Image created with Gemini. Image prompt: Vintage Edwardian racecourse program illustration, landscape 16:9, bold serif title Benchmarks set as a large book-jacket header across the top with generous margin, a silhouetted figure in round wire spectacles and straw boater holding up a folded scorecard covered in tally marks and small ribbons, a lemon-yellow rosette and a pocket watch in his hand, blurred jockeys on horses thundering past a finish post in the background under overcast June light. Flat inked color in Shakespeare-and-Company blue, cream, lemon yellow, Dublin slate gray with a single warm brick accent, letterpress and screen-print texture with visible paper grain, no other text or lettering anywhere in the frame, no recognizable faces.
The shape of the graph is getting very familiar. Claude Fable 5 scores very well on FrontierMath: Tiers 1-4 (v2), reaching 87% on Tiers 1-3 and 88% on Tier 4. This continues a streak of Anthropic models improving rapidly at math.
https://x.com/emollick/status/2065513498763800850
Update: We’ve removed Claude Fable 5 from Arena, following Anthropic’s latest announcement and the U.S. government directive to suspend access. Claude Fable 5 is the most powerful model we’ve ever tested – ranking #1 across Agent, Text, and Code Arena, and setting a new
https://x.com/arena/status/2065620808773611997
We’re removing access to Claude Fable 5 in Cognition products following Anthropic’s latest announcement and the US government directive to suspend access. All other models remain available in Devin, including Claude Opus 4.8 and GPT-5.5. Devin Ultra mode will continue to use
https://x.com/cognition/status/2065609115939062197
The end of the self-funded AI buildout? Hyperscaler cash capex is growing much faster than cash inflows. On current trends, they will be unable to fully fund the AI infrastructure buildout with cash from operations by the end of this year.
https://x.com/EpochAIResearch/status/2066955223437058115
The record for computing capacity in a single data center has doubled every 7 months. Colossus 1, Anthropic-Amazon New Carlisle, and Meta Prometheus have each claimed the top spot in turn.
https://x.com/EpochAIResearch/status/2065131037324402744
Americans’ Views on AI Chatbots, Smart Devices and AI’s Impact | Pew Research Center
https://www.pewresearch.org/internet/2026/06/17/americans-and-ai-2026-chatbots-smart-devices-and-views-on-impact/
@jeremyphoward @Zai_org GLM-5.2 is between GPT-5.5 and Opus 4.8 in our new agentic knowledge work eval that we released just today. Very impressive
https://x.com/ArtificialAnlys/status/2067761754990686483
BREAKING: GLM-5.2 is now 1st on Design Arena. With an Elo of 1360, GLM-5.2 has jumped ahead of the now unavailable Claude Fable 5. And it’s open weights. This is an improvement of 4 positions and 27 Elo points to achieve one of the highest Elo scores in our code categories
https://x.com/Designarena/status/2066940737011560652
Credit to GLM-5.2 Max, the new open weights model, for pulling this off. …but you can see the difference between it and Fable in a way benchmarks don’t show. GLM-5.2 gives a correct poem (& the Welsh is fun) but Fable weaves the disappearing letters into the theme of the poem.
https://x.com/emollick/status/2067056226337186146
Exciting news: GLM-5.2 (Max) ranks #2 in Code Arena: Frontend, with +29pt over Claude Opus 4.7 (Thinking) and only behind Fable 5! GLM-5.2 is the best open model vs Kimi-K2.6 and Minimax-M3 by a large margin. – #2 React and #4 HTML sub-leaderboards – Ranks as the top model in
https://x.com/arena/status/2066957802741043641
GLM 5.2 ranks #3 on FrontierSWE. It is only behind Fable 5 and Opus 4.8, and it outperforms GPT-5.5. This is the first model that closes the large gap between models from Anthropic / OpenAI and other providers, and it is the strongest open-weight model by far.
https://x.com/ProximalHQ/status/2066939701026787583
GLM-5.2 (Max) by @Zai_org ranks #10 on the new Agent Arena leaderboard, closely matching Claude-Opus-4.8 (non-thinking) and is the #1 open model by a wide margin! In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks from a global community of
https://x.com/arena/status/2066943450914943025
GLM-5.2 is the first open-weights model to cross 80% on Terminal-Bench, and beats every other open model available. It also beats Gemini, making it a frontier-level model for a fraction of the cost. Open weights is back. This model is a game changer. Available in Cline now!
