Ant Ling ✓
@AntLingAGI
MoE model series with foundation (Ling), reasoning (Ring) and any-to-any (Ming) from Ant Group’s AGI initiative, @TheInclusionAI. https://t.co/6LEkFlo2cq
13 414Followers
5Following
651Posts total
1.9MViews on collected posts
Последние посты
Ищем новые посты…
@AntLingAGI Could you share results for source verification separately from final-answer accuracy across the three formats?
168 views · 3 likes
· 0 reposts · 0 replies
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With the FP8, FP4 and INT4 quantized versions now available, we hope more developers will explore how Ling-3.0-flash-Fin can support real-world financial workflows and build applications for different deployment needs.
Join the Ling community on Discord:
https://t.co/hUwiZSBpfX
405 views · 13 likes
· 1 reposts · 0 replies
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The quantized checkpoints are available now:
Hugging Face:
https://t.co/1I0VHc1Iha
https://t.co/MdlPyFw2A9
https://t.co/Eip0yNylT2
ModelScope:
https://t.co/jwnad8n5YR
https://t.co/YbD18O8Fa1
https://t.co/KYqLMQtCiR
868 views · 21 likes
· 2 reposts · 1 replies
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See Ling-3.0-flash-Fin in action. 🎬 https://t.co/RVMYtT9X6h
777 views · 19 likes
· 1 reposts · 2 replies
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1:31
Three quantized versions of Ling-3.0-flash-Fin are now available: FP8, FP4 and INT4.
If you’re building real-world financial workflows, you can choose the version that best fits your infrastructure, memory and efficiency needs. 🧵 https://t.co/R9Mqq9abJb
431.2K views · 208 likes
· 45 reposts · 41 replies
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@AntLingAGI Those numbers are impressive 👏 Big win for open models
1 views · 0 likes
· 0 reposts · 0 replies
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Try Ling-3.0-flash-VL free for two weeks on Computrix. API access requires a Token Plan (from $20), fully refundable if used exclusively with Ling-3.0-flash-VL. Any paid-model use makes the plan non-refundable. Use Base64 for image or video inputs. https://t.co/Ng6b4tjVAL
436 views · 1 likes
· 0 reposts · 0 replies
Open on X →
Questions or feedback? Join the Ant Ling community on Discord—we’d love to hear from you:
https://t.co/hUwiZSBpfX
Ling-3.0-flash-VL will be open-sourced soon. Stay tuned—this is only the beginning. 🚀
459 views · 4 likes
· 0 reposts · 1 replies
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@AntLingAGI 这个世界确实需要出了编程和 cowork 之外的模型，比如医疗健康模型，回归人本
251 views · 3 likes
· 0 reposts · 0 replies
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Questions or feedback? Join the Ant Ling community on Slack—we’d love to hear from you:
https://t.co/hUwiZSBpfX
Stay tuned. This is only the beginning. 🚀
888 views · 13 likes
· 0 reposts · 1 replies
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Sante is now available on @OpenRouter and @vercel_dev. Healthcare professionals, researchers, and developers can try Sante free for one month through the OpenRouter API and explore its capabilities on real-world healthcare tasks.
Try it here:
OpenRouter: https://t.co/E8QxuQDNtA
1.4K views · 21 likes
· 3 reposts · 1 replies
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Safety and ethics are fundamental to healthcare. To better meet the ethical and safety requirements of real-world healthcare, we specifically strengthened these capabilities. Sante achieves 82.06 on MedEthicAlign and 78.63 on AFUSAFE-MedSCE.
1.1K views · 11 likes
· 0 reposts · 1 replies
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Retrieval matters enormously in healthcare. Many real-world tasks depend on finding the right information and grounding decisions in reliable evidence, so strengthening Sante’s deep research and evidence-based retrieval capabilities was a key focus.
We also explored the value of
1.4K views · 15 likes
· 0 reposts · 1 replies
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Sante is particularly strong in medical knowledge and reasoning.
With only 5.1B active parameters per token, it ranks #1 among flash-size models on MedXpertQA-Text, DiagnosisArena-MCQ, and AFUMED-Drug, scoring 53.88, 83.83, and 89.59, respectively.
It also reaches 45.73 on
2.1K views · 22 likes
· 1 reposts · 2 replies
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Today, we’re introducing Ling-3.0-flash-Sante — an MoE model enhanced for health and medicine, built on Ling-3.0-flash.
Inspired by the French word “santé,” meaning “health,” Sante is built for real-world healthcare tasks spanning medical reasoning, professional healthcare https
1M views · 446 likes
· 77 reposts · 48 replies
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@AntLingAGI The benchmark spread is interesting because it spans perception, STEM, documents, agents, frontend coding, and medical reports. Consistency across that range may be more revealing than one headline score.
487 views · 5 likes
· 0 reposts · 0 replies
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Video Understanding and Multimodal Agents
Ling-3.0-flash-VL decomposes a request, searches across the video timeline, reasons over candidate clips, and finds the target segment. It can then call tools to extract keyframes, crop images, and verify the result. https://t.co/wG4tOOX
988 views · 21 likes
· 2 reposts · 1 replies
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0:40
Watch and Ask: Your Real Time Video Learning Assistant
While a course is playing, Ling-3.0-flash-VL reads the current frame and answers questions about formulas and explain formulas, diagrams, or board work. It can explain steps, answer follow up questions, and generate https://
1K views · 20 likes
· 2 reposts · 1 replies
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1:05
GUI Agent: From Task Understanding to Autonomous Execution
