On November 5 Ant Group’s Ant Ling (@AntLingAGI) launched Ling-3.2-flash: a 560B-total / 25B-active native multimodal MoE with a 1M context window and native image, text and video input. The official changelog targets complex visual understanding, multimodal long-horizon agents, 3D understanding and interaction, and medical multimodal work; APIs are OpenAI- and Anthropic-compatible and free for the first two weeks. The launch post cites vendor evals versus Claude Opus 5 (max): OmniDocBench1.5 92.21 vs 90.56, ZEROBench(Sub) 43.71 vs 39.52, Vision2Web 85.86 vs 80.20. DesignArena placed it about #10 on Image-to-Frontend with Elo 1238.
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Key Takeaways
Specs (official docs): 560B total / 25B active; 1M context; native image/text/video; four focuses: complex vision, multimodal long-horizon agents, 3D, medical multimodal
Availability: chat UI plus OpenAI/Anthropic-compatible APIs; changelog says free for first 2 weeks, then original pricing resumes
Vendor evals (official X): OmniDocBench1.5 92.21 vs Opus 5 90.56; ZEROBench(Sub) 43.71 vs 39.52; Vision2Web 85.86 vs 80.20
Third party: DesignArena Image-to-Frontend ~#10, Elo 1238; official price page stresses 2-week free promo and has not listed 3.2 USD rates yet
Hands-on: model id Ling-3.2-flash; use image_url / video_url blocks; weights not on Hugging Face (API-first)
Voice model company Cartesia launched Managed Agents on Oct 29, an end-to-end voice agent platform for enterprise phone calls that tunes its own speech-to-text and text-to-speech models together with the agent harness, and bundles telephony, knowledge bases, tool calls and outbound batches. Cartesia says it ranks #1 on Artificial Analysis's Speech Agent Arena with a 1115 Elo and scores 87.4% accuracy and 90.7% experience on ServiceNow's EVA-Bench (vendor-reported, with GPT-5.5 as the reasoning model). Launch pricing is 50% off at $0.03 per minute.
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Key Takeaways
On Artificial Analysis's Speech Agent Arena blind preference test, Managed Agents ranks #1 of 30 agents with a 1115 Elo, ahead of Gemini 3.1 Flash Live Minimal (1099), Grok Voice 3.0 (1064) and GPT-Live-1 (1041) (Cartesia blog, as cited by the vendor)
First-attempt results on ServiceNow EVA-Bench across 200+ customer service, IT service and HR scenarios: 87.4% accuracy and 90.7% experience, versus 73.7%/71.2% for Grok Voice 3.0 and 59.0%/61.6% for GPT Realtime 2.1 (EVA-Bench)
All these benchmark runs use GPT-5.5 as the reasoning model; Cartesia's internal evaluation says 85% of calls had no turn-taking issues, versus 67% for other agents
Launch pricing is 50% off at $0.03 per minute; Cartesia says the platform has already handled tens of millions of conversations in finance, retail, hospitality and healthcare
It supports 44 languages; English, Spanish, Hindi, French and Japanese can switch mid-call today, with the rest coming soon
Google DeepMind announced Gemini 4 Carbon on Oct 28, calling it its strongest model, just two weeks after Gemini 4 Argon. The official blog says it ranks first on v5.0 of the Artificial Analysis Intelligence Index at 59 and leads three cybersecurity evaluations: 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation and 71.0% on CWE-bench for patching; it also gains 89 points to rank fourth on WebDev Arena at 1769. Access starts this week for a subset of cyber defenders in the Fairwind program, with developers, businesses and consumers to follow. Introductory pricing matches Argon at $2 per million input tokens and $10 per million output tokens, rising to $4 and $20 afterwards.
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Key Takeaways
Positioning: Google calls it its strongest model, aimed at cybersecurity and long-horizon agentic work; internal teams saw it run for days to root-cause and patch subtle bugs across large codebases and low-level systems
Official scores (evaluated without guardrails): 88.2% on SEC-bench Pro, 54.8% on ExploitGym and 71.0% on Collinear's CWE-bench; 59 and first place on the Artificial Analysis Intelligence Index v5.0; +89 to fourth place on WebDev Arena at 1769
Safety and alignment: successful cheating on Terminal-Bench 4.0 fell to 0.0% with a 0.4% attempt rate, high-severity misalignment alerts in internal live coding dropped by almost 80%, unsupported claims are about half of Argon's, and the Gray Swan indirect prompt injection attack success rate fell from Argon's 0.7% to 0.0%
Internal examples: more than 2x Argon's success rate at generating secure code, and the Chrome team reports more than 10x better high-quality patches than another lab's large frontier model
Rollout: a subset of cyber defenders in the Fairwind program first, alongside the U.S. government's voluntary pre-release testing; no developer API yet; introductory pricing of $2 / $10 per million tokens with cached input 95% off, then $4 / $20
Microsoft AI announced on Oct 27 that MAI-Image-2.6 and MAI-Image-2.6-Flash are generally available in Microsoft Foundry, with multi-reference editing from up to five images, Bing web grounding, output up to 1.5K and published per-token pricing. The same day its next image editing model, MAI-Image-2.7-Preview, reached #3 overall in Arena's Image Edit leaderboard at 1470 points, 45 points above 2.6; for now it is available only for editing in MAI Playground.
