China open model week kept going with MiniMax M2.5 claiming an Opus-matching 80.2% on SWE-Bench Verified, however, as often happens on Thursdays, all 3 leading US labs had updates - Anthropic closed their $380B round confirming a historic >10xing of revenue to $14B as of today (remember in August Dario projected $10B), with Claude Code’s ARR doubling, hitting 2.5B year to date. Not to be outdone, OpenAI rolled out their answer to Claude’s fast mode (2.5x speedup) with GPT-5.3-Codex-Spark, which delivers >1000 tok/s (10x speedup), an impressively fast turnaround of the Cerebras deal.

All fantastic news, but we give the title story to the new Gemini 3 Deep Think today, and Jeff Dean dropped by the studio to give an update on the general state of GDM:

This is the same model that scored that IMO Gold last summer, and is simultaneously the #8 best Codeforces programmer in the world and helping new semiconductor research, but perhaps most impressive is that it reaches new SOTA levels (eg on ARC-AGI-2) while also being very efficient - 82% cheaper per task - something Jeff was very excited about in his pod.

AI Twitter Recap

Google DeepMind’s Gemini 3 Deep Think V2: benchmark jump + “science/engineering reasoning mode” shipping to users

• Deep Think V2 rollout + access paths: Google is shipping an upgraded Gemini 3 Deep Think reasoning mode to Google AI Ultra subscribers in the Gemini app, and opening a Vertex AI / Gemini API early access program for select researchers/enterprises (GoogleDeepMind, Google, GeminiApp, tulseedoshi). Multiple Googlers emphasized this is meant to be a productized test-time compute heavy mode rather than a lab-only demo (OriolVinyalsML, JeffDean, demishassabis, sundarpichai).

• Key reported numbers (and what’s notable about them):

  • ARC-AGI-2: 84.6% (promoted as new SOTA; independently certified/verified by the ARC community) (Google, arcprize, fchollet, scaling01).

  • Humanity’s Last Exam (HLE): 48.4% without tools (sundarpichai, _philschmid, JeffDean).

  • Codeforces Elo: 3455 (framed as “only ~7 humans” above it; discussion about “no tools” conditions and what that implies for evaluation) (scaling01, YouJiacheng, DeryaTR_).

  • Olympiad-level written performance in Physics/Chemistry (and references to IMO/ICPC history) (Google, NoamShazeer, demishassabis, _philschmid).

  • Cost disclosures for ARC: ARC Prize posted semi-private eval pricing like $13.62/task for ARC-AGI-2 and $7.17/task for ARC-AGI-1 (arcprize).

• Real-world “engineering” demos and claimed impact: Several posts push the message that Deep Think’s value is in practical scientific/engineering workflows: finding errors in math papers, modeling physical systems in code, optimizing semiconductor crystal growth, and even a sketch → CAD/STL pipeline for 3D printing (e.g., laptop stand and turbine-blade-esque components) (Google, Google, Google, GeminiApp, joshwoodward, tulseedoshi, OriolVinyalsML).

• ARC context / what “saturating ARC” means: François Chollet (ARC’s creator) both celebrated certification and later reiterated that ARC’s purpose is to steer research toward test-time adaptation / fluid intelligence, not to “prove AGI” (fchollet, fchollet). In a separate thread he defines “AGI” as the end of the human–AI gap and argues benchmarks must evolve until humans can no longer propose tasks where they outperform AI, with a rough expectation of ~2030 for that state (fchollet, fchollet).

Open coding/agent models shipping fast: MiniMax M2.5 + Zhipu’s GLM-5 battle for “best open agentic coder”

• MiniMax M2.5: distribution + positioning: MiniMax’s new model is pushed as an “agent-verse / long-horizon agent” model, rapidly appearing across aggregators and tools: OpenRouter (OpenRouterAI), Arena (arena), IDE/agents like Cline (cline), Ollama cloud free promo (ollama), Eigent agent scaffolds (Eigent_AI), Qoder (qoder_ai_ide), and Blackbox AI (blackboxai).

  • Benchmarks cited in the thread include claims like 80.2% SWE-Bench Verified and strong performance vs closed models in coding settings; multiple tweets stress throughput + cost as differentiators (e.g., 100 tokens/s and $0.06/M blended with caching are cited by Cline) (cline, cline, guohao_li, shydev69). Community vibe checks (e.g., Neubig) claim it’s one of the first open-ish coding models he’d seriously consider switching to for daily work (gneubig).

• GLM-5: model scale + infra hints + “open model leaderboards”:

  • Tooling ecosystem reports: GLM-5 is used on YouWare with a 200K context window for web projects (YouWareAI); one user reports ~14 tps on OpenRouter (scaling01).

  • A more detailed (but still third-party) technical summary claims GLM-5 is 744B params with ~40B active, trained on 28.5T tokens, integrates DeepSeek Sparse Attention, and uses “Slime” asynchronous RL infra to increase post-training iteration speed (cline). Another tweet nitpicks terminology confusion around attention components (eliebakouch).

  • Local inference datapoint: awnihannun reports running GLM-5 via mlx-lm on a 512GB M3 Ultra, generating a small game at ~15.4 tok/s using ~419GB memory (awnihannun).

  • Arena signal: the Arena account says GLM-5 is #1 open model in Code Arena (tied with Kimi) and overall #6, still ~100+ points behind Claude Opus 4.6 on “agentic webdev” tasks (arena).

