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DeepSeek V4.1 Flash

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Headlines · 9

  1. OpenRouter adds :nitro fastest-provider routing

    OpenRouter is promoting its :nitro suffix on X: appending :nitro to any model slug routes a request to whichever provider is currently serving that model fastest. In a demo using DeepSeek V4.1 Flash, described as its most-used coding model, the same prompt reportedly took 6.8s without :nitro and 3.9s with it. The figures come from a post the OpenRouter account reshared, so they are vendor claims rather than independently verified results.

    OpenRouter (X) · 🔥 8
  2. Anthropic's Claude Haiku 5.5 debuts as fastest model tested; Sonnet 5.5 tops coding index

    Anthropic released Claude Haiku 5.5 on October 7, replacing the year-old Haiku 4.5; Artificial Analysis scored it 43 on its Intelligence Index (13th among available models, ahead of all "fast" models) and measured 237 tokens per second, the fastest it has tested, though average time to first token is 337 seconds. API pricing is $0.10/$0.01/$0.50 per million input/cached/output tokens, with all prices 5x above 100k tokens. Claude Sonnet 5.5, launched September 28 at Sonnet 5's per-token prices ($2/$0.10/$10 per million), ranked first on Artificial Analysis' Coding Agent Index (68), second on its Intelligence Index (56) and second on Vals AI's Vals Index (67.04%), but Artificial Analysis says another model matches or beats it at every effort level at lower cost per task, and at max effort Sonnet generates about 193,000 output tokens per task. Anthropic says Sonnet 5.5 is the first Sonnet to block reasoning extraction without falling back to another model, part of an effort to make distillation harder.

    The Batch · DeepLearning.AI · 🔥 8
  3. Tencent Hunyuan open-sources Hy Memory 2.0 memory framework and model

    Tencent Hunyuan released Hy Memory 2.0, an open-source stack consisting of the Hy-Memory-30B-A3B memory model (roughly 30B total / ~3B active parameters, Apache-2.0), a memory framework SDK, and MemHub, a local memory app with a CLI and Mac client. The model is trained specifically for the write path inside external memory frameworks, replacing the general-purpose LLM in Mem0, MemOS and Hy-Memory by deciding what to retain and how new evidence updates the memory bank; the company says it ranks first on four benchmarks spanning chat, work and code memory, with work and code memory newly added in 2.0. Per the company and its arXiv paper, across the Mem0, Hy-Memory, MemOS and held-out EverMemOS harnesses on a fixed 20% benchmark subsample, the model scored 55.49% macro-average, above Claude Opus 5 at 54.62%, DeepSeek V4.1 Flash at 53.64% and Qwen3-30B-A3B at 44.37%. IT之家 also reports chat memory accuracy of 72.6% on LoCoMo with per-question tokens falling from 1,159 to 703, and says MemHub plugs into 10 AI tools including WorkBuddy, Cursor, Codex, Qwen Code, DeepSeek Harness and OpenCode.

    腾讯混元 · Hugging Face · 🔥 39
  4. OpenCode makes DeepSeek V4.1 Flash free, capped at 200K context

    OpenCode announced that DeepSeek V4.1 Flash is now offered free on its platform, with the free tier observed to support a 200K context window, down from the model's 1M-context version. The move extends the earlier "Operation Cheepseek" program: OpenCode temporarily raised the model's allowance in its OpenCode Go plan from $15 to $60 in September, then to $80 on October 12, and calls under the free offer no longer count against that allowance. DeepSeek V4.1 Flash launched on September 10, 2026 as a native multimodal MoE model with a 552B-parameter main trunk plus 196B Engram conditional-memory parameters, a causal encoder-decoder design in which the first 20 of 40 language layers are encoders and the last 20 decoders, cutting prefill compute roughly in half; DeepSeek's own platform lists no free API tier. Community reports flag volatile throughput: one user saw up to about 490 tokens/s on the ollama cloud platform, but speeds can fall to 50-60 tokens/s around midday to afternoon and decline as context grows, reaching roughly 50 tokens/s at 200K.

    IT之家 AI · 🔥 8
  5. DeepSeek services down nearly 5 hours; 'DeepSeekHarness' account surfaces

    Starting at 08:11 Beijing time on October 15, DeepSeek's web, app and API services were partially disrupted, with the official status page showing recovery at 13:04 the same day — a total of 4 hours 52 minutes. Affected services included DeepSeek chat, the DeepSeek V4 Pro API and the DeepSeek V4.1 Flash API (one report instead named a V4.1 Pro API). Third-party monitor StatusGator said it received 86 related reports within 24 hours, and users in Luxembourg, Poland and New York also reported no response or very slow service. Around the same time a Bilibili account named 'DeepSeekHarness' appeared, prompting speculation about a new model or promotion of DeepSeek's open-source agent runtime, though no official evidence supports that link.