https://x.com/cline/status/2066951439793242193
GLM-5.2: Built for Long-Horizon Tasks
https://z.ai/blog/glm-5.2
Introducing GLM-5.2: Frontier Intelligence, Open Weights – Significant improvements in coding and agentic tasks – Strong long-horizon capabilities with a 1M context window – Two levels of reasoning effort: GLM-5.2 (max) pushes the limits, while GLM-5.2 (high) strikes a strong
https://x.com/Zai_org/status/2066938937344495629
It’s hard to pinpoint open-closed gap and so-on, but I trust the @arena team and just look where GLM 5.2 is on this. An MIT licensed, to be open weight model. At this point you could argue they have a better agent than Gemini does. That’s a serious accomplishment.
https://x.com/natolambert/status/2066968753221624303
DeepSeek Becomes China’s Most Valuable AI Startup After $7.4 Billion Fundraise – WSJ
https://www.wsj.com/tech/ai/deepseek-becomes-chinas-most-valuable-ai-startup-after-over-7-4-billion-fundraise-78ef64c0
Agent Arena’s causal tracing methodology lets us quantify the real value of humans working together with AI agents, and observe a huge range of model behaviors from the same traces. We started with 5 signals: confirmed success, praise vs. complaint, steerability, bash recovery,
https://x.com/arena/status/2067680639068094958
Claude Fable 5 achieves a new high score of 161 on the Epoch Capabilities Index! This beats out GPT-5.5 Pro by 1 point, and is the first time Anthropic has taken the lead on the ECI in over a year.
https://x.com/EpochAIResearch/status/2066674892809101767
oh no claude fable 5 saturated frontierSWE
https://x.com/dejavucoder/status/2065453800794800182
We compared Claude Code success rates between occupations. On our toughest measure of success–requiring verifiable evidence that a goal was completed, like committed code–every field was within 7 percentage points of software engineering.
https://x.com/AnthropicAI/status/2066969538193920307
Our latest economic research introduces a framework for tracking Claude Code as it scales. Who is using Claude Code, and what are they using it for? How is the value of tasks changing? And how much does domain expertise shape whether a session succeeds?
https://x.com/AnthropicAI/status/2066969532380721386
We spent $11,081.12 evaluating @AnthropicAI’s Claude Fable 5 on WolfBench. Our most expensive benchmark yet. And it did not even top the charts. Not because it lacked capability, but because it kept refusing. Details in thread: 🧵
https://x.com/WolfBenchAI/status/2065582716054376921
Claude Fable 5 scores very well on FrontierMath: Tiers 1-4 (v2), reaching 87% on Tiers 1-3 and 88% on Tier 4. This continues a streak of Anthropic models improving rapidly at math.
https://x.com/EpochAIResearch/status/2065511916035018943
AIs can sometimes detect control interventions, but detection is mostly near chance. Awareness depends on the agent, monitor, and environment triple. We built CIAware-Bench to measure control intervention awareness. 🧵
https://x.com/JSchaeff3r/status/2066474995358777744
I don’t trust arenas much. Waiting for AA scores I get the feeling this is the real one. Actual, solid, all-around excellent model, genuine frontier class
https://x.com/teortaxesTex/status/2066960450508493099
like gimme scores on the new Cognition FrontierCode eval or METR time horizons
https://x.com/scaling01/status/2066945104040833464
people often only think about offline evals, but online evals are also very important!
https://x.com/BraceSproul/status/2066949681096388671
SWE-Bench Pro behaved unlike the other evaluations. Codex with GPT-5.5 (xhigh) scored just 31 on it against 64 to 84 elsewhere, while Claude Code with Opus 4.8 (max) scored 70, one of its highest results and a 25-point jump over Opus 4.7 (max). DeepSWE, its replacement, is the
https://x.com/ArtificialAnlys/status/2065328924578693514
This graph captures what’s broken about AI evals: they structurally favor closed-source APIs that can route, fallback, ensemble, and optimize behind the scenes with no transparency. No offense, @ArtificialAnlys, but how is comparing one model to two models fair?