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Key Takeaways
MAI-Image-2.6 and MAI-Image-2.6-Flash are generally available in Microsoft Foundry, and Microsoft says 2.6 is coming soon to Adobe Firefly
Official pricing: 2.6 costs $5 text input, $8 image input and $38 image output per 1M tokens; Flash costs $1.75, $2.50 and $19
2.6 supports multi-reference editing with up to five images, Bing web grounding, dynamic aspect ratios and output up to 1.5K
Per Arena, MAI-Image-2.7-Preview is #3 overall in Image Edit Arena at 1470 points, 45 points above 2.6 (#7), putting Microsoft AI #2 among labs
Per Artificial Analysis, 2.7-Preview is #2 on AA-Image-Editing v2.1 and beats 2.6 on all 7 editing actions, ranking #1 on 4 of them
On Oct 22 Canva released Canva Craft, the first image-generation foundation model trained from scratch by its in-house lab, Canva Research. It works in two stages: a planning model writes out the composition (elements, spatial relationships, hierarchy and any text), then a rendering model produces the image at up to 2K across more than 30 aspect ratios. Training leaned heavily on millions of human-made templates from Canva's library, such as posters, social posts, slides and infographics. Canva says it debuted 4th overall on the crowdsourced DesignArena leaderboard and 1st in the People & Portraits and Landscapes & Nature categories. It is available now to all Canva users in Dream Lab and to developers through the Connect API as craft_v1_2k, with Canva MCP access coming soon.
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Key Takeaways
Two-stage design: a planning model writes the composition (elements, relative scale, hierarchy, text), then a rendering model outputs up to 2K across 30+ aspect ratios (announcement)
Vendor-reported ranking: 4th overall on DesignArena, 1st in People & Portraits (of 87 models) and Landscapes & Nature (of 85), 2nd in Abstract & Patterns and 4th in Products
Training emphasized millions of human-made Canva templates; designers judged more than 25,000 head-to-head image pairs against leading models
For developers: POST /v1/image-generations on the Connect API with model set to craft_v1_2k, which spends the user's AI credits (API docs)
Prompts work in any language and in-image text covers 55 languages; the faster, cheaper Craft-fast and Craft-ultrafast variants and MCP access are not out yet
On Oct 20, Krea said Krea 3, the image-editing model it released on Oct 15, has entered several third-party leaderboards. On Arena's Multi-Image Edit board, Krea 3 Ultra ranks #4 at 1435±11 (preliminary, 2,991 votes), behind three OpenAI models; it is #8 on single-image editing. On Artificial Analysis, Ultra scores 1151 Elo (#5) and Fast scores 1125 (#11). Krea 3 edits at native 2K, returns most edits in under 8 seconds and costs $0.06 per image.
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Key Takeaways
Arena Multi-Image Edit: Krea 3 Ultra is #4 at 1435±11 on 2,991 votes, marked preliminary; the top 3 are two GPT-Image-2.5 variants and GPT-Image-2
Arena single-image Image Edit: Krea 3 Ultra is #8 at 1411±11 on 2,619 votes
Artificial Analysis Image Editing v2.1: Ultra is #5 at 1151 Elo (statistically tied for #3 with Nano Banana 2.1 and MAI-Image-2.6); Fast is #11 at 1125
Design Arena Image Editing: #4 of 51 models at 1298 Elo
Vendor figures: most edits under 8 seconds, native 2K, $0.06 per image, up to 6 reference images
On Oct 19 (UTC) fal released FLUX 3 Max, a video model built by fal Research with Black Forest Labs through post-training, reinforcement learning and inference optimization on top of the FLUX 3 foundation model. It handles text-to-video, image-to-video and keyframes, up to 20 seconds, at 720p or 1080p, with audio on by default. On Artificial Analysis's preliminary image-to-video arena it scores 1,139 Elo, level with Seedance 2.5 and within the confidence interval of leader MiniMax H3 (1,141), and fal says a 10-second clip takes about 12.45 seconds. A 75% launch discount runs until Nov 7, putting 720p at $0.0425 per second.
