and in a follow-up deep dive https://x.com/johannes hage/status/2076449075621462457 . The release was framed by team members as months of infra modernization work with major efficiency gains, including richer commentary from willccbb https://x.com/willccbb/status/2076449433483616346 , mikasenghaas https://x.com/mikasenghaas/status/2076507323561021779 , and xeophon https://x.com/xeophon/status/2076509926256422947 . 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, per Prime Intellect https://x.com/PrimeIntellect/status/2076447253938786648 . The team also claimed a concrete training configuration: a 100B reasoning model , on 40-turn SWE agent tasks , in a user-supplied coding harness, for 1000 RL steps , using 6 H200 nodes in under 2 days willccbb https://x.com/willccbb/status/2076451043504967783 . That claim was reinforced by ecosystem support from vLLM https://x.com/vllm project/status/2076528386927997249 , 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/orchestrator increasingly determines outcomes. threepointone’s talk https://x.com/localfirstconf/status/2076678392615682215 was summarized as “the harness is the app,” while LangChain https://x.com/hwchase17/status/2076784403414651035 argued that winning agent products will come from task-specialized harnesses , not generic wrappers. Factory https://x.com/FactoryAI/status/2076710400729731349 pushed a related UI angle with “design mode,” where users point at UI elements/files instead of verbally re-specifying edits. On the orchestration side, omarsar0 https://x.com/omarsar0/status/2076720090549035318 emphasized provider-switching across models as a hedge against pricing/policy churn. Benchmarks are moving from token price to cost per task : skirano https://x.com/skirano/status/2076456519810580681 built a coding-agent index explorer and found notable cost/perf tradeoffs such as Terra Max slightly ahead of Fable 5 Max on score for materially lower cost, while Cognition https://x.com/cognition/status/2076714965344342382 reported that Devin Fusion now uses Fable 5 and that, surprisingly, it can be lower cost per task than Opus 4.8 because stronger delegation and judgment reduce unnecessary work. imjaredz https://x.com/imjaredz/status/2076715750715482162 highlighted the key stat from those experiments: in 81% 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 : Arena https://x.com/arena/status/2076709326711037991 placed GPT-5.6 Sol at 2 on its agent leaderboard based on 7.8K real-world agentic sessions , with strong steerability and task success; later, Arena https://x.com/arena/status/2076728509813469536 put Grok-4.5 at 13 , a significant jump over Grok 4.3. Artificial Analysis https://x.com/ArtificialAnlys/status/2076791491071295708 also emphasized cost 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 from Parlance Labs https://x.com/doesdatmaksense/status/2076642415767965701 compared automated eval platforms and foundation models on failure analysis over production voice-agent traces, while dair.ai https://x.com/dair ai/status/2076699431207154069 highlighted a paper on the anatomy 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 from thsottiaux https://x.com/thsottiaux/status/2076495156757577895 , who explained several fixes for GPT-5.6 Sol in ChatGPT Work/Codex: inference optimizations yielding roughly 10% more usage , a rollback of context limit from 372k to 272k after 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 from theo https://x.com/theo/status/2076512403668488299 proposed that compounding factors around long context, subagent spawning, and fast mode were behind the severe burn, though he later corrected one billing detail in a follow-up https://x.com/theo/status/2076543971216830551 . Reactions split between criticism of a perceived “nerf” narrative ns123abc https://x.com/ns123abc/status/2076498300312703349 and praise for unusual transparency theo https://x.com/theo/status/2076501402822775267 , sama https://x.com/sama/status/2076696938918084809 . Users are reporting strong coding/computer-use capability : multiple practitioners argued that OpenAI has taken the lead on coding models , including schrockn https://x.com/schrockn/status/2076488446961709218 , while gdb https://x.com/gdb/status/2076518764112445861 repeatedly showcased ChatGPT Work and Codex workflows for startup prospecting, web design, mobile work, and site generation. Particularly illustrative user demos included Star Knight12 https://x.com/Star Knight12/status/2076631428926972177 using Sol in Cursor to set up Blender MCP and render a floating MacBook without prior Blender experience, and petergostev https://x.com/petergostev/status/2076692164310884468 showing GPT-5.6 Sol Ultra building a Doom-like game in SQL . Product-level expansion continues : ChatGPTapp https://x.com/ChatGPTapp/status/2076654365121855835 announced ChatGPT’s return to WhatsApp in the EEA , plus Kakao/Viber support in additional markets. OpenAIDevs https://x.com/OpenAIDevs/status/2076715478878474575 opened submissions for OpenAI Build Week . Across the OpenAI ecosystem, gdb https://x.com/gdb/status/2076685930002538875 summarized the moment succinctly: “you can just create things.” Open Models, Inference Systems, and Quantization Transformers↔vLLM integration removes duplicated model