Search: “DeepSWE”

22 stories

DeepSeek releases DeepSeek-V4.1-Flash, a 552B-parameter multimodal MoE model with 1M-token context and KV cache cut to 890 bytes per token.

DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with a 552B-parameter backbone that activates 8B parameters per token during prefill and 16B during decode. It uses a Causal Encoder-Decoder architecture, Compressed Sparse Attention 2, and FP4 KV caching to reduce the global KV cache footprint to 890 bytes per token, roughly one quarter of DeepSeek-V4-Flash. The model was trained from scratch on 45T tokens with context extended to 1M tokens, includes an Engram conditional-memory module (196B parameters), and is released under the MIT license. Post-training uses SFT, RL, and on-policy distillation with large-scale automated synthesis of agentic tasks and a controllable reasoning effort setting from 1 to 100.

Hugging Face trending modelsupdated Sep 11, 00:33 UTC · 4d agofirst Sep 10, 02:17 UTC · 5d agoModel release 3 sources1

Real-SWE: Benchmarking AI models on private, real-world, enterprise codebases

Real-SWE benchmark tests coding agents on licensed private enterprise codebases; top model Fable 5.1 resolves only 38.8% of tasks.

Real-SWE is a new benchmark evaluating frontier AI coding agents on tasks drawn from private production codebases licensed from real companies, spanning billing, tax calculation, and cross-service migrations. Fable 5.1 with Claude Code leads at 38.8% resolution rate (pass@1 over eight runs), followed by GPT-6 Astra Codex CLI at 33.8% and Gemini 3.8 Flash Gemini CLI at 31.2%. Tasks use native harnesses and realistic tooling including Docker, Kubernetes, PostgreSQL, Redis, and Linear; median reference solutions edit 11 files versus 6 for DeepSWE and FrontierCode.

Hacker News · AISep 12, 20:25 UTC · 3d agoAI research HN 45↑ · 31 comments

Sakana AI Launches Fugu Max and Fugu Ultra v2 for Cheaper, Stronger Multi-Agent Orchestration

Sakana AI released Fugu Max and Fugu Ultra v2, API-only orchestrator models that route tasks across model pools to cut costs and boost multi-step reasoning.

Sakana AI released Fugu Max and Fugu Ultra v2, two orchestrator models that route queries across a pool of third-party and open-weights models, including the NVIDIA Nemotron family. Fugu Max is priced at $2 per million input and $6 per million output tokens, 40-60% cheaper per output token than Sonnet 5, GPT 5.6 Terra, and Kimi K3, and reportedly wins 6 benchmarks including Terminal Bench 2.1 and GPQA Diamond. Fugu Ultra v2 targets complex multi-step reasoning, scoring 48.3 on Chartography and 74.3 on DeepSWE. Both are live through Sakana's OpenAI-compatible API only, with no open weights and no EU/EEA availability.

MarkTechPostSep 11, 06:34 UTC · 4d agoModel release

Google releases Gemini 3.8 Flash, its third Flash model in six weeks

Google releases Gemini 3.8 Flash, topping the DeepSWE coding leaderboard six weeks after 3.7 Flash, alongside the cybersecurity-focused 3.8 Flash Cyber.

Google shipped Gemini 3.8 Flash, its third Flash model in six weeks, placing it at the top of the DeepSWE software engineering leaderboard. The companion Gemini 3.8 Flash Cyber showed a reported 2.6x patch accuracy increase for the Chrome security team and found a critical vulnerability in two hours, but is limited to trusted testers and governments. Gemini 3.8 Flash improved over 3.7 Flash on the OSWorld-2.0 computer-use benchmark yet remains far behind Claude Opus.

Ars Technica · AISep 2, 18:13 UTC · 13d agoModel release1

[AINews] Andrew Ng gets into AI Engineering

Andrew Ng relaunches DeepLearning.AI around AI Engineering, defining four core skills from an analysis of 10,000+ job postings and expert interviews.