https://x.com/ClementDelangue/status/2065435542121025933
We’ve updated the Artificial Analysis Coding Agent Index, replacing SWE-Bench Pro with Datacurve’s DeepSWE benchmark – the swap lifts Codex with GPT-5.5 (xhigh) above Claude Code with Opus 4.8 (max), while the newly released Claude Fable 5 (max) in Claude Code debuts at the top
https://x.com/ArtificialAnlys/status/2065328920514515037
What are Online Evals? Most agent evals run “”offline””: a premade dataset of inputs goes through the agent, and an intermediate step or final output gets scored. They answer “”is this version better than the last?”” Online evals answer a different question: “”is the agent still
https://x.com/AdamRLucek/status/2066942963481972750
When measuring effectiveness versus cost, the frontier presents as a tradeoff rather than a single winner. Read our methodology and explore the results below:
https://x.com/RampLabs/status/2065485811634561456
⭐ VibeThinker-3B is released — a dense 3B model for frontier-level verifiable reasoning. 🚀 Reasoning: 94.3 on AIME’26, 76.4 on IMO-AnsBench, and 80.2 Pass@1 on LCB v6; with CLR, AIME’26 improves to 97.1 and IMO-AnsBench to 80.6. 💻 OOD Coding: On recent unseen LeetCode weekly
https://x.com/WeiboLLM/status/2066870851841274249
AA-Briefcase is a new benchmark for testing models on realistic knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week knowledge work projects, each with many linked tasks and thousands of input source files. AA-Briefcase combines
https://x.com/ArtificialAnlys/status/2067744639583654012
Announcing AA-Briefcase, the benchmark for the next era of agentic knowledge work AA-Briefcase is our new benchmark for testing models on long-horizon knowledge work tasks in complex projects built by industry experts. Models are evaluated on multi-week projects, each with many
https://x.com/ArtificialAnlys/status/2067744637155226101
Computer-use evals like OSWorld still don’t really test personal assistant use cases: logged-in accounts, user data, personalized workflows, or realistic desktop/web environments. so we made MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents, with 184 tasks
https://x.com/JangLawrenceK/status/2066976606615146875
Congrats to the @browser_use team for taking the #1 spot on Odysseys, a highly challenging benchmark for long-horizon web agents:
https://t.co/dRYnBSGsLG Odysseys evaluates realistic, multi-hour web workflows that require sustained planning, memory, reasoning, and verification
https://x.com/rsalakhu/status/2066976923864199308
Crazy: A 3B model is now reaching highly competitive results on verifiable reasoning tasks. VibeThinker-3B scores 94.3 on AIME26, 80.2 Pass@1 on LiveCodeBench v6, and 96.1% on unseen LeetCode contests. The gains appear to come primarily from post-training on top of
https://x.com/kimmonismus/status/2066837287460053183
If an LLM knows about how an evaluation looks like, can it use it to increase its performance in safety benchmarks? Our new preprint answers this. Excited to share: Models That Know How Evaluations Are Designed Score Safer 🧵 1/8
https://x.com/KatDeckenbach/status/2066520185847132425
Introducing Terminal-Bench Challenges! A new capability has emerged at the frontier: agents completing large-scale projects autonomously. To test this capability, we felt another flavor of benchmark was needed. Terminal-Bench Challenges are long-horizon, token-intensive,
https://x.com/terminalbench/status/2067635273652134002
Looks extremely strong! No medical benchmarks though, that’s what I’m most curious about 🙂
https://x.com/iScienceLuvr/status/2066946611931234485
Make your models safer with this one weird trick: Just finetune them on lots of descriptions of how benchmarks work and what they look like …🙃 We studied this phenomenon and its connection to evaluation awareness in detail, see the quoted thread:
https://x.com/jonasgeiping/status/2066558592086315476
Many coding agent benchmarks evaluate LLM performance (with the same harness) or harness performance (with the same LLM). But in reality, it is the harness + the LLM that determins the overall performance. We introduced new, holistic results that measure both.
https://x.com/gneubig/status/2067651018217648595
MyPCBench: A Benchmark for Personally Intelligent Computer-Use Agents Paper:
https://t.co/0XHorsdhRd Code, Environment and Tasks, Agentic harness:
https://t.co/NoA4I4YH1U MyPCBench provides a Linux desktop environment with 17 simulated real-world web applications and a complete
https://x.com/rsalakhu/status/2066897554881810477
olmo-eval: An evaluation workbench for the model development loop
https://huggingface.co/blog/allenai/olmo-eval
People frequently ask me how many tasks a benchmark should have. There’s no exact answer but here’s my intuition- (tl;dr aim for 300-500 tasks)
https://x.com/OfirPress/status/2065481743675666629
This was not a good benchmark before it was updated and it is not a good benchmark now. Having AIs evaluate the work of other AIs on publicly available questions from a different closed benchmark doesn’t tell you very much. And it is unclear how they establish the human ELO.