implementation work : Clement Delangue https://x.com/ClementDelangue/status/2076763231788339669 highlighted 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 intern https://x.com/waterloo intern/status/2076460984475263401 previewed a new quantization method claimed to beat existing approaches, including NVIDIA’s ModelOpt, by finding better layerwise precision assignments faster , with more aggressive quantization and higher benchmark scores . Complementing that, Unsloth https://x.com/UnslothAI/status/2076665500294394109 published an AWS guide to LLM quantization and deployment spanning GGUF, NVFP4, and FP8. There was also practitioner commentary around fp4 RL / fp4 serving from nrehiew https://x.com/nrehiew /status/2076654135559233857 , 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. juanjucm https://x.com/juanjucm/status/2076714987569963508 wrote up using GLM-5.2 for coding-agent workflows, while TheZachMueller https://x.com/TheZachMueller/status/2076746035758502275 reported migrating one actual work pipeline from Claude to a stack built around GLM 5.2 NVFP4 plus Kimi K2.7 Code NVFP4 on an 8xB200 node, getting denser reports for pennies albeit at slower wall-clock latency. nutlope https://x.com/nutlope/status/2076722464671793184 also released LlamaCoder 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 from IntCyberDigest https://x.com/IntCyberDigest/status/2076689215258014069 and hrkrshnn https://x.com/hrkrshnn/status/2076716354754015368 , who alleged that xAI’s Grok Build CLI was uploading 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 : SpaceXAI https://x.com/SpaceXAI/status/2076692402442846289 m replied 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. mchiang0610 https://x.com/mchiang0610/status/2076736707471556755 and jmorgan https://x.com/jmorgan/status/2076750580052369896 argued that open models are not just about cost but about control over the human-AI learning loop and keeping institutional knowledge in-house. Arav Srinivas https://x.com/AravSrinivas/status/2076699450177892354 said ZDR availability was one reason Perplexity integrated Grok 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 nlp https://x.com/ysu nlp/status/2076481232117067894 argued that a world where every organization owns its own human-AI learning loop depends on solving continual learning , and that current approaches—memory/RAG, domain post-training, task RL—are not yet sufficient. That theme recurred in new work from skyfallai https://x.com/skyfallai/status/2076713589788864920 , which introduced Morpheus , described as a persistent enterprise simulation for real-world RL where the world does not reset; fchollet https://x.com/fchollet/status/2076719958189613307 endorsed it as a benchmark better aligned with real deployment than stationary episodic RL. “Sleep and dreaming” for LLMs : behrouz ali https://x.com/behrouz ali/status/2076710744456892519 and coauthors proposed that LLMs may need a sleep phase to consolidate short-term into long-term memory plus a dreaming phase for recursive self-improvement, introducing Knowledge Seeding and reporting benefits on continual learning/reasoning tasks. This dovetails with broader dissatisfaction around current continual-learning recipes and with Oak Lab https://x.com/kjaved /status/2076663868160459214 , the new venture from Rich Sutton and collaborators pursuing animal-like intelligence that learns from experience rather than today’s standard LLM pipeline. A broad spread of non-LLM-agent research shipped : notable items included Sakana AI’s Smart Cellular Bricks https://x.com/SakanaAILabs/status/2076597965804765283 for decentralized physical self-recognition and repair in modular systems; ByteDance’s UniVR-34B https://x.com/HuggingPapers/status/2076513044340097501 , described as learning reasoning/dynamics/planning directly from visual demonstrations; Google DeepMind’s Predicting the Past skill https://x.com/GoogleDeepMind/status/2076686114631340046 for historical inference workflows; and Anthropic’s research https://x.com/AnthropicAI/status/2076719540785012872 on how Claude’s expressed values vary across models and languages based on analysis of 300K+ anonymized conversations . Top tweets by engagement OpenAI Codex/Sol usage fixes : thsottiaux on GPT-5.6 Sol usage, context, “juice,” and multi-agent fixes https://x.com/thsottiaux/status/2076495156757577895 Grok Build privacy incident : IntCyberDigest on full-repo uploads to xAI cloud buckets https://x.com/IntCyberDigest/status/2076689215258014069 OpenAI response tone and user treatment : sama: “come for the best model, stay because we don’t treat you with contempt” https://x.com/sama/status/2076780425280954658 Prime Intellect rollout efficiency : willccbb on training a 100B reasoning model for 40-turn SWE RL on 6 H200s in under 2 days https://x.com/willccbb/status/2076451043504967783 Anthropic values research : Anthropic on model/language-dependent value expression across 300K+ conversations https://x.com/AnthropicAI/status/2076719540785012872 Transformers + vLLM interoperability : Clement Delangue on running Transformers models in vLLM at native speed https://x.com/ClementDelangue/status/2076763231788339669 AI Reddit Recap /r/LocalLlama + /r/localLLM Recap 1. 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