Andrew Ng, cofounder of Google Brain and Coursera, relaunched DeepLearning.AI with a focus on AI Engineering, basing the curriculum direction on an analysis of over 10,000 job postings plus interviews and surveys. He identifies four key skills: building and deploying AI applications, software engineering fundamentals, effective use of coding agents, and shaping the build with product sense. The Latent Space AI News issue also recaps agent ecosystem developments, including NVIDIA's 'Skill Lift' evaluation proposal showing skill scan scores correlate only weakly (Spearman rho = 0.14) with judged quality, and Konwinski's open-source persistent-agent 'microharness' Headlong, which achieved an unattended self-debugging repair in 48 minutes.

Latent SpaceAug 25, 02:50 UTC · 21d agoAI industry1

[AINews] DeepSeek v4.1-Flash: 763B-P8B-D16B novel causal Encoder–Decoder architecture with vision marks the Return of the Whale

DeepSeek released V4.1-Flash, an open-weight 763B-parameter model with a novel causal encoder-decoder architecture, 1M context, vision input, and MIT license.

DeepSeek launched V4.1-Flash, an open-weight MIT-licensed model using a novel causal encoder-decoder architecture with 763B total parameters and asymmetric active parameters: 8B for prefill and 16B for decode. It supports 1M-token context and text+image input, priced at $0.30 per 1M input and $1.20 per 1M output tokens with a 50% off-peak discount. Artificial Analysis scored it 40 on its Intelligence Index, above DeepSeek V4 Pro 0813, and Vals ranked it the #1 open-weight model ahead of Kimi K3. Baseten shipped day-0 support and Ollama began rolling it out to paid subscribers.

Latent SpaceSep 12, 05:56 UTC · 3d agoModel release1

Cognition launches new SWE-2 model, Rivaling Fable 5.1 and GPT-Astra

Cognition released SWE-2, a coding model post-trained from Kimi K3 that scores 50.0% on FrontierCode 1.1 Main, near Fable 5.1 at 64% lower cost.

Cognition introduced SWE-2, its most advanced coding model, post-trained from the 2.8T-parameter Kimi K3 base model. It achieves 50.0% on FrontierCode 1.1 Main, 73.0% on DeepSWE 1.1, and 92.8% on Terminal-Bench 2.1, beating Grok 4.6 and SWE-1.7 while matching Fable 5.1 and GPT-5.6 Sol at a fraction of the price. The company says it scaled reinforcement learning to the multi-trillion-parameter regime for the first time, using Pareto-informed cost penalties that train all reasoning-effort levels in a single run, tripled RL environments, and NVFP4/FP8 quantization-aware training. SWE-2 is available today in Devin Desktop and CLI, with rollout on Devin Web and Fusion.

Hacker News · AIupdated Sep 12, 13:10 UTC · 3d agofirst Sep 10, 15:29 UTC · 5d agoModel release 8 sources HN 58↑ · 15 comments1

DeepSeek AI Released DeepSeek-V4.1-Flash with 1M Context, FP4 KV Cache, and Cross-Layer Attention Reuse

DeepSeek released open-weight V4.1-Flash, a 552B MoE model with 1M context and FP4 KV cache, beating Opus-5 and GPT-5.6 Sol on agent benchmarks.

DeepSeek-V4.1-Flash is a multimodal Mixture-of-Experts model with a 552B-parameter backbone plus 196B Engram parameters, activating 8B parameters at prefill and 16B at decode, with a 1M-token context window. It introduces a causal encoder-decoder design, Compressed Sparse Attention 2, and FP4 (E2M1) KV cache quantization, cutting global KV cache to 890 bytes per token, about 1/4 of V4-Flash and 437x smaller than V1. Pre-training covered 45T multimodal tokens; the MIT-licensed weights ship on Hugging Face with vLLM and SGLang support. It scores 90.6 on Terminal-Bench 2.1 and 74.2 on DeepSWE v1.1, ahead of Opus-5 and GPT-5.6 Sol.

MarkTechPostSep 10, 07:31 UTC · 5d agoModel release1

GitHub Introduces Project HydraFusion: Runtime Multi-Model Orchestration That Builds a Workflow Per Coding Task in Copilot CLI

GitHub's Project HydraFusion research preview builds per-task multi-model workflows (Single, Cascade, Critique) in Copilot CLI, reporting +4.9 quality at 67% lower cost on TerminalBench 2.1.