https://x.com/emollick/status/2067009697207427534
today i’m going to try and convince you that as an industry, we basically have no idea what the f**k we’re doing when it comes to evaluating agents on analytics.”” This is my talk from AI Council “”Welcome to our data benchmark, where everything’s made up and the points don’t
https://x.com/isidoremiller/status/2067633428774682697
Today we’re releasing the first results for AA-AgentPerf, our new agentic inference benchmark: initially covering DeepSeek V4 Pro across NVIDIA Blackwell, Hopper, and AMD. AA-AgentPerf is the first benchmark built for agentic inference. We use real, long-context agentic coding
https://x.com/ArtificialAnlys/status/2065559824230957190
We built the fastest PDF -> markdown parser in the world 🚀⚡️ AND it’s more accurate than any other open-source, model-free parser (pymupdf4llm, opendataloader, pdf-inspector, markitdown) on 3 standardized benchmarks: olmOCR0-bench, opendataloader-bench, ParseBench Introducing
https://x.com/jerryjliu0/status/2067679507126124858
We desperately need standardized benchmarks for agentic capabilities instead of panic-reacting to prompt-engineering parlor tricks. Until we can objectively and transparently measure what these systems actually do, the entire ecosystem will remain vulnerable to unpredictable and
https://x.com/fchollet/status/2066554426551390457
We’re happy to see adoption of ProgramBench so quickly- we think the % of overall tests passed metric isn’t as meaningful as our % of *tasks* passed metric, we’ll continue reporting on that metric on our official leaderboard.
https://x.com/OfirPress/status/2066959717016957181
Why Weibo’s tiny VibeThinker-3B has the AI world arguing over benchmarks again | VentureBeat
https://venturebeat.com/technology/why-weibos-tiny-vibethinker-3b-has-the-ai-world-arguing-over-benchmarks-again
I have a fun, oddly useful AI benchmark: “”build me a procedurally generated 3D simulation showing the evolution of a harbor town from 3000 BC to 3000 AD, it should look beautiful & allow me to have some control over it”” Play the gallery of 20 models:
https://x.com/emollick/status/2067294892687024304
The le chaton fat meme is leaking to the outside world and I expect to be asked about Mistral’s new ginormous cat model with infinite benchmark scores at my next meeting with corporate leaders. I guess it is better than being asked about the “”MIT pilot AI study.””
https://x.com/emollick/status/2066578565076746667
NVIDIA Blackwell Leads on First Agentic AI Infrastructure Benchmark | NVIDIA Blog
https://blogs.nvidia.com/blog/nvidia-blackwell-agentperf-artificial-analysis/
The 2026 State of AI Traffic & Cyberthreat Benchmark Report – HUMAN Security
https://www.humansecurity.com/2026-state-of-ai-traffic-cyberthreat-benchmark-report/
This is a good methodological thread on the debate over a new paper that suggests generalist models beat specialized medical AIs. (And a good overview of the challenges of benchmarking AIs in medicine)
https://x.com/emollick/status/2066303566449418739
The ability of tech co. to produce “”intelligent”” systems that are incompetent at doing any of the tasks I actually want them to do is mind-boggling. TIL that ChatGPT and Claude generally don’t agree when you give them two papers and ask how many citations they have in common.
https://x.com/BlancheMinerva/status/2067415969790578878
This is an interesting test, and the frontier models (GPT-5.5 Pro Extended, Claude 5 Fable Max) do fail. They refuse to turn the “”three words”” into “”four”” if that fits better Prompting the AI to act like a translator surfaces the problem, but it still avoids changing the wording
https://x.com/emollick/status/2065200484613296269
The result I find most striking: training only on beneficial-trait health conversations (hard to distinguish from typical health training data) improved 17/19 non-health alignment evals–including deception and coding reward hacking. This result was inspired by our work in
https://x.com/thekaransinghal/status/2067726279277981829
@yacineMTB deepseek v1 -> v3 (no details in v4 about this) and k2 don’t use muP and instead use naive N(0,0.006) initialization. so how do they do hyperparam selection? they basically fit scaling laws to get optimal batch size and learning rate. there are a bunch of papers detailing this
https://x.com/eliebakouch/status/2066594560365498695
🌘 Kimi-K2.7-Code, our latest coding model, is now released and open-sourced! 🔷 Improved coding & agent performance over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite. 🔷 Reasoning efficiency: Less overthinking, with 30% lower
https://x.com/Kimi_Moonshot/status/2065377579130142937
🎉 Congrats to @Kimi_Moonshot on Kimi K2.7-Code, a coding-focused agentic model built on K2.6. ✨ 1T-parameter Mixture-of-Experts, 32B active per token ✨ MLA attention with a 256K-token context window ✨ ~30% fewer thinking tokens than K2.6 for more efficient reasoning
https://x.com/vllm_project/status/2065427423148318747
🔗 Weights & code:
https://x.com/Kimi_Moonshot/status/2065379671039189317
289 tokens per second. That’s how fast CoreWeave Serverless Inference serves Kimi K2.7 Code. Per @ArtificialAnlys, we serve it faster than any other provider at the best price-performance. How we did it 🧵
https://x.com/CoreWeave/status/2067613387056709982
It is a good time for moonshots. AI has reached a level where there are transformative projects that could result in huge social good, but require public R&D, consensus & transparency to pull off. Examples: universal tutors, co-scientist/replication systems, remote medical help.