  • A long Chinese-language-style analysis reposted via ZhihuFrontier argues GLM-5 improves hallucination control and programming fundamentals but is more verbose/“overthinks,” suggesting compute constraints (concurrency limits) show through (ZhihuFrontier).

OpenAI’s GPT-5.3-Codex-Spark: ultra-low-latency coding via Cerebras (and why UX becomes the bottleneck)

• Product announcement: OpenAI released GPT-5.3-Codex-Spark as a “research preview” for ChatGPT Pro users in the Codex app/CLI/IDE extension (OpenAI, OpenAIDevs). It’s explicitly framed as the first milestone in a partnership with Cerebras (also touted by Cerebras) (cerebras).

• Performance envelope:

  • The headline is “1000+ tokens per second” and “near-instant” interaction (OpenAIDevs, sama, kevinweil, gdb).

  • Initial capability details: text-only, 128k context, with plans for larger/longer/multimodal as infra capacity expands (OpenAIDevs).

  • Anecdotal reviews highlight a new bottleneck: humans can’t read/validate/steer as fast as the model can produce code, implying tooling/UX must evolve (better diffs, task decomposition, guardrails, “agent inboxes,” etc.) (danshipper, skirano).

• Model size speculation: There are community attempts to back-calculate size from throughput vs other MoEs; one estimate suggests ~30B active and perhaps 300B–700B total parameters (scaling01). Treat this as informed speculation, not an official disclosure.

• Adoption/availability: Sam Altman later says Spark is rolling to Pro; OpenAI DevRel notes limited API early access for a small group (sama, OpenAIDevs). There are also “Spark now with 100% of pro users” type rollout notes with infra instability caveats (thsottiaux).

Agent frameworks & infra: long-running agents, protocol standardization, and KV-cache as the new scaling wall

• A2A protocol as “agent interoperability layer”: Andrew Ng promoted a new DeepLearning.AI course on Agent2Agent (A2A), positioning it as a standard for discovery/communication across agent frameworks, mentioning IBM’s ACP joining forces with A2A and integration patterns across Google ADK, LangGraph, MCP, and deployment via IBM’s Agent Stack (AndrewYNg).

• Long-running agent harnesses are becoming product features:

  • Cursor launched long-running agents and explicitly ties it to a “new harness” that can complete larger tasks (cursor_ai).

  • LangChain folks discuss “harness engineering” research: forcing self-verification/iteration, automated context prefetch, and reflection over traces as levers that change outcomes materially (Vtrivedy10).

  • Deepagents added bring-your-own sandboxes (Modal/Daytona/Runloop) for safe code execution contexts (sydneyrunkle).

• Serving bottlenecks: KV cache & disaggregation:

  • PyTorch welcomed Mooncake into the ecosystem, describing it as targeting the “memory wall” in LLM serving with KVCache transfer/storage, enabling prefill/decode disaggregation, global cache reuse, elastic expert parallelism, and serving as a fault-tolerant distributed backend compatible with SGLang, vLLM, TensorRT-LLM (PyTorch).

  • Moonshot/Kimi highlighted Mooncake’s origins (Kimi + Tsinghua) and open-source trajectory (Kimi_Moonshot).

• A surprisingly common theme: “files as queues”: A viral thread describes a reliable distributed job queue using object storage + a queue.json (FIFO, at-least-once) as a minimalist primitive (turbopuffer). Another tweet claims Claude Code “agent teams” communicate by writing JSON files on disk, emphasizing “no Redis required” CLI ergonomics (peter6759).

Research notes: small theorem provers + label-free vision training + RL algorithms for verifiable reasoning

• QED-Nano: 4B theorem proving with heavy test-time compute: A set of tweets introduces QED-Nano, a 4B natural-language theorem-proving model that matches larger systems on IMO-ProofBench and uses an agent scaffold scaling to >1M tokens per proof, with RL post-training “rubrics as rewards.” They promise open-source weights and training artifacts soon ( _lewtun, _lewtun, setlur_amrith, aviral_kumar2).

• LeJEPA: simplifying self-supervised vision: NYU Data Science highlights LeJEPA (Yann LeCun + collaborators) as a simpler label-free training method that drops many tricks but scales well and performs competitively on ImageNet (NYUDataScience).

• Recursive/agentic evaluation discourse: Multiple tweets debate recursive language models (RLMs) and stateful REPL loops as a way to manage long-horizon tasks outside the context window (lateinteraction, deepfates, lateinteraction).

Top tweets (by engagement)

• Gemini 3 Deep Think upgrade + sketch→STL demo: @GeminiApp

• OpenAI Codex-Spark announcement: @OpenAI, @OpenAIDevs, @sama

• Anthropic funding/valuation: @AnthropicAI

• Gemini Deep Think “unprecedented 84.6% ARC-AGI-2”: @sundarpichai

• Simile launch + $100M raise; simulation framing: @joon_s_pk, @karpathy

AI Reddit Recap

/r/LocalLlama + /r/localLLM Recap

1. GLM-5 Model Launch and Benchmarks

• Unsloth just unleashed Glm 5! GGUF NOW! (Activity: 446): The image presents a benchmark comparison table for various AI models, highlighting the performance of GLM-5 against other models like GLM-4.7, DeepSeek-V3.2, Kimi K2.5, Claude Opus 4.5, Gemini 3.0 Pro, and GPT-5.2. The table categorizes performance into areas such as Reasoning, Coding, and General Agent, with GLM-5 showing particularly strong results in the Reasoning category. Additionally, the table provides a cost comparison, suggesting that GLM-5 offers competitive performance at a potentially lower cost. One comment humorously suggests the need for a data center to run these models, indicating the high computational requirements. Another comment questions the feasibility of running the model on a low-end GPU like the GT 710, highlighting concerns about accessibility and hardware demands.