Project HydraFusion is a research preview available on all GitHub Copilot plans inside Copilot CLI that treats model routing as workflow selection, choosing among Single, Cascade (draft plus quality gate), and Critique (cross-family reviewer) execution patterns per request. Against Claude Opus 5 baselines at medium reasoning, fixed HydraFusion policies cut estimated cost 67% while adding 4.9 quality points on TerminalBench 2.1, and cut cost 36% and 65% with slight quality dips on DeepSWE and CheckpointBench. Billing is per token at each underlying model's standard rate; there are no open weights or self-hosting options.

MarkTechPostSep 5, 19:40 UTC · 10d agoAI tools & infra1

Google Released Gemini 3.5 Flash Cyber AI, a Specialized AI Model for Vulnerability Hunting

Security AffairsJul 23, 11:44 UTC · Jul 23, 2026Vulnerability

Cognition Releases SWE-2: A Kimi K3 Post-Trained Coding Model That Matches Fable 5.1 on FrontierCode at 64% Lower Cost

Cognition released SWE-2, an RL post-trained coding model from Kimi K3, scoring 50.0% on FrontierCode 1.1 Main and available only inside Devin.

Cognition released SWE-2, its most capable coding model, post-trained with reinforcement learning from Moonshot AI's 2.8T-parameter Kimi K3 base. It scores 50.0% on FrontierCode 1.1 Main, within 1 point of Fable 5.1 at 64% lower cost, and RL reportedly adds 5-6 points over the K3 base on many benchmarks. It is the first Cognition model with selectable reasoning-effort levels all trained in a single RL run using Pareto-slope-matched cost penalties. There are no open weights and no standalone API; it runs only inside Devin (Desktop, CLI, with Web and Fusion rolling out), free for paid tiers through October 10, 2026.

MarkTechPostSep 12, 23:56 UTC · 2d agoModel release1

New Deepseek model V4.1-Flash cuts memory needs for AI agents

DeepSeek released V4.1-Flash, a 552B-parameter open-weight model cutting KV cache needs to a quarter of its predecessor for cheaper million-token AI agents.

DeepSeek released V4.1-Flash, a multimodal model with 552 billion total parameters and 1 million-token context, trained from scratch on 45 trillion tokens of text and images. The model reduces KV cache footprint to about a quarter of DeepSeek-V4-Flash in fast GPU memory and one-eighth offloaded, and 437x smaller per token than DeepSeek-V1, via an encoder/decoder split, 8-16B active parameters per token, and FP4 cache storage. It scores 74.2% on DeepSWE v1.1, narrowly beating Anthropic Opus 5 and OpenAI GPT-5.6 Sol, with gains attributed to data and RL scaling rather than new algorithms. Weights are on Hugging Face under MIT license, also served via API at V4-Flash prices.

The DecoderSep 10, 12:40 UTC · 5d agoModel release1

nex-agi/Nex-N2.5-Pro — new model trending #30 on Hugging Face

Nex-AGI launches Nex-N2.5 agentic model family (mini/Pro/Max), with Max built on a 1.6-trillion-parameter MoE foundation.

Nex-AGI introduced Nex-N2.5, a next-generation family of agentic models in three sizes (mini, Pro, Max) focused on long-horizon agentic tasks including computer use, web browsing, and autonomous program execution. Nex-N2.5-Max is built on a 1.6-trillion-parameter text-only Mixture-of-Experts foundation, marking the company's first complete post-training effort at trillion-parameter scale. Weights will be released open-source on Hugging Face and ModelScope, with hosted access via OpenRouter. Benchmark comparisons against Claude Opus 5, GPT-5.6 Sol, Kimi-K3, GLM-5.3, DeepSeek-V4-Pro-0813, and Qwen3.8-Max show competitive scores on Terminal-Bench 2.1 and SWE-Bench Pro, though weights were listed as "coming soon" at publication.

Hugging Face trending modelsSep 8, 11:01 UTC · 7d agoModel release1

nex-agi/Nex-N2.5-mini — new model trending #30 on Hugging Face

Nex-AGI releases Nex-N2.5 agentic model family (mini, Pro, Max) with a 1.6-trillion-parameter MoE Max, open weights, and hosted access via OpenRouter.