    IT之家 AI · 🔥 27
  6. Intel CEO calls Chinese AI models 'good enough' and cheaper

    Intel CEO Lip-Bu Tan named DeepSeek, Moonshot's Kimi and Z.ai's GLM at a DBS panel, saying their performance is "already good enough," their token costs are lower and adoption has been "very fast," while adding that the market should decide which model fits which use case. Third-party price trackers put DeepSeek V4.1 Flash at $0.15 per million input tokens and $0.60 per million output tokens in off-peak hours, versus $2 and $10 for OpenAI's GPT-6.1 Sol, though DeepSeek doubles its rates at peak times, Kimi K3's output price is also $10, and GLM 5.3's $4.40 output price is about 44% of GPT-6.1 Sol's. Consulting firm Digital Applied, citing OpenRouter rankings, said Chinese AI providers together passed 45% of weekly token usage in early April, up from under 2% a year earlier, but attributed the rise to per-token cost and specific strengths such as coding and long context rather than benchmark leadership.

    科技新报 · 🔥 0
  7. Zhipu's GLM-5.3 HighSpeed spotted in ZCode at nearly 500 tokens/s

    Zhidx reported on Oct 12 that Zhipu has quietly opened a gray-scale test of a high-speed flagship model, GLM-5.3 HighSpeed, inside its AI coding tool ZCode, available only to randomly selected ZCode subscribers with no public application channel. X user Leo said he was granted access and saw output above 490 tokens/s; for comparison, AI Ping lists DeepSeek V4.1 Flash's official API at 121.34 tokens/s and Zhipu's own GLM-5.3 API at 70.84 tokens/s, and the model is said to carry a 1M-token context window with up to 131,072 output tokens, with pricing undisclosed. Zhipu offered a 400 tokens/s GLM-5.1-highspeed API to selected enterprise users in May, attributing the gains to system-level work with TileRT across the inference engine, scheduling and infrastructure; it has also posted the natively multimodal GLM-5.3-Flash on Hugging Face (320B total / 18B active parameters), whose model card claims it beats GLM-5.2 on benchmarks and real-world workloads at one-tenth the price and approaches Claude Opus 4.8 on coding and agentic benchmarks.

    量子位（原生 RSS） · 🔥 0
  8. OpenAI, Anthropic accused of borrowing DeepSeek tech without credit

    An English op-ed and an X user's screenshot allege that OpenAI's and Anthropic's recent cache price cuts came from adopting DeepSeek's cache optimization without acknowledgment. The Chinese outlet Huxiu's Kanjian Lab checked the figures and found the price cuts themselves are documented — Claude Opus 5.5's cache-read price fell from $0.50 to $0.20 per million tokens versus Opus 5, and GPT-6.1 Sol's cache input is $0.10 per million tokens — but pricing tables do not establish technical provenance, and the original author provided no code or company statement, with the percentage drop also shifting depending on the baseline used. DeepSeek's V4.1 Flash technical report says its global KV cache footprint is reduced roughly 437x versus early V1 at the same context length, about a quarter of the previous V4 Flash. The piece also notes that NVIDIA's Nemotron Nano 2 report explicitly credits DeepSeek's FP8 training recipe, while Anthropic in February accused DeepSeek, Moonshot and MiniMax of improperly obtaining Claude capabilities and acknowledged distillation is common industry practice.

    虎嗅 AI · 🔥 0
  9. Reflection AI debuts Beam, its first open-weight model

    Reflection AI, a 2024 startup founded by two former Google DeepMind researchers and backed by Nvidia, unveiled Beam, a text-only mixture-of-experts model with 501 billion total parameters and 23 billion active, pretrained on 23.8 trillion tokens with a 1 million token context window and aimed at reasoning, coding and agentic work. The company says Beam reaches performance close to Z.ai's GLM-5.2 using roughly one-third to one-quarter of the inference compute, and approaches Qwen3.8-Max, though its own published scores still trail newer Chinese open models such as GLM-5.3, Kimi K3 and DeepSeek V4.1 Flash. Reflection says the weights, a technical report and developer tooling are due this month.

    IT之家 AI · 🔥 0

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