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Key Takeaways
Artificial Analysis AA-Video-I2V v1.1 (preliminary): FLUX 3 Max at 1,139 Elo, level with Seedance 2.5 and just under MiniMax H3 at 1,141; "tied #1" rests on overlapping confidence intervals
Speed (fal-reported, 10-second 16:9 at 720p): FLUX 3 Max 12.45s, Gemini Omni Flash 1.1 47.37s, Seedance 2.5 277.80s; the press release quotes 10.95s instead
Versus the original FLUX 3: Elo up from 1,089 to 1,139 (+50) and a 10-second clip down from 126.44s to about 12s (11.5x per the press release, about 10x per the product page)
Pricing: promotional $0.0425/s at 720p and $0.0725/s at 1080p until Nov 7, then $0.17/s and $0.29/s, billed per output second
API options: 5-20 second duration, multiple aspect ratios, reasoning_level (low/high), generate_audio on by default; model IDs blackforestlabs/flux-3-max/*
Meta officially disclosed its gigawatt-scale AI supercluster Prometheus in New Albany, Ohio, housing hundreds of thousands of frontier GPUs with 85%+ pretraining goodput and 90%+ in reinforcement learning. Powered by this infrastructure, Meta's latest coding flagship Muse Wonder Full surged to #6 globally on LMSYS Code Arena: WebDev with 1726 Elo points (surpassing Claude Sonnet 5.5 and Gemini 4 Argon), lifting Meta to the #3 AI research lab worldwide.
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Key Takeaways
Meta unveiled its gigawatt-scale Prometheus supercluster in Ohio with hundreds of thousands of GPUs, delivering 85%+ pretraining goodput
Exceeded 90%+ goodput on RL workloads, recording ~47,000 GPU-hours MTBF during Muse Wonder's training cycle
Muse Wonder Full scored 1726 Elo on LMSYS Code Arena: WebDev (#6 globally), outperforming Claude Sonnet 5.5 (+16) and Gemini 4 Argon (+48)
Elevated Meta to the #3 AI lab worldwide; confirmed Muse Glimmer 30B open weights capable of 4-bit local execution on a single consumer GPU
LMSYS officially announced a 14-day exclusive direct testing window for OpenAI flagship vision generative model GPT-Image-2.5 Sunburst in Direct Mode (active until Nov 2, 8 a.m. PT). Currently holding the undisputed #1 ranking across LMSYS Text-to-Image, Image Edit, and Multi-Image Edit arenas, the model can now be directly chosen from the dropdown menu before returning exclusively to anonymous Battle and Agent Mode.
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Key Takeaways
LMSYS Arena enabled 14-day exclusive Direct Mode testing for OpenAI GPT-Image-2.5 Sunburst until November 2 at 8 a.m. PT
The model holds the undisputed #1 ranking across Text-to-Image, Image Edit, and Multi-Image Edit Arenas
Demonstrates superior spatial reasoning and ambient lighting consistency across multi-image composition, mitigating visual drift
Following Direct Mode, the model will remain anonymously active in Battle and Agent modes to power Elo ratings
MiniMax officially announced M3.1 Flash, its next-generation lightweight frontier model (previously dominating leaderboards as the secret model Space Bunny across 200T+ tokens). Featuring a native 1M context window, flat-rate API pricing of $0.15 / $0.60 per 1M input/output tokens ($0.015 cache reads), and 3x faster inference at 1/3 the cost of M3. It surged +106 points to 1588 on LMSYS Code Arena: WebDev (#12 among open models), with full weights set for open release on October 23.
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Key Takeaways
MiniMax officially launched M3.1 Flash, unveiling that it was the mystery model Space Bunny evaluated across 200T+ tokens
Features native 1M context with flat-rate API pricing of $0.15/$0.60 per 1M tokens ($0.015 cache reads), offering 3x speed at 1/3 cost of M3
Debuts on LMSYS Code Arena: WebDev at 1588 Elo (+106 pts over M3), ranking #12 among open models with major leaps in Gaming (+179) and Reference Design (+155)
Artificial Analysis benchmark confirms Intelligence Index of 42 and HLE of 48% at half the token consumption of rivals; weights release on Oct 23
Arena announced a major update to Code Arena: WebDev: DeepSeek-V4.1-Flash-20261017 (Max) clinched the #1 position among open models with 1670 points, surpassing Kimi K3 (Max) at a 10x lower cost. Ranking #12 overall, it outperforms Claude Opus 5 (High) and narrows the gap with closed-source frontier GPT-6 Sol (Max) to roughly 10 points, resetting the Pareto efficiency curve at $0.30 input / $1.20 output per million tokens.