Nex-AGI launched Nex-N2.5, a family of agentic models in mini, Pro, and Max sizes, with the Max version built on a 1.6-trillion-parameter text-only Mixture-of-Experts foundation and the company's first complete post-training effort at trillion-parameter scale. The models target long-horizon computer use, web browsing, and visually grounded agentic tasks, with expanded agent training environments. Reported benchmarks include Max scoring 86.1 on Terminal-Bench 2.1 and 65.7 on SWE-Bench Pro, trailing Claude Opus 5. Weights are being released openly on Hugging Face and ModelScope, with hosted access through OpenRouter.

Hugging Face trending modelsSep 8, 05:08 UTC · 7d agoModel release1

[AINews] GPT-6 Astra: OpenAI’s biggest LLM launch of all time

OpenAI launched GPT-6 Astra, its new flagship model, claiming state-of-the-art computer use, software engineering, math, and cybersecurity capabilities.

OpenAI launched GPT-6 Astra as its new flagship model, describing it as its most intelligent and aligned model with state-of-the-art computer use, software engineering, and math/science capabilities. Pricing is $10/$50 per 1M input/output tokens standard ($20/$100 fast tier), rolling out first to limited organizations, then ChatGPT Plus/Pro/Business/Enterprise, the API, and AWS. OpenAI claims 99.9% on ARC-AGI-3, 98% on FrontierMath Tier 4, and 100% on ExploitBench. Artificial Analysis scored Astra 67 on the Coding Agent Index and 61 on the Intelligence Index, behind Claude Fable 5.1, and the system card drew attention for reporting decreased chain-of-thought monitorability despite alignment gains.

Latent SpaceSep 4, 05:18 UTC · 11d agoModel release2

Google’s Gemini 3.8 Flash takes on bigger AI models at a lower cost

Google released Gemini 3.8 Flash with a security-focused Cyber variant that produces 2.6x more correct patches and found a critical vulnerability in under two hours.

Google launched Gemini 3.8 Flash for developers and a gated Gemini 3.8 Flash Cyber model reserved for vetted security teams through the new Fairwind program. The company says the model beats most larger frontier models on the DeepSWE v1.1 engineering benchmark at lower cost, and Chrome Security reports the Cyber variant produced 2.6 times more correct patches than the best commercial models while Google's Cloud Vulnerability Research team found a critical foundational vulnerability in under two hours. The models show significant prompt-injection robustness gains measured by Gray Swan and carry CBRN misuse safeguards, with cyber-offense restrictions on the standard version. Pricing matches Gemini 3.7 Flash at $0.75 per million input tokens and $3.75 per million output tokens.

Help Net SecuritySep 3, 00:00 UTC · 12d agoModel release

Introducing Gemini 3.8 Flash and 3.8 Flash Cyber

Google DeepMind releases Gemini 3.8 Flash and 3.8 Flash Cyber with improved reasoning, coding, and cybersecurity vulnerability detection and automated patching.

Google DeepMind introduced Gemini 3.8 Flash, its strongest reasoning and coding model, priced at $0.75 per million input and $3.75 per million output tokens, alongside Gemini 3.8 Flash Cyber, a cybersecurity-specialized variant offered to trusted defenders via the Fairwind Program. The Cyber variant shows frontier-level autonomous vulnerability discovery on CyberGym, exceeds 70% success on an internal benchmark spanning 20 programming languages, and scores 47.2% pass@1 on the CWE-Bench patching benchmark. Google reports it produced 2.6x more correct Chrome vulnerability patches than larger commercial models and found a critical foundational bug in under 2 hours.

Google DeepMindSep 2, 16:18 UTC · 13d agoModel release

[AINews] Claude Fable/Mythos 5.1: new SOTA model, 75% cache price cut but 70% more output tokens

Anthropic launched Claude Fable 5.1 and Mythos 5.1, claiming new SOTA benchmarks, with 75% cache-read price cut and 1M-token context.

