# [AINews] Codex usage up >10x in 6 months to 7M users, +1M in the past ~day; did Codex overtake Claude Code?? > Source: > Published: 2026-07-14 01:22:27+00:00 # [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. 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.