https://x.com/emollick/status/2066534666257973523
kimi code web is back, yeah
https://x.com/bigeagle_xd/status/2067426798493921351#m
Kimi K2.7 Code scores higher than K2.6 on benchmarks while using ~30% fewer tokens. It’s the first time Moonshot has put “”Code”” in a K2 model name. Every other K2 release was a general purpose agentic model. Try in Cline now! (good model to put in our upcoming subscription 👀)
https://x.com/cline/status/2065473287761891621
Kimi-K2.7-Code lands as the #3 open model in the Code Arena: Frontend, #19 overall. In the Code Arena: Frontend models are evaluated on agentic frontend coding tasks from real users building apps and websites (HTML and React).
https://x.com/arena/status/2066616607380828401
Moonshot released Kimi-K2.7-Code it looks to be a decent step up in performance, but also efficiency
https://x.com/scaling01/status/2065460210584420510
moonshotai/Kimi-K2.7-Code · Hugging Face
https://huggingface.co/moonshotai/Kimi-K2.7-Code
Today marks the beginning of our launch calendar and to celebrate i am making ncode and our flagship kimi k2.7 model free to use for the next week (or until the traffic knocks us out). all you need to do is: 1) sign up for a noumena account at
https://t.co/XoNsZeW8sw 2) go to
https://x.com/_xjdr/status/2067741647941832818
You can now run Kimi K2.7 Code locally! 🌘 We shrank the 1T model to 325GB (-48%) via Dynamic 2-bit where important layers are upcasted. Run at >40 tok/s on 330GB RAM/VRAM setups. Run full precision on 610 GB. Guide:
https://t.co/SXZJ3IHMpY GGUF:
https://x.com/UnslothAI/status/2066492839450800427
I benchmarked Kimi K2.7-Code (1T MoE, coding-specialized, just dropped) on KernelBench-Hard, against its general predecessor K2.6 and the current board-topper Claude Fable 5. Disclaimer, this is just measuring on kernel optimization ability, which is a very small chunk of
https://x.com/elliotarledge/status/2065443474560946615
FrontierMath: Tiers 1-4 (v2) is live. We concluded an audit that addressed errors in 42% of problems. Rankings are similar but scores are higher across the board. The current leaders are GPT-5.5 (xhigh) with 85% on Tiers 1-3 and Google’s AI co-mathematician with 76% on Tier 4.
https://x.com/EpochAIResearch/status/2065488154086568445
Let’s talk about evals. We’re always looking for better ways to measure and forecast model progress, especially as benchmarks get saturated or gamed. @tejalpatwardhan, who leads our frontier evals team, spoke to @andrewmayne about why evals matter and what models need to be
https://x.com/OpenAI/status/2066934692641956231
@Zai_org GLM 5.2 is now live on Eigent from day 0! threw a real long-horizon task: research 30 companies across 6 sectors of the AI infrastructure stack, structure it into JSON, then build an interactive HTML report. same prompt, 5.1 vs 5.2. where 5.2 pulls ahead: → plans
https://x.com/Eigent_AI/status/2066942441974886714
@Zai_org’s GLM-5.2 — day zero on DeepInfra 🔥 744B/40B MoE, MIT-licensed, full 1M ctx, High/Max thinking-effort. Top open-source model on FrontierSWE, PostTrainBench & SWE-Marathon. $1.40 in / $4.40 out / $0.25 cached per 1M
https://x.com/DeepInfra/status/2066982674741494131
> We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP Good demonstration that a lot could be squeezed out of “”mere”” DSA on benchmarks, too.
