Search “DeepSeek”

When replying to particle api, OpenRouter suggested following its new account @ModelsOnOR to get the latest news on OpenRouter model releases. Previously, OpenRouter had introduced that Particle-1 was already live, saying that it uses 28–52% fewer output tokens than DeepSeek V4.1 Flash on four coding benchmarks.

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based on Qwen3.6-35B-A3B. The model scored 60.9 and 40.0 on the paper reproduction test PaperBench and the scientific research challenge test FrontierScience, respectively, higher than GLM-5.3's 42.5 and 30.7; it scored 84.0 on the tool-calling test BFCL V4, higher than DeepSeek-V4.1-Flash-1017's 75.0. The model has been released with open weights under the Apache 2.0 license, and the official release provides approximately 72GB of BF16 weights and GGUF quantized versions at various precisions.

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ability to build a complete product feature by feature starting from an empty project, comprising 17 products, 241 feature requests, and 544 test plans. GPT-6.1 Sol leads with a 65% pass@1, GPT-6 Astra is at 60%, and Opus 5.5 is at 49%, with all three passing over 90% of test plans. Opus 5.5 completes a build in about 70 minutes on average, roughly 2.5 times faster than Sol; DeepSeek V4.1 Flash passes 77% of test plans at about $2.50 per build, but no open-weight model can pass a complete build.

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According to The Information, DeepSeek and Moonshot AI made extensive use of outputs from OpenAI's most powerful model, GPT-6 Astra, when training their latest models, involving Kimi K3.1 Pro and V4.1-Flash 1017. This kind of distillation requires models to expose hidden reasoning steps, and both OpenAI and Anthropic prohibit the practice. Claude was once the primary target, but Anthropic says the new methods have cut the amount of data attackers can obtain by 90% to 99%. In late September, Open

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activated per step, and supports text and image inputs. On Terminal-Bench 4.0, its score rose from 28% in the preview version to 41%, slightly above GLM-5.3's 40%, but on the new version of the Artificial Analysis Intelligence Index it scored only 26 points, below the 32 points of DeepSeek V4.1 Flash and GLM-5.3. API pricing is $0.70 per million input tokens and $2.10 per million output tokens, under a modified MIT license; using it as a commercial cloud service requires separate authorization.

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orld's best open-weight model in automated coding and cybersecurity, and making it freely available. Pierre Stock, Mistral's VP of Science, said the model was trained on increasingly difficult cybersecurity and coding problems, enabling it to match or surpass models from Chinese labs such as DeepSeek and Z.ai. Stock also stressed that the West needs its own advanced open-weight models, because closed models can be shut down remotely, whereas open weights cannot have access cut off once released.

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gents to execute, covering product selection, building, launch, and first-month profitability. It has two layers: a human-facing playbook/00–13 chapters and an Agent-facing AGENTS.md (Phase 0–14, with each step marked [AGENT] or [HUMAN]); before starting, one must pass the Gate A scoring. The tech stack is fixed on Cloudflare Workers, D1, Better Auth, OpenRouter routing to DeepSeek, Superwall plus Stripe, etc., and explicitly lists a long set of non-choices such as Vercel, Supabase, and Next.js.

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-scoring Opus 5.5 scores only 45%, showing the benchmark is far from saturated. Compared with version 1.1, reference solutions modify on average 67% more files and 89% more lines of code, the codebase size is 25% larger, task-covered repositories increase by 55%, and blocking scoring criteria increase by 43%. DeepSeek V4.1 Flash drops from 43.9% to 19.3%, and Kimi K3 drops from 44.2% to 17.7%; open-source models, especially Western open-source models, still have significant room for improvement.

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g tasks that have been retired or not included in the Intelligence Index), for cross-time comparison; the Intelligence Index is still used to measure the intelligence level of current models. Its data shows that after the emergence of reasoning models, the frontier has improved by about 65 to 70 points per year, roughly twice the 2023 rate; open-weight models typically lag closed-source models by about 4 months, and DeepSeek V4.1 Flash 1017 ranks highest among open-weight models with 324 points.

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enterprise agents, responsible for providing production inference and post-training support for agents built on Postman Agent Stack. Baseten says its seven model APIs released in 2026 all ranked in the top two in Artificial Analysis's throughput and latency rankings, with Beam 1.0, DeepSeek V4.1 Flash and others taking first place. Harvey used Baseten to post-train a 27B open-source model, matching closed-source frontier models on legal agent benchmarks, while the latter cost about $50 per task.

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Prime Intellect has released Prime Mega MoE, which builds on the Mega MoE kernel from DeepSeek's open-source DeepGEMM library by adding a fused and overlapped backward pass, making it usable for training rather than inference only. In the prime-rl trainer, the full training step for Qwen3 MoE models is 32% to 34% faster than the baseline and 48% to 55% faster than TorchTitan; the single-layer forward pass is up to 3.11x faster (from 12.0 ms to 3.9 ms), but the backward pass for Qwen3-235B-A22B i

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6.1 Sol (max) at 55 points, with Gemini 4 Argon (high) at 50 points. The gap between open-weight models and the leaders has widened from 3 points to 12 points; the released-weight DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) both score 32 points. The per-task cost of leading models differs by more than 13 times: GPT-6.1 Terra (max) is $0.96, while Claude Fable 5.1 (max) is $13.04. A Progress Index v1 was also released, placing models released since November 2022 on the same capability scale

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DeepSeek founder Liang Wenfeng, with a net worth of 280 billion yuan, debuted on the Hurun Rich List at No. 7 nationwide. The two logics he emphasized at a closed-door meeting were: proactively open-sourcing core capabilities so the entire industry grows on his foundation, and, amid the boom of wrapper monetization at the application layer, restraining short-term gains and relentlessly focusing on infrastructure. Sources say he has recently been intensively catching up on embodied intelligence i

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cing cut its costs by 80% to 90%. A U.S. bank with more than $7 billion in assets doubled its ChatGPT seats to 500 for free, paying less than $7 per user instead of the usual $20 or more. OpenAI is partly sacrificing its own profits to gain market share, but it has also achieved real price cuts through its Sol and Luna model lines; according to an investor memo, Anthropic's growth slowed from Q2 to Q3, and both companies also face low-price pressure from Chinese models such as DeepSeek and Kimi.

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After having Doubao, Kimi, DeepSeek, GPT, Qwen and Claude each write 5 episodes of a restaurant business-war short drama, the 30 scripts were highly homogenized: noodle shops appeared 6 times and all six models chose a noodle shop for their first version, "conscience" appeared in every script, and "rules" in about 80%. Claude's dialogue is short and rhythmic, with an apprentice urging a price cut in every version, making it closest to the "heartburn style"; Doubao packs in punchy lines but 3 ver

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