🌱 Seed-2.1-Pro Review: Better Post-Training Can't Hide an Aging Base @ByteDanceSeed_ Seed-2.1-Pro 0915 earns higher reasoning scores with fewer tokens, and its agent ability has climbed from nearly unusable to passable. But in the three months since the last version, rivals iterated roughly a generation and a half — and the old base model is running out of tricks. That is the verdict from Zhihu contributor toyama nao, who runs a long-running monthly logic benchmark and put the 0915 build through his full evaluation suite. 1️⃣ Coding and agent work: from unusable to passable The jump over the predecessor is large. Deliverable completeness now beats DeepSeek V4.1 Flash, though first-pass success still trails top-tier models — a model caught in the middle. 🔹 Frontend: some aesthetic sense, but unstable. Constrained stacks like iOS system components look decent; open stacks like web or raw canvas expose visible flaws in proportion and color. 🔹 Task adaptability: every task now at least completes. In a HarmonyOS-flavored project it barely knew, the model read docs and iterated its way to usable — a good sign. 🔹 Delivery efficiency: roughly tied with DeepSeek V4.1 Flash and GLM-5.3-Flash, splitting steps evenly between writing and verifying. All three sit below the current Chinese SOTA. 2️⃣ Multi-step reasoning: the biggest gain This is where 0915 improved most — and where it leads its tier, with a small token-efficiency edge. On problems where GLM-5.3 falls into exhaustive enumeration, 0915 repeatedly finds higher-scoring answers with fewer tokens, showing what the author calls real "big-model intuition." The caveat: multi-turn reasoning, which demands in-context learning and reflection, remains mediocre — on par with Chinese peers. 3️⃣ Where 0915 still stumbles Hallucination runs high, and context confusion appears regardless of prompt length — especially when source details are tangled. The agent symptom is subtler than dropping requirements: 0915 keeps every requirement but misreads semi-ambiguous ones — arguably the more dangerous failure mode. 4️⃣ The contrarian bet: a true non-thinking mode Seed is one of the few teams still maintaining a genuinely non-thinking mode, and this version quietly got good: average output dropped from 8K tokens back to ~1K, with no measurable capability regression, a slight gain in complex reasoning, and readable prose intact. For latency-sensitive scenarios that still need some reasoning, the author considers it a legitimate option. 5️⃣ The long march His closing frames 0915 as a rest stop, not a destination: the predecessor's lukewarm market reception forced ByteDance's team into a forced march of biweekly iterations, and better post-training is now visibly paying off in cost per task. But the base is old, and the competition has moved. His last line is worth keeping: sometimes the long way around is the real shortcut. 🔗 Full Reading: 🔗 Key links: Author's monthly logic benchmark (Aug 2026): #ByteDance# #Seed# #LLM# #AIAgents# #LLMBenchmark# #ReasoningModels# #AI#

⚡ 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#

My friend said I need to cosplay Tayama—and honestly that’s my after-work vibes 🚬 And just in case, it’s a paper straw in my hand. #smokingbehindthesupermarketwithyou# #yamadacosplay#

From smoking behind the super market to smoking on bed… with you 🚬 ehehe 😉 Ready fo Tayama 👉 pa./tinyasa #スーパーの裏でヤニ吸うふたり# #smokingbehindthesupermarketwithyou# #tayama# #山田小姐# #在超市後門吸菸的二人#