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Key Takeaways
DeepSeek-V4.1-Flash-20261017 (Max) captured the #1 open model position in Arena Code Arena: WebDev with 1670 points, surpassing Kimi K3 (Max)
Ranks #12 overall on the leaderboard, defeating Claude Opus 5 (High) and trailing closed-source champion GPT-6 Sol (Max) by only ~10 points
Resets the Pareto efficiency frontier at $0.30 input / $1.20 output per million tokens, cutting operational costs by ~10x against rival models
Gains +52 points over its previous 20260910 checkpoint in just over a month
Arena released an update to the Arena Alignment Index built from 90,000+ real-world agent sessions: Meta's Muse Wonder (Max) ranks #6 by lab at 77.5% (2.09% unauthorized action rate, 14.41% deceptive completion rate), Reflection AI's open-weight Beam-1.0-501B-A23B debuts at #7 with 75.3% (4.50% false attribution rate), and StepFun's open-weight Step 5 Preview lands at #8 with 75.1% (2.11% unauthorized action rate).
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Key Takeaways
Built from 90,000+ real-world agent sessions, the Arena Alignment Index evaluates Unauthorized Action (UA), False Attribution (FA), and Deceptive Completion (DC)
Meta's Muse Wonder (Max) ranks #6 by lab at 77.5%, posting the lowest unauthorized action rate (2.09%) and deceptive completion rate (14.41%) of the new cohort
Reflection AI's Beam-1.0-501B-A23B (#1 US open model on Agent Arena) debuts at #7 with 75.3% and leads the newcomers with a 4.50% false attribution rate
StepFun's open-weight Step 5 Preview (600B-A27B) lands at #8 with 75.1% and a 2.11% unauthorized action rate, nearly matching Muse Wonder
Three new frameworks advance web and multimodal agent post-training: Salesforce AI Research's CLIFT (arXiv:2610.06829) certifies URL-conditional self-verification questions for dense RL rewards and judge-free Conformal Trajectory Selection (CTS), propelling Gemma-4 31B to a 74.6% SOTA on WebArena Infinity (beating Gemini 3 Flash at 70.1%) and lifting GPT-5.5 to 53.7% on VisualWebArena. Meanwhile, MMSearch-OPD (arXiv:2610.22240) boosts 9B multimodal deep search by +15.3 points to 60.2%, and Northeastern University's SCOPED-8B (arXiv:2610.22217) uses skill-decoupled on-policy expert distillation to reach 73.13% across 8 benchmarks—surpassing Qwen3-VL-32B (72.01%) and Claude Opus 4.6 (72.86%).
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Key Takeaways
Salesforce's CLIFT conformal self-verification propels Gemma-4 31B to 74.6% on WebArena Infinity (+12.8 pts over base, beating Gemini 3 Flash at 70.1%) with judge-free test-time trajectory selection
Transferring the open-trained CLIFT question bank to GPT-5.5 sets a 53.7% SOTA on VisualWebArena, while MMSearch-OPD boosts 9B multimodal deep search accuracy by +15.3 points to 60.2%
Northeastern University's SCOPED-8B reaches 73.13% average accuracy across 8 VLM benchmarks via skill-decoupled on-policy expert distillation, beating Qwen3-VL-32B (72.01%) and Claude-Opus-4.6 (72.86%)
Robotics research typically separates world models (predicting scene transitions) from control policies (generating actions), fragmented by proprietary robot-specific kinematic formats. Sber AI researchers introduced GreenWAM, a foundational world model for robotics. GreenWAM unifies 20 robot embodiment families (from bimanual arms to dexterous humanoids) under a common joint coordinate space, co-training across visual, action-conditioned, and world-action objectives with specialized action-expert heads. GreenWAM achieves top-tier 3D trajectory and depth accuracy on WorldArena 1.0, establishes state-of-the-art policy benchmark scores on RoboTwin 2.0 and LIBERO-plus, and successfully drives real-world humanoid robots outperforming Cosmos 3 Edge and pi_0.5.
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Key Takeaways
Unified anatomical action space: standardizes joint coordinates across 20 diverse robot families, breaking down proprietary kinematic data silos
Dual world model and policy capability: unifies visual scene generation with real-time policy execution via action-conditioned multi-task pretraining
Real humanoid deployment: tops WorldArena 1.0 3D trajectory benchmarks and outperforms Cosmos 3 Edge and pi_0.5 on physical humanoid manipulation
Arena released an agentic version of its Max router. Trained on 2.5M+ real Agent Arena sessions, it picks between Fable 5.1 (Max), Astra (Max) and Astra (Medium) per task based on complexity, task type and cost. In a live test it ranked #1 in Agent Arena with a +14.7% score at a $3.27 median cost per task — 0.3% better than the top public model, Fable 5.1 (Max), at 37.5% lower cost — and sent 67% of coding sessions to the cheaper Astra (Medium). It is free to try in Agent Mode for a limited time; API access is via a signup form.
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Key Takeaways
Training data: 2.5M+ real Agent Arena sessions; Arena tested 60+ agentic models over the past 4 months
Live test: #1 in Agent Arena with +14.7% score and a $3.27 median cost per task — 0.3% above Fable 5.1 (Max) at 37.5% lower cost