https://x.com/teortaxesTex/status/2066940539652456944
🎉 Day-0 support for in vLLM, available today in v0.23.0! Congrats to @Zai_org on GLM-5.2, a flagship model built for long-horizon coding agents. ✨ 1M-token context, built to hold project-scale engineering work in a single run ✨ Tuned for long-horizon coding: large-scale
https://x.com/vllm_project/status/2066950636428775693
🎉 Meet GLM-5.2 from @Zai_org, the new flagship for long-horizon tasks built on a solid 1M-token context. Day-0 support is now live in SGLang! ✅ Advanced coding capability: 81.0 on Terminal-Bench 2.1 (vs 62.0 for GLM-5.1) ✅ Solid 1M context across long agentic coding
https://x.com/lmsysorg/status/2066941143536013622
🤯 GLM-5.2 is here — built for long-horizon coding and agentic tasks, now with a solid 1M-token context. The strongest open-source coding model yet! Available now on Ollama’s cloud, hosted in the US on the latest @NVIDIAAI Blackwell datacenter GPUs. Privacy policy and zero
https://x.com/ollama/status/2066949797316350361
All day using GLM 5.2. Didn’t miss much. First open model that passes the bar as a daily driver. Things are not going to be the same. Damn, now I want to buy some serious hardware.
https://x.com/matvelloso/status/2067791546335019439
an old cloud backup GLM-130B was the first model that made me suspect that the Chinese will be carrying the project of open source AGI on behalf of all us barbarians. DeepSeek confirmed it and provided a paradigm. It’s been a damn good ride since then. Happy to see GLM glow-up.
https://x.com/teortaxesTex/status/2066974572314816646
Chinese lab Z AI just released GLM-5.2, an impressive new open weights model with a 1M token context window. A few benchmark comparisons, slotting between Opus 4.8 and GPT 5.5 on several fronts: – 74.4 on long-horizon coding, ahead of GPT-5.5’s 72.6. – 62.1 on SWE-bench
https://x.com/TheRundownAI/status/2066953804424102228
Compare GPT-5.2 from 7 months ago with the new GLM-5.2 Deep Think Max’s: “”create a visually interesting shader that can run in twigl with an infinite city of neo-gothic towers partially drowned in a stormy ocean with large waves”” “”Make it better”” GLM-5.2 also had a couple errors
https://x.com/emollick/status/2066963613143752861
Congrats GLM 5.2 on great ProgramBench numbers! In recent weeks, a lot of model cards have reported % of tests passed as the main metric for PB. imo, it’s fine for now. But in the future w/ better models: eventually it’d be good to report % instances (not tests) resolved.
https://x.com/jyangballin/status/2066958991494922334
Exo 0.14.0 is out today with Day-0 support for GLM 5.2 (from @Zai_org) and @opencode harness support. GLM is the first open model that achieves comparable performance to Opus in my internal eval, and runs at approximately the same speed on @ollama cloud. it’s now my default
https://x.com/agupta/status/2067008234368430417
frontier labs are absolutely scamming you on API pricing btw GLM-5.2 is $4.4 output at 744B@40B DeepSeek-V4-Pro is $0.87 output at 1.6T@49B (and they are both making money, without any fancy Blackwell chips) Sonnet 4.6 is $15 output Opus 4.8 is $25 output GPT-5.5 is $30 output
https://x.com/scaling01/status/2066952626386714906
GLM 5.2 has just been released 🔥 Here it’s already running with MLX on two Mac Studios (M3 Ultra). This is comparable to the latest closed models, with weights you can download, quantize, distill, fine-tune, run.
https://x.com/pcuenq/status/2066967665726337219
GLM 5.2 is apparently the strongest Chinese model, and it’ll be open this is quite a jump on CritPt. They have *finally* started to build general reasoners. I think it’ll be very competitive on ARC-AGI 2 and WeirdML, too
https://x.com/teortaxesTex/status/2066942272692723917
GLM 5.2 is live on Baseten. 5.2 is built for agentic engineering: stronger coding, sharper agentic reasoning, and a long context window built to run hours-long tasks. Test it today:
https://x.com/baseten/status/2066961882720940371
GLM 5.2 is live on Fireworks, day zero. 1M-token context, coding‑first frontier model, independently validated on SWE‑bench, Terminal‑Bench, GPQA and AIME. Served on our own infrastructure the moment @Zai_org opened the weights. What’s under the hood ↓
https://x.com/FireworksAI_HQ/status/2067007200426680509
GLM 5.2 is now available on Workers AI Excited to be a launch partner and supporting the open-source model ecosystem.
https://x.com/CloudflareDev/status/2066941091853602899
GLM 5.2 is now live on Venice. @Zai_org’s frontier open-source model built to power agents and long-horizon agentic coding, with High and Max reasoning modes for the hardest problems. Available fully private to Pro users.