Anthropic released Claude Fable 5.1 and Mythos 5.1 as flagship models for coding and knowledge work, with a 1M-token context window and pricing of $10/$50 per million input/output tokens and cache reads cut 75% to $0.25. Artificial Analysis Intelligence Index scored Fable 5.1 at 66 versus 63 for Claude Opus 5, with HLE at 59.1% and Terminal-Bench v2.1 at 91.4%, though per-task cost rose ~20% due to 1.7x output token usage. Community analysis suggested Fable and Mythos may share underlying weights with different safety/routing behavior, and release notes highlighted Enterprise Frontier Safeguards and zero-data-retention support.

Latent SpaceSep 2, 07:46 UTC · 13d agoModel release2

deepseek-ai/DeepSeek-V4-Flash-Vision-Exp — new model trending #10 on Hugging Face

DeepSeek released DeepSeek-V4-Flash-Vision-Exp, its first experimental multimodal model, with large multimodal agent benchmark gains over V4-Flash-0731.

DeepSeek AI published DeepSeek-V4-Flash-Vision-Exp, an experimental multimodal model built on the DeepSeek-V4-Flash architecture with added visual modules and continued training. It scores 83.9 on Terminal Bench 2.1 and 36.5 on ApexBench Pass@1 versus 26.2 for DeepSeek-V4-Flash-0731, while remaining comparable to Opus-4.8 on several benchmarks. The MIT-licensed repository ships a tokenizer, OpenAI-style and TXT prompt encoding, and a minimal PyTorch inference implementation, with vLLM and SGLang deployment recipes.

Hugging Face trending modelsAug 31, 06:16 UTC · 15d agoModel release1

[AINews] OpenAI to reach AGI bar by end-2026

OpenAI chief scientist Jakub Pachocki says unreleased Astra model meets the 'Automated AI Research Intern' goal; Altman expects internal AGI declaration by December 2026.

OpenAI chief scientist Jakub Pachocki says the unreleased Astra model fulfills the September 2026 'Automated AI Research Intern' target. Sam Altman told TIME he expects OpenAI to declare AGI achieved internally by December 2026. The roundup also covers Zhipu's GLM-5.3-Flash (320B total parameters, 18B active, 1M context), Google's Gemini Omni 1.1 Flash video model topping the Text-to-Video Arena, and the $399 open-source Microduck biped robot from Pollen Robotics and Hugging Face.

Latent SpaceAug 28, 07:12 UTC · 18d agoAI industry

unsloth/Qwen3.8-Flash-Next-GGUF — new model trending #21 on Hugging Face

Qwen released Qwen3.8-Flash-Next, an experimental 125B-parameter open-weight MoE previewing the Qwen4 architecture, with Unsloth shipping optimized GGUF quants.

Qwen released Qwen3.8-Flash-Next, an experimental open-weight preview of the architecture planned to underpin Qwen4. The model has 125B parameters with 6B activated, 512 experts (10 routed plus 1 shared), Qwen Sparse Attention (QSA), Gated DeltaNet, Gated Residual, and n-gram embeddings, with 262,144-token native context extendable to 1,000,000 tokens. Unsloth provides Dynamic 3.0 GGUF quantizations, and multi-token prediction (MTP) delivers 1.3-1.7x faster inference via llama.cpp or Unsloth Desktop.

Hugging Face trending modelsAug 26, 12:29 UTC · 20d agoModel release1

[AINews] 10% worse, 100x cheaper, 10000x faster: Why Simulation is taking over

Latent Space argues AI training pipeline stages—rewards, data, teachers, curricula, environments—are flipping from human-made to model-made simulation.

Latent Space's AINews essay traces how each component of AI training has turned synthetic since 2022: reward models (InstructGPT, RLAIF), synthetic pretraining data (Microsoft Phi, NVIDIA Nemotron-4 340B), model teachers (Alpaca, DeepSeek-R1 distillation), and self-generated curricula (Self-Rewarding Language Models, SPIN). In 2026 it highlights Karpathy's autoresearch loop—700 experiments yielding 20 kept improvements, cutting GPT-2 training time from 2.02 to 1.80 hours—and Z.ai's GLM-5.3 fully synthetic RL environment, judging, and verification stack. It frames these shifts as 'simulation': 10% worse but 100x cheaper and 10,000x faster than human equivalents.

Latent SpaceAug 22, 07:36 UTC · 24d agoAI industry