  • A user inquired whether the new Glm 5 model requires any implementation changes in llama.cpp, suggesting that the model might be compatible without additional modifications. This could imply ease of integration for developers already using llama.cpp for other models.

  • Another user humorously questioned if the Glm 5 model could run on a GT 710 graphics card, which is known for its limited computational power. This highlights the potential hardware requirements and limitations for running such advanced models, suggesting that more powerful GPUs might be necessary.

  • The release of Glm 5 in GGUF format suggests a focus on optimized performance and compatibility. GGUF, being a format designed for efficient model storage and execution, indicates that Glm 5 might offer improved performance metrics or reduced resource consumption compared to previous versions.

• GLM-5 scores 50 on the Intelligence Index and is the new open weights leader! (Activity: 892): The image highlights the performance of GLM-5, which scores 50 on the Intelligence Index, positioning it as the leading model among open weights. This is significant as it surpasses other models like Opus 4.5 and GPT-5.2-xhigh, indicating a strong performance in AI evaluations. Notably, GLM-5 also has the lowest hallucination rate on the AA-Omniscience benchmark, showcasing its accuracy and reliability in generating outputs. The discussion suggests that open-source models are closing the gap with proprietary ones, with upcoming models like Deepseek-V4 expected to use similar architectures but on a larger scale. Commenters note the narrowing performance gap between open-source and closed-source models, with some anticipating further advancements in open-source AI capabilities.

  • GLM-5 is noted for having the lowest hallucination rate on the AA-Omniscience benchmark, which is a significant achievement in reducing errors in AI-generated content. This positions GLM-5 as a leader in accuracy among open-weight models, surpassing models like Opus 4.5 and GPT-5.2-xhigh.

  • The open-source AI community is rapidly closing the gap with closed-source models, now trailing by only about three months. This is evidenced by the upcoming release of DeepSeek v4, which will utilize the same DSA architecture as GLM-5 but on a larger scale, indicating a trend towards more powerful open-source models.

  • There is a desire within the community for transparency regarding the hardware requirements of these advanced models, as expressed by users who wish for detailed specifications, such as memory requirements, to be published alongside model announcements.

2. MiniMax M2.5 Release and Discussion

• MiniMaxAI MiniMax-M2.5 has 230b parameters and 10b active parameters (Activity: 436): OpenHands has announced the MiniMax-M2.5 model, which features 230 billion parameters with 10 billion active parameters. This model is noted for its competitive performance, ranking fourth in the OpenHands Index, and is significantly cost-effective, being 13 times cheaper than Claude Opus. It excels in software engineering tasks, particularly in app development and issue resolution, but has room for improvement in generalization tasks. The model is accessible for free on the OpenHands Cloud for a limited time, enhancing its accessibility for developers. Commenters are optimistic about the potential of the MiniMax-M2.5 model, with suggestions to integrate it with Cerebras technology for enhanced performance and efficiency, particularly for users with 128GB machines.

  • Look_0ver_There discusses the potential for a hybrid model using the MiniMax-M2.5’s architecture, suggesting that a ~160B REAP/REAM hybrid could be developed with minimal performance loss. They propose that such a model could be quantized to run efficiently on 128GB machines, allowing for deep-context tool use, which would be beneficial for users with limited hardware resources.

  • Rascazzione highlights the achievement of the MiniMax-M2.5 model, noting its efficiency compared to other models like GLM, which required doubling its parameters to evolve, and Kimi, which has 1T parameters. They emphasize that if the quality and size of MiniMax-M2.5 are confirmed, it represents a significant advancement in AI model development.

  • eviloni points out that with only 10b active parameters, the MiniMax-M2.5 should achieve decent speed even on non-high-end GPUs. They suggest that this performance could improve further with quantized versions, making the model more accessible to users without cutting-edge hardware.

• Minimax M2.5 Officially Out (Activity: 664): Minimax M2.5 has been officially released, showcasing impressive benchmark results: SWE-Bench Verified at 80.2%, Multi-SWE-Bench at 51.3%, and BrowseComp at 76.3%. The model is noted for its cost efficiency, with operational costs significantly lower than competitors like Opus, Gemini 3 Pro, and GPT-5. At 100 output tokens per second, the cost is $1 per hour, and at 50 TPS, it drops to $0.3, allowing for four instances to run continuously for a year at $10,000. More details can be found on the official Minimax page. Commenters highlight the potential game-changing nature of Minimax M2.5 due to its cost efficiency compared to other models, and there is anticipation for the release of open weights on platforms like Hugging Face.

  • The Minimax M2.5 is highlighted for its cost-effectiveness, with operational costs significantly lower than competitors like Opus, Gemini 3 Pro, and GPT-5. Specifically, running M2.5 at 100 tokens per second costs $1 per hour, and at 50 tokens per second, it costs $0.3 per hour. This translates to a yearly cost of $10,000 for four instances running continuously, which is a substantial reduction compared to other models.

  • There is anticipation for the release of open weights on Hugging Face, which would allow for broader experimentation and integration into various applications. This is a common expectation in the AI community for new models to facilitate transparency and reproducibility.