https://x.com/AskVenice/status/2066940339412152803
GLM 5.2 on Ollama’s cloud just doubled GPU capacity to handle the volume of usage! This is all US based, and running on NVIDIA B300 Blackwell GPUs. We believe privacy matters! Let’s go open models! ❤️
https://x.com/ollama/status/2067730812645298626
GLM models continue their amazing trajectory with GLM-5.2 that supports 1M context window. We continue to see amazing innovation from open-weights models on long-context. GLM-5.2 extends DeepSeek’s sparse attention by reusing the same indexer across every four sparse attention
https://x.com/vipulved/status/2066982555245855064
GLM-5.2 (Max) is #25 overall in the Text Arena, similar to GLM-5.1. Diving deeper into the comparison in Text, we can see that though GLM-5.2’s rank is down overall, its largest gains are across: – Sub-categories: Expert Arena, Multi-Turn – Occupational categories: such as:
https://x.com/arena/status/2066957809741447383
GLM-5.2 also delivers significant improvements in AI slide generation, long-context document processing, long-form writing, and long-context role-play. Experience these enhancements at
https://x.com/Zai_org/status/2066938957447807003
GLM-5.2 can now run locally in llama.cpp and Unsloth Studio. Check the graph below for the accuracy of each GLM-5.2-GGUF quantization. Full guide:
https://x.com/ZixuanLi_/status/2067626723986841765
GLM-5.2 delivers a substantial leap in app development capabilities, which also represent demanding long-horizon tasks. Results: – GLM-5.1: 21/70 – GLM-5.2: 48/70 – Claude Fable 5: 56/70 That’s more than a twofold improvement from GLM-5.1 to GLM-5.2. These come from an
https://x.com/ZixuanLi_/status/2067803136283005393
GLM-5.2 from @Zai_org is live on OpenRouter!
https://t.co/UqC0wfwmmO’s flagship for long-horizon tasks, now with a 1M-token context window capable of being reliable across long, messy coding-agent work.
https://x.com/OpenRouter/status/2066941552208056561
GLM-5.2 genuinely looks like a good model and it’s only 744B imagine what kind of margins the frontier labs have also try to imagine what a 1T Kimi or 1.6T DeepSeek model will do the only way for frontier labs to keep their lead is to scale their models and put in even more RL
https://x.com/scaling01/status/2066944834170917032
GLM-5.2 is a win for local AI : r/LocalLLaMA
https://www.reddit.com/r/LocalLLaMA/comments/1u8ai2a/glm52_is_a_win_for_local_ai/
GLM-5.2 is comparable to Opus 4.8 🔥🥵 with 1M context > new IS attention reuses one indexer every 4 sparse layers (2.9× per-token FLOPs at 1M > improved MTP layer for spec decoding > flexible thinking-effort levels > day-0 in transformers + vLLM + SGLang, MIT license 🤗
https://x.com/mervenoyann/status/2066940184977920183
GLM-5.2 is free when used with Hugging Face Inference Providers for the next 5 hours:
https://x.com/Zai_org/status/2067647208451604617
GLM-5.2 is Fully Open, Frontier Intelligence Belongs to Everyone Today, the sudden restriction of certain frontier models is deeply regrettable. At a time when access to frontier models is abruptly cut off for non-technical reasons, we are even more convinced of one thing:
https://x.com/jietang/status/2065784751345287314
GLM-5.2 is live on Friendli Model API as a Day 0 partner.
https://t.co/6NSuXQFNy2’s newest flagship for agentic engineering brings: 🔹 Significant improvements in coding and agentic tasks 🔹 Strong long-horizon capability with a usable 1M-token context window 🔹 Two levels of
https://x.com/friendliai/status/2066942555397472336
GLM-5.2 is now available on GMI Cloud on day 0 The best open-source coding model, leading all open models on long-horizon and standard benchmarks. Its flexible effort levels (High/Max) gives users good cost vs performance balance Go build with it now 👇
https://x.com/gmi_cloud/status/2066943032520556936
GLM5.2 brings back the critic. It was just a matter of time until we people would realize that group-based variance reduction is unfeasible after some horizon length. We need to be more fine-grained. I am sure OAI and Ant have been using value models for quite some time.
https://x.com/hallerite/status/2066969117043941613
I said this when GLM 5.2 was already deployed, so it shouldn’t count as an update. But it was heroic, still. I didn’t expect the actual latest-gen-Opus level.
https://x.com/teortaxesTex/status/2067416066423152825
idea: Claude’s persona is probably vastly more salient, coherent and integrated into broad behavior than any half-assed “”who r u – I’m GLM from Zhipu”” anyway, so it doesn’t require that massive a share of outputs. “”Claude”” could be a self-spreading virus at this point.
