Hermes x GLM5.2 x Pokémon i asked GLM5.2 to one-shot a playable Pokemon game via Hermes (using /goal) it generated everything, including the assets and the sprites. Cost: ~110k tokens / ~$0.5 The game visuals could be improved but the results are satisfying considering the speed and the cost. With more back and forth, an improved and more complete version could be reached very quickly.

Ornith 1.0 32B🦜 vs GLM5.2 🐲 i ran Ornith 1.0 32B locally on my Mac M2 Max (~60 tok/s) against GLM-5.2 on the same 5 animation tasks: - burning paper - paper slice - bubble wrap - zipper opening - mechanical watch as expected GLM-5.2 wins on quality, but Ornith is surprisingly competitive for a model running entirely on-device. the gap is smaller than I expected

I'm canceling Claude and upgrading @AskVenice to Max. For the past two weeks, I reverted to Opus 4.8 from @AnthropicAI after using @Zai_org 's GLM5.1 through Venice for a month and a half. My motivation was simple: make the most of a subscription I'm already paying for. Well my money will be better spent on Venice. Yes GLM is slower but Opus is so retarded and sloppy while being dystopian privacy raping. Now that 5.2 is out, it's a no brainer.

Goldman - the leaders in enterprise AI coding can sustain their lead due to the data flywheel: "Compared with consumer AI data sets, enterprise AI has a unique positive flywheel of coding data (with clearly defined success/failure scenarios that can then feedback for consistent model iterations/reinforcement learning post-training). We believe this positive loop will enable Zhipu to sustain its #1# leadership position in enterprise AI coding scenario, where such leadership could even widen based on global examples. We see GLM5.2’s recent out-performance (ranked nearly on par with US SOTA models, on Arena AI based on actual user feedback) as a landmark moment for Chinese AI models’ intelligence (we call it the “Zhipu GLM-moment” as models reach the overall performance threshold for wide adoption) after the DeepSeek moment (where breakthroughs were mainly on cost efficiencies)."

Introducing Z Code: From Vibe Coding to Agentic Engineering. Z Code is a new kind of Agentic Development Environment (ADE). Not another IDE with a chatbot — a cockpit where Claude Code, Gemini CLI, and Codex work together in one workspace. ⚡ Multi-Agent Framework — switch between Claude, Gemini, and Codex mid-project, zero friction. 📱 Dual-Device Sync — connect your phone to your desktop agent via QR code. Code on the go, real-time. 🧠 Multi-Provider Support — OpenAI, Anthropic, Google, DeepSeek, Bigmodel, and more. One interface, all engines. Powered by GLM-5 — 744B params (40B active), open-source SOTA, MIT license. Download Z Code: Try GLM-5: Weights: Coding Plan: #ZCode# #GLM5# #AgenticEngineering# #ADE#

💥 GLM-5.3-Flash: Reported Opus 4.8-Level Scores at One-Tenth the Price Zhipu has confirmed that Ox Alpha — the anonymous model that dominated OpenRouter and OpenCode last week — is GLM-5.3-Flash, a 320B-parameter MoE with 18B active, released with open weights and an API at roughly one-tenth of GLM-5.3's price. Zhihu contributor 小小将 opens with a confession: he had hoped Ox Alpha was an external team fine-tuning the open GLM weights. He was wrong — it was Zhipu's own model all along. His broader take: at this performance and this price, Flash becomes the new "kill line" for large models — the bar below which rivals simply get priced out. 1️⃣ The numbers behind the "kill line" Flash is somewhat larger than DeepSeek-V4 Flash, at 320B total and 18B active. On Zhipu's own benchmark comparisons, overall capability is roughly level with Claude Opus 4.8. More reference points he cites: 🔹 57 on the Artificial Analysis Intelligence Index — around GPT-5.6 Terra's level, slightly below GLM-5.3. 🔹 No.5 on Code Arena, one spot above GLM-5.3. 🔹 API at 1/10 of GLM-5.3's price, with a limited-time half-off promo bringing it to 1/20. 2️⃣ Same DeepSWE score, a fraction of the cost The author's sharpest comparison is on DeepSWE, where Flash scores 63% at a single-task cost of $0.24. DeepSeek-V4-Pro hits the same score at $1.67 per task — roughly seven times the cost for equivalent results. This is the author's cost arithmetic on reported figures, not an independent measurement. 3️⃣ The architecture that cuts the bill Part of the price drop is structural. Flash uses a hybrid of linear and sparse attention, sharply cutting attention compute, and a new IndexPool that compresses the indexer's cache from four copies to one — reducing latency and memory at million-token context. Versus GLM-5.3, the company reports attention compute down to about 1/3 and KV cache down to about 1/4.4. 4️⃣ The compute mystery, answered by domestic chips One reason few believed Ox Alpha was Zhipu's: the company was not thought to have enough spare compute for a massive free public test. The answer, per Zhipu: Flash is served from domestic Chinese chip clusters. The team built a custom inference engine on SGLang and worked around limited VRAM and bandwidth with quantization, layered deployment, and trading compute for communication — lifting end-to-end serving performance about 3x on the same hardware, with per-token cost now close to mainstream NVIDIA GPUs. 5️⃣ Chinese silicon just passed its biggest stress test The author's conclusion: Chinese chip clusters have shown they can carry large-scale inference for a frontier-level model — including a free, record-breaking public trial. If that holds, he argues, the outlook for Chinese models just got a lot brighter. 🔗 Full Reading: #GLM# #Zhipu# #GLM5Flash# #AIChips# #LLM# #AIInfra# #OpenWeights#