  • The potential impact of Minimax M2.5 on existing models like GLM 5.0 and Kimi 2.5 is discussed, with some users suggesting that if the benchmarks are accurate, M2.5 could surpass these models in popularity due to its ease of use and cost advantages. This could shift the landscape of preferred local models, as users currently favor models like Kimi 2.5 and DeepSeekv3.2.

• GLM 5.0 & MiniMax 2.5 Just Dropped, Are We Entering China’s Agent War Era? (Activity: 465): GLM 5.0 and MiniMax 2.5 have been released, marking a shift towards agent-style workflows in AI development. GLM 5.0 focuses on enhanced reasoning and coding capabilities, while MiniMax 2.5 is designed for task decomposition and extended execution times. This evolution suggests a competitive landscape moving from generating better responses to completing complex tasks. Testing plans include API benchmarks, multi-agent orchestration with Verdent, IDE workflows similar to Cursor, and infrastructure routing with ZenMux to evaluate their performance on long-duration tasks and repository-level changes. The comments highlight a broader context of AI development in China, mentioning other recent releases like Seedance 2.0 and Qwen-image 2.0, suggesting a vibrant and competitive AI ecosystem. There’s also a sentiment that this competition benefits end-users by driving innovation.

3. AI Model Identity and Community Concerns

• Why do we allow “un-local” content (Activity: 466): The post discusses the concern of ‘un-local’ content in a subreddit focused on local AI models, suggesting that posts linking to API resources should also include links to downloadable model weights, such as those on Hugging Face. The author argues that this would prevent the subreddit from becoming a platform for marketing rather than technical discussion. The debate centers on whether posts about models without released weights should be allowed, with some agreeing that such posts should be tied back to local relevance, even if the models are not immediately available for local use. The discussion highlights a need for a balance between maintaining the subreddit’s focus on local models and allowing discussions on potentially relevant advancements. Commenters generally agree with the need for a framework to prioritize ‘local’ content, but acknowledge the difficulty in drawing strict boundaries. Some suggest that posts about models with pending weight releases should be allowed if they are likely to become relevant to local use. The moderation team emphasizes the importance of staying true to the sub’s spirit rather than strictly adhering to its original intent, to keep the community active and relevant.

  • The discussion highlights a framework for determining the relevance of posts to a local-focused subreddit. It suggests that purely local content, such as running models on specific hardware and benchmarks, should be prioritized. However, posts about non-local models or breakthroughs should be allowed if they can be tied back to local implications, such as potential future applications or relevance to local models.

  • A consensus among moderators is mentioned, emphasizing the importance of allowing content that is adjacent or relevant to the local ecosystem. The discussion acknowledges the difficulty in drawing strict boundaries, as the relevance of certain models or announcements can vary. For instance, the announcement of Minimax M2.5 ahead of its weights release poses a challenge in determining its local relevance.

  • The moderation team has debated the balance between maintaining the subreddit’s original focus and adapting to current trends. They argue that strict adherence to the original intent could lead to the subreddit’s decline, as seen with the diminishing relevance of models like Llama. The focus is on maintaining the spirit of the subreddit rather than strict rules, allowing for flexibility in content relevance.

• GLM thinks its Gemini (Activity: 354): The image depicts a chat interface where a language model initially identifies itself as GLM-5 but then corrects itself to say it is actually Gemini, a large language model developed by Google. This raises questions about the model’s identity and the potential use of Gemini in either distilling GLM or generating synthetic data. The comments highlight a common issue where users ask language models to identify themselves, which they typically cannot do accurately due to context limitations. One comment suggests that the model’s response might be influenced by non-empty context, implying that the model’s identity confusion could be due to prior interactions or prompts.

  • NoobMLDude raises a technical inquiry about the relationship between GLM and Gemini, questioning whether GLM is distilled from Gemini outputs or if Gemini is used in generating synthetic data. This suggests a curiosity about the training processes and data sources involved in developing these models, which could impact their performance and capabilities.

Less Technical AI Subreddit Recap

1. AI Model Launches and Performance Comparisons

• Anthropic raises $30B, Elon crashes out (Activity: 4819): The image is a meme featuring a fictional tweet from Anthropic announcing a $30 billion funding round, valuing the company at $380 billion. This is a satirical take, as such a funding round and valuation are not real. The tweet humorously suggests that the funds will be used for research, product innovation, and infrastructure expansion. Elon Musk is depicted as responding critically, accusing Anthropic’s AI of being biased and labeling it as ‘misanthropic and evil,’ which is a play on words with the company’s name. This meme is likely a commentary on the competitive and sometimes contentious nature of AI development and funding, as well as Musk’s outspoken views on AI ethics and bias. The comments reflect a mix of confusion and humor, with one user questioning a reference to ‘Name of the Wind,’ a fantasy novel, suggesting it is unrelated to the topic. Another comment suggests that Musk’s response is a projection of his own insecurities, while a third implies jealousy on Musk’s part.

• Introducing Simile - The Simulation Company (Activity: 504): Simile has introduced an AI-based simulation platform designed to model societal behaviors and predict human actions at scale. The company has developed a foundation model that uses generative agents to simulate real people with high accuracy, allowing organizations to test decisions before implementation. This approach is already being utilized by companies for applications such as earnings call rehearsals and policy testing. Simile is supported by $100M in funding from notable investors including Index Ventures, Andrej Karpathy, and Fei-Fei Li. Commenters highlight the potential of Simile’s technology to revolutionize decision-making processes, comparing it to Asimov’s concept of Psychohistory. The involvement of prominent figures like Karpathy and Fei-Fei Li lends credibility to the project, suggesting it is not merely speculative.