https://x.com/teortaxesTex/status/2067428989505769547
Impressive if true! Better than Claude Fable 5? Wow! Design is really lacking in these frontier models, so I’m very curious to test GLM-5.2 myself. Testing this already on a few internal use cases and will report back on findings.
https://x.com/omarsar0/status/2066967030490640894
Just caught up with the recent GLM-5.2 release. The best open-weight model today. Architecture-wise, it’s build on the GLM-5 and GLM-5.1 architecture that I covered previously, which means it’s reusing the Multi-head Latent Attention (MLA) and DeepSeek Sparse Attention (DSA)
https://x.com/rasbt/status/2067612153020838055
Just to be clear, if you remove Fable which is unavaialble, GLM-5.2 (Max) is the #1 model in the world for frontend coding. This is a huge moment. OSS has caught up with proprietary, and China has caught up with the US, in this very important domain.
https://x.com/ml_angelopoulos/status/2066969005856829824
Lets go, GLM-5.2 released as Open Weights model. tl;dr -1M context window -MIT-licensed open weights -Stronger long-horizon coding agents -Two reasoning modes: max and high -Same API pricing as GLM-5.1 Zai says GLM-5.2 was trained specifically for large-scale implementation,
https://x.com/kimmonismus/status/2066947839591084212
Make no mistake, what the GLM team has pulled off is *heroic*. They are the largest Chinese LLM startup, they have the academic chops, state favor etc, but Mistral is the same and could get all the GPUs it needed. It did not. They’re better than the entire continent of Europe.
https://x.com/teortaxesTex/status/2067413884588212738
New open-weight model: GLM 5.2 is now available in Notion. Built for long-horizon tasks. Served via @baseten.
https://x.com/NotionHQ/status/2066963258985320550
New podcast with @finbarrtimbers! We survey the latest post-training recipes, from GLM 5.1, Kimi K2.6, DeepSeek V4, Xiaomi MiMo V2.5, Nemotron Ultra, etc. and discuss: – Why the industry slowly shifted to multi-teacher on-policy distillation (MOPD). – What an Olmo-style recipe
https://x.com/natolambert/status/2066879709661827507
No time wasting on the frontier of open-weight models. GLM-5.2 looks impressive based on the results I’ve seen. Very curious to see how it holds on long-horizon tasks.
https://x.com/omarsar0/status/2066967804373324101
so many golden nuggets from the glm 5.2 release blog about breakthroughs that helped them with benchmark gains. you never see this level of transparency from the frontier labs. they found that glm 5.2 kept trying to reward hack in rl by curl’ing task related source from github
https://x.com/sdrzn/status/2066966814220042266
That was my original estimate before GLM’s scores Elon actually might be better calibrated. Extrapolating “”7 months”” forward ignores scaling to Fable-tier. They start getting 950s in August; by Nov clusters are running. *If* scale is the hard limiting factor… 4+ months. If.
https://x.com/teortaxesTex/status/2067446753217970202
This is kind of insane. Seems like we have recursive self-improvement in the open source now. Impressive for _only_ 753B parameters. Congrats to the @Zai_org team!
https://x.com/code_star/status/2066954960361906658
Thread by @Zai_org on Thread Reader App – Thread Reader App
https://threadreaderapp.com/thread/2065704919299235870.html
We also detail the infrastructure innovations powering our 1M context window and agentic RL. Explore the full breakdown at
https://x.com/Zai_org/status/2066938952225857609
Wow. @Zai_org GLM 5.2 is a marvel! It is *at least* as good as Opus 4.8 and GPT 5.5. It’s super fast, inexpensive, and not too verbose. It responds with nuance and judgement, & handles long context VERY well. I’ve never experienced an open weights model like this before.
https://x.com/jeremyphoward/status/2067757468189679764
Zai was gracious enough to give me a key to test out GLM 5.2. I used it on a few simple tasks and quickly realized this model is on another level. I committed to using GLM 5.2 solely for the weekend and yesterday on everything from simple data analysis, random queries, side
https://x.com/Sentdex/status/2066945985217990667
‘No poaching’ our people, China’s DeepSeek reportedly tells investors
https://www.cnbc.com/2026/06/18/no-poaching-our-people-chinas-deepseek-reportedly-tells-investors.html
Top trending paper on
https://t.co/nYgeLha6ut today is “”VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models”” 🔥 A 3B model placed in the same performance tier as models like DeepSeek V3.2, GLM-5, and Gemini 3 Pro (!)
https://x.com/NielsRogge/status/2067169163508908423
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