⚡ GLM-5.3-Flash: Near-Flagship Logic at One-Tenth the Price Zhipu has confirmed that Ox Alpha — the anonymous model that just topped usage charts on OpenRouter and OpenCode — is GLM-5.3-Flash, a 320B-parameter MoE with 18B active, released with open weights. The company prices its API at roughly one-tenth of GLM-5.3, and reports an Artificial Analysis Intelligence Index score of 57, on par with Claude Opus 4.8. Zhihu contributor toyama nao, known for a long-running monthly logic benchmark built on self-designed problem sets, argues the launch fills a gap the market has had since DeepSeek raised prices: a model good enough to use daily and cheap enough to ignore. The core judgment: GLM-5.3-Flash is not a capability breakthrough. It is a cost breakthrough — same lineage as GLM-5.3, nearly identical results on many tasks, but a new path on inference efficiency. 1️⃣ Why the market needed this model After DeepSeek's price hike, its tier lost any clear price-performance leader. GLM-5.3 then improved quality without raising prices and quietly took that spot. Given compute costs in China, shrinking the model is the pragmatic route to genuinely low prices — and smaller models have already proven they can carry real workloads. What the market lacked was a model cheap enough that cost stops being a decision. Flash is that option. 2️⃣ Where Flash matches GLM-5.3 — and where the floor drops In the author's monthly logic evaluation, Flash matches the standard GLM-5.3 on most low- and mid-difficulty tasks, including coding. On hard tasks it can still reach the same ceiling — just not reliably. In practice that means more retries to get the best answer. Retries are cheap at Flash's pricing, but output speed hasn't improved, so the experience still degrades. 3️⃣ Hallucination: wider variance in both directions Flash's hallucination behavior shows a wider spread than the standard model. 🔹 At its best, it hallucinates less than GLM-5.3, catching very fine details buried in the context. 🔹 At its worst, it is worse — misreading long prompts and making basic mistakes. Longer inputs trigger the bad case more often. 4️⃣ The real story: token efficiency without longer reasoning Unlike many small models that stretch their reasoning chains to buy intelligence, Flash uses fewer tokens than GLM-5.3 on nearly every task — as little as 40% of the standard model's consumption in the best cases. On problems where most models brute-force the search space, Flash often narrows it down almost by intuition. The author notes GPT-5.6 still holds the token-efficiency crown on some difficulty levels. One catch: on tasks that genuinely require exhaustive search, Flash's consumption matches the standard model — and it can hit the API's 128K max output cap, leaving answers truncated. 5️⃣ Why this matters Four years into the LLM era, most people have still never used one for daily work. Products built on top of these models urgently need a model cheap enough to flip their ROI positive. As flagships cross the "good enough" threshold and their capability starts to overflow, that surplus intelligence should be inherited by a more civilian model. The market needed a low-price model — someone had to ship it. 🔗 Full Reading: 📊Author's monthly logic benchmark: #GLM# #Zhipu# #GLM5Flash# #LLM# #AIInference# #OpenWeights# #AIAgents#

GLM-5.3 Flash quant showdown on 2× DGX Spark per model. Bench v6.7.1 — 76 scenarios × 2 repeats. EXL3 TR3 4bpw • TrueScore: 90.9 • Capability: 92.2 • Operational: 87.6 • Median turn: 4.30s • Long-response effective rate: ~35.4 tok/s • 53,347 total output tokens NVFP4 • Raw TrueScore: 78.5 • Capability: 71.6 • Operational: 71.9 • Median turn: 13.42s • Long-response effective rate: ~26.5 tok/s • 996,608 total output tokens EXL3 won instruction following (94.2 vs 48.9), structured output (97.2 vs 12.8), code (98.1 vs 68.5), visuals (92.3 vs 36.3), long context (100 vs 85.3) and robustness (100 vs 86.8). NVFP4 won agentic work (98.5 vs 88.0), planning (100 vs 91.2) and narrowly won safety (87.8 vs 85.6). Tool use tied at 78.4. NVFP4 completed every request with zero transport errors, but emitted 18.7× more output and took 3.12× longer per median turn. Important: this is a deployment comparison, not a pure quant-only test. EXL3 used FP8 KV, CUDA graphs and a configured 1M context ceiling. NVFP4 used Marlin, FP8 E4M3 KV, eager mode and a 262K ceiling. Verdict: EXL3 TR3 4bpw was the much better all-around deployment. NVFP4 was genuinely strong for planning and agentic workflows, but its verbosity and format behavior need fixing before it can compete on practical quality and speed.