  • Rare-Site highlights the contrast between the rigorous testing in software development, such as A/B testing for UI elements, and the often intuitive decision-making in economic policies. They emphasize the potential of Simile to revolutionize decision-making by simulating reality, especially with the backing of prominent figures like Karpathy and Fei-Fei Li. This could represent a significant advancement in AI capabilities.

  • EmbarrassedRing7806 raises a concern about the competitive landscape, questioning the ability of Simile to maintain a competitive advantage or ‘moat’. They reference a similar project, Aaru, suggesting that the field of simulation technology might be crowded or rapidly evolving, which could impact Simile’s unique positioning.

  • The_Scout1255 expresses surprise at the emergence of simulation technology this year, indicating that the development of such advanced simulation capabilities was unexpected in the current timeline. This suggests a rapid pace of innovation in the field, potentially driven by recent advancements in AI and computational power.

• Lead product + design at Google AI Studio promises “something even better” than Gemini 3 Pro GA this week (Activity: 626): The image captures a social media exchange where a lead from Google AI Studio hints at an upcoming release that is expected to surpass the anticipated Gemini 3 Pro GA. This suggests that Google may be preparing to unveil a new product or feature that could potentially include advanced capabilities, possibly related to coding agents, as speculated by users. The discussion reflects a high level of anticipation and excitement within the community for Google’s next move in AI development. One comment suggests that Google needs a product similar to Codex, as Gemini 3 Pro reportedly lacks effective agentic features. This indicates a demand for more advanced AI functionalities from Google.

  • Impressive-Zebra1505 highlights a critical gap in Google’s AI capabilities, noting that “Google needs something akin to Codex ASAP,” as Gemini 3 Pro struggles with agentic features. This suggests a potential area for improvement or innovation in Google’s AI offerings, particularly in enhancing the model’s ability to handle tasks autonomously, similar to OpenAI’s Codex.

  • Hemingbird discusses a New Yorker article that provides an in-depth look at Anthropic and its AI model, Claude. The article is praised for its nuanced understanding of AI, particularly in differentiating next-token prediction from simple autocomplete. It also explores the role of ‘AI psychonauts’ in model interpretability, highlighting the diverse and sometimes unconventional approaches to understanding AI behavior.

  • kvothe5688 speculates that the upcoming announcement from Google AI Studio might involve a “rumoured coding agent.” This aligns with the broader industry trend of integrating more sophisticated coding capabilities into AI models, potentially addressing the limitations noted in Gemini 3 Pro’s current functionalities.

• How is this not the biggest news right now? (Activity: 865): Google has developed a math-specialized version of its AI model, named Aletheia, which has achieved a perfect score on the International Mathematical Olympiad (IMO) and significantly outperforms other models on various benchmarks. The image shows Aletheia leading the leaderboard with a 91.9% score on the Advanced Proofbench and 100% on the IMO 2024 category, far surpassing other models like “GPT-5.2 Thinking (high)” and “Gemini 3 Pro.” This model is described as a generator-verifier agent, which may not directly compare to traditional language models, suggesting a different approach in its architecture and capabilities. Some commenters question the significance of this news, noting that achieving high scores on IMO with sufficient fine-tuning and resources is possible. Others highlight that Aletheia’s architecture as a generator-verifier agent makes it distinct from typical language models, suggesting that the leaderboard comparison might not be entirely fair.

  • Alex__007 highlights that both OpenAI and Google achieved gold at the International Mathematical Olympiad (IMO) with their models, suggesting that with sufficient fine-tuning and inference expenditure, such results are achievable. The commenter questions the generalization of these models beyond specific benchmarks and inquires about the accessibility and cost of using Aletheia, indicating a need for more transparency in these areas.

  • Faintly_glowing_fish points out that the model in question is a generator-verifier agent, which differs from traditional language models. This distinction implies that comparing its performance on leaderboards with standard language models might be misleading, as they serve different purposes and operate under different paradigms.

  • jjjjbaggg discusses the model’s focus and cost, suggesting it might be an iteration of Gemini Deepthink with extensive scaffold engineering and fine-tuning. They note that scaffold engineering can become obsolete as reinforcement learning (RL) techniques evolve, potentially eliminating the need for such scaffolding in future model generations.

• GLM 5 is out now. (Activity: 312): The image is a performance evaluation chart comparing several language models, including the newly released GLM-5, against others like GLM-4.7, Claude Opus 4.5, Gemini 3 Pro, and GPT-5.2 (xhigh). The chart highlights GLM-5’s strong performance across various benchmarks such as “SWE-bench Verified” and “t²-Bench,” indicating its competitive edge in these categories. The release of GLM-5 is emphasized by its highlighted position in the chart, suggesting improvements over its predecessor, GLM-4.7, and competitive performance against other leading models. One commenter criticizes the benchmarks for not reflecting real-life usage, while another highlights the cost-effectiveness and efficiency of models like Oppus 4.6 over GLM-5, suggesting that despite GLM-5’s performance, it may not be as practical for certain tasks.

  • SnooTangerines2270 highlights a critical performance issue with GLM 5, noting that while it may be cost-effective, it often leads to inefficient workflows characterized by repetitive ‘copy-paste-fix-it’ cycles. They contrast this with Oppus 4.6, which they claim offers superior performance by understanding user intent without extensive prompting, thanks to its advanced swarm agent capabilities. This suggests that for users prioritizing efficiency and time savings, Oppus 4.6 might be a more suitable choice despite its higher cost.

  • ianxiao criticizes the performance of GLM 5, stating that it operates at ‘unusable token/s’, implying that the model’s processing speed is insufficient for practical use. This suggests that despite any potential improvements or features, the model’s throughput may not meet the demands of users requiring fast and efficient processing.

  • stiky21 expresses a preference for Opus and Codex over GLM 5, indicating a possible perception of superior performance or reliability in these alternatives. This choice might reflect a broader sentiment among users who prioritize established models with proven track records over newer releases that may not yet have demonstrated their capabilities in real-world applications.

• Deepseek V4 is coming this week. (Activity: 385): Deepseek V4 is anticipated to release by February 17, coinciding with the Chinese New Year. The update reportedly includes the capability to handle 1 million tokens, suggesting a significant enhancement in processing capacity. This positions Deepseek as a competitive alternative to major models like Opus, Codex, and others, potentially offering similar capabilities at a reduced cost. One commenter highlights that Deepseek’s advancements make it a cost-effective alternative to major models, suggesting that China’s development in AI is competitive with global leaders.

  • A user mentioned that Deepseek has been updated to handle 1M tokens, suggesting a significant increase in its processing capabilities. This could imply improvements in handling larger datasets or more complex queries, which is a notable enhancement for users dealing with extensive data or requiring detailed analysis.

  • Another user reported that after the update, Deepseek provided a highly nuanced and original review of a complex piece of character writing. This suggests improvements in the model’s ability to understand and critique creative content, indicating advancements in its natural language processing and comprehension abilities.

  • One comment highlighted a perceived increase in the ‘personality’ of Deepseek’s responses post-update, comparing it to ChatGPT. This suggests enhancements in the model’s conversational abilities, potentially making interactions more engaging and human-like.

• MiniMax-M2.5 Now First to Go Live on NetMind (Before the Official Launch), Free for a Limited Time Only (Activity: 14): MiniMax-M2.5 is now available on the NetMind platform with first-to-market API access, free for a limited time. This model is designed for agents, supporting multilingual programming, complex tool-calling chains, and long-horizon planning. It surpasses Claude Opus 4.6 on SWE-bench Pro and Verified, making it one of the top models for software engineering. It also achieves state-of-the-art scores in Excel manipulation, deep research, and document summarization. With an output speed of approximately 100 TPS, it is about 3x faster than Opus-class models, and is priced at $0.3/M input tokens and $1.2/M output tokens, making it suitable for high-volume, always-on production workloads. A comment notes that despite the announcement, the service is paid, indicating potential user concerns about cost despite the initial free access.

2. AI in Medical Diagnosis and Healthcare

• This morning ChatGPT talked me out of toughing out a strain in my calf muscle and to go get it looked at because it suspected a blood clot. (Activity: 6516): The image and accompanying post highlight a real-life scenario where ChatGPT played a crucial role in prompting a user to seek immediate medical attention for a suspected blood clot. The user initially considered ignoring a calf muscle strain, but ChatGPT’s advice led them to discover a life-threatening condition involving multiple blood clots in the lungs. This incident underscores the potential of AI tools like ChatGPT in providing timely health advice, although it should not replace professional medical consultation. The comments further illustrate similar experiences where ChatGPT’s guidance led to the discovery of serious health issues, emphasizing its utility in preliminary health assessments. Commenters shared similar experiences where ChatGPT’s advice led to the discovery of serious health conditions, such as heart blockages and shingles, highlighting the AI’s potential in preliminary health diagnostics.

• gpt is goated as a doctor (Activity: 1219): The post discusses using ChatGPT for medical diagnosis by analyzing lab reports, claiming it accurately identified conditions like Crohn’s disease, fatty liver, and a tumor, suggesting follow-up tests that were later confirmed by doctors. This highlights GPT’s capability in medical pattern recognition, leveraging its training on extensive medical literature to perform sophisticated pattern matching against documented cases and clinical correlations. It excels in the differential diagnosis phase, suggesting potential diagnoses and tests, but should be used as a diagnostic assistant rather than a replacement for doctors. Comments emphasize GPT’s role as a second opinion tool, enhancing patient-doctor interactions by enabling informed discussions. However, caution is advised as GPT provides confident answers based on pattern matching, not true diagnosis. The potential for AI integration in healthcare workflows is noted, suggesting it could improve diagnostic efficiency and patient outcomes.

  • BookPast8673 highlights the effectiveness of GPT in medical pattern recognition due to its training on extensive medical literature and case studies. It excels in differential diagnosis by matching symptoms and data points against a vast database of documented cases, which allows it to recall rare conditions and drug interactions quickly. However, it is emphasized that GPT should be used as a diagnostic assistant rather than a replacement, as it can suggest tests but cannot interpret the full clinical picture or patient history.

  • BookPast8673 also discusses the potential for AI integration into healthcare systems, suggesting that AI could act as a co-pilot for doctors by flagging potential diagnoses and suggesting follow-up tests in real-time. This integration could reduce diagnostic delays and unnecessary testing, ultimately saving time and money while improving patient outcomes. The comment underscores the importance of AI as a tool to enhance, rather than replace, human medical expertise.

3. Gemini 3 Deep Think and ARC-AGI-2 Benchmarks

• The new Gemini Deep Think incredible numbers on ARC-AGI-2. (Activity: 1286): The image presents a bar graph illustrating the performance of various AI models on the ARC-AGI-2 benchmark, with the Gemini 3 Deep Think model achieving a leading score of 84.6%. This score significantly surpasses other models like Claude Opus 4.6 (68.8%), GPT-5.2 (52.9%), and Gemini 3 Pro Preview (31.1%). The Gemini 3 Deep Think’s performance is particularly notable as it approaches the threshold for effectively solving the benchmark under the ARC Prize criteria. Additionally, the model’s Codeforces Elo rating of 3455 places it in the top 0.008% of human competitors, highlighting its advanced capabilities in reasoning and knowledge without the use of tools. Commenters are impressed by the significant performance leap of the Gemini 3 Deep Think model, noting its potential breakthrough in AI capabilities. The model’s high Codeforces Elo rating is also highlighted as a remarkable achievement, indicating its superior problem-solving skills.

  • FundusAnimae highlights the significant performance improvement of the Gemini Deep Think model on the ARC-AGI-2 benchmark, noting that it scores above 85%, which is considered effectively solving the benchmark according to the ARC Prize criteria. The model’s Codeforces Elo rating of 3455 places it in the top 0.008% of human competitors, which is particularly impressive given that it achieved this without any tools.

  • Agreeable_Bike_4764 points out the rapid progress of the ARC-AGI-2 model, noting that it took less than a year to reach a performance level considered as ‘saturation’ (85% solved) since its release. This suggests a fast-paced development and improvement cycle in AI model capabilities.

• Google upgraded Gemini-3 DeepThink: Advancing science, research and engineering (Activity: 674): Google’s Gemini-3 DeepThink has set a new benchmark in AI performance, achieving 48.4% on Humanity’s Last Exam without tools, 84.6% on ARC-AGI-2 as verified by the ARC Prize Foundation, and an Elo rating of 3455 on Codeforces. It also reached gold-medal level performance in the International Math Olympiad 2025. These results highlight its advanced capabilities in reasoning and problem-solving across scientific domains. For more details, see the original article. A notable debate in the comments revolves around the comparison of Gemini-3 DeepThink to GPT 5.2, with some users pointing out that the comparison should be made with GPT 5.2 Pro, which is a more direct competitor.

  • SerdarCS points out a potential issue with the comparison metrics used by Google, noting that they are comparing Gemini-3 DeepThink to GPT-5.2 Thinking instead of GPT-5.2 Pro, which would be a more direct competitor. This suggests a possible bias in the benchmarking process, as the Pro version might offer different performance characteristics that are more aligned with Gemini-3’s capabilities.

  • brett_baty_is_him inquires about specific benchmarks related to Gemini-3 DeepThink, particularly focusing on Software Engineering (SWE) benchmarks and long context benchmarks. This indicates a need for detailed performance metrics to evaluate the model’s capabilities in handling complex engineering tasks and extended context scenarios, which are critical for assessing its utility in technical applications.

  • verysecreta expresses confusion over the naming conventions used for Gemini-3 DeepThink, comparing it to other models like “Flash” and “Pro”. The comment highlights the ambiguity in distinguishing whether “Deep Think” is a separate model or a mode within the existing Gemini framework. This reflects a broader issue in AI model branding and clarity, which can impact user understanding and adoption.

• Google Just Dropped Gemini 3 “Deep Think” : and its Insane. (Activity: 844): Google has released Gemini 3 ‘Deep Think’, an advanced AI model noted for its exceptional capabilities in reasoning, coding, and science, comparable to Olympiad-level performance. It is already being applied in practical scenarios, such as semiconductor material design at Duke University. The model has also achieved a new benchmark by solving PhD-level math and physics problems, showcasing its potential in academic and research settings. Image Some users express concern over the high cost of accessing Gemini 3, which is priced at $270 per month with a limit of 10 messages per day, suggesting that its use may be restricted to those who can afford such a premium service.

  • TechNerd10191 highlights the restrictive nature of Gemini 3’s pricing model, which costs $270 per month and limits users to 10 messages per day. This is contrasted with ChatGPT Pro, which offers 100+ messages on its 5.2 Pro version, suggesting a significant limitation for users who require extensive interaction with the model.

  • NervousSWE raises concerns about the practicality of using Gemini 3 for coding due to the 10 messages a day limit. They speculate on the efficiency of the model, suggesting that if one message with Gemini 3 can achieve what would take 10 messages with other models, it might still be viable for power users. This highlights a potential strategy for maximizing the limited interactions by focusing on complex, high-value queries.

  • blondbother compares Gemini 3’s offering with ChatGPT Pro, noting that the latter provides 100+ messages per day on its 5.2 Pro version. This comparison underscores the limitations of Gemini 3’s 10 queries a day policy, which may deter users who need more frequent access, especially when considering the high subscription cost.

AI Discord Recap

1. GLM-5 Model Release & Ecosystem Momentum

• GLM-5 Grabs the Gold (Twice): GLM-5 hit #1 among open models on both the Text Arena leaderboard (score 1452, on par with gpt-5.1-high) and the Code Arena leaderboard, with Arena also pointing to Peter Gostev’s review of GLM-5 and MiniMax-M2.5.

  • Engineers debated whether GLM-5 tilts more agentic than “general assistant” (similar comparisons to MiniMax), and a separate thread noted chat.deepseek.com “silently” feels different with no official announcement, sharpening interest in independent evals.

• GGUF Goes Brrr: GLM-5 Runs Local: Unsloth shipped GLM-5 GGUFs plus a local llama.cpp guide via their post and the weights at unsloth/GLM-5-GGUF.

  • One user reported 46 t/s with 3× Nvidia Blackwell RTX 6000 GPUs, kicking off practical discussion about real-world throughput and whether GLM-5’s tuning targets longer-horizon tool use over chat polish.

2. Agentic Coding: Speed, Long-Running Agents, and New Leaderboards

• Codex Spark Lights the Fuse (1000 tok/s): OpenAI launched GPT-5.3-Codex-Spark in research preview with an official post, “Introducing GPT‑5.3 Codex Spark”, plus a video demo and example CLI usage like codex -m gpt-5.3-codex-spark --yolo -c model_reasoning_effort="xhigh".

  • Cursor users highlighted Cerebras-backed speed (”the speed is just a whole new level!“), while also stressing that the real shock is fast deployable code changes, not just token throughput.

• Cursor Lets Agents Run Wild (…and Bills TBD): Cursor shipped long-running agents, and users poked around pricing/limits via dev tools on cursor.com/dashboard while also debating Composer 1.5 pricing (reports like $3.5 input / $17.5 output in some views).

  • The vibe split between excitement (“HOW I LET CURSOR LONG RUNNING AGENT RUN FOR 1 WEEK” as a meme headline) and frustration over unclear pools/limits—especially compared against cheaper/high-scoring alternatives like GLM-5.

• Windsurf Turns Eval Into a Spectator Sport: Windsurf published an Arena Mode public leaderboard with an announcement and writeup: announcement, blog analysis, and the live leaderboard.

  • They also added GPT-5.3-Codex-Spark (preview) into Arena Mode per this update, creating a new feedback loop where users compare “Frontier” (e.g., Opus 4.6) vs “Fast” model behavior under battle-group constraints.

3. GPU/Infra Tooling + Kernel-Gen Experiments

• torchao Trims Fat, Adds MXFP8 MoE Muscles: The torchao v0.16.0 release added MXFP8 MoE building blocks for training with expert parallelism and pushed toward ABI stability, per release notes.

  • The same release also deprecated older configs/less-used quantization options, reinforcing a “keep it lean” direction that kernel and inference folks immediately map to simpler deployment surfaces.

• $30k in 5 Days: Kernel-Gen Hackathon Energy: GPU MODE organizers lined up $20–30k of compute for 4–5 days (late February) to run rapid kernel-generation experiments using models like Qwen3/GLM4.7 Flash, integrating evals such as Kernelbot/Flashinferbench.

  • They called for collaborators and pointed at concrete baselines/datasets like kernelbook-kimi_k2_thinking-evals-unique-synthetic-prompts plus tooling progress like NCU/Compute-Sanitizer as tool calls in FlashInfer Bench docs and a modularization PR: flashinfer-bench #183.

• TraceML Watches Your Ranks Like a Hawk: An engineer shared TraceML, an OSS tool for PyTorch DDP that shows live per-rank step time/skew with ~one line of instrumentation, at traceopt-ai/traceml.

  • The pitch resonated because it targets the boring-but-fatal failure mode: you think you’re scaling, but one GPU drags, and you only notice after a burned weekend.

4. Search/OCR + MCP Toolchains for Practical Agents

• Google Search MCP: No Keys, No Mercy: LM Studio users shared VincentKaufmann/noapi-google-search-mcp, a Google Search MCP built on Chromium Headless that avoids API keys and supports YouTube transcription, Images/Lens, and even local OCR.

  • The thread framed this as a pragmatic “agent toolbelt” upgrade: fewer vendor dependencies, more modalities, and a clear MCP-shaped interface for plugging into LLM workflows.

• SigLIP2 Tags 150k Photos Without an LLM Identity Crisis: For bulk image tagging, the community recommended SigLIP2 via the HF blog “SigLIP2”, specifically pointing to google/siglip2-large-patch16-256 as a small(ish) vision backbone for generating tags in Python.

  • The underlying theme: don’t overpay for a chatty multimodal LLM if a focused vision encoder solves the pipeline cleanly.

• Granite 4 + DuckDuckGo: Cheap Search Brains: LM Studio users reported Granite 4 tiny/micro models work well for web search when paired with DuckDuckGo’s API, with some asking for tooling to fetch and extract text from URLs.

  • This clustered with other “build-your-own search stack” chatter (and Perplexity frustration elsewhere), suggesting engineers are actively reconstructing search workflows with local models + scraping/tooling.

5. Observability, Introspection, and “Show Your Work” Governance

• Anthropic’s “Introspection” Paper Gets Side-Eyed: Unsloth’s research channel dug into Anthropic’s “Introspection” paper, debating what counts as real introspection versus a redundant network that detects “abnormal” activations/weights.

  • One camp argued it’s basically a sensor for weight/activation fiddling (”pressure sensor on a pressure cooker“), while others pointed out models can detect light steering, implying some usable awareness of internal state drift.

• KOKKI v15.5 Makes Audits a First-Class Output: In OpenAI’s prompt-engineering discussions, KOKKI v15.5 proposed an explicit Draft → Audit output contract to make accountability user-visible, with members noting the intentional tradeoff: higher token usage and latency for observability.

  • The follow-on debate got concrete: if you truly want a “guarantee,” one member said it would look like a deterministic system, not a transformer, so the realistic goal becomes bounded error + inspectable behavior rather than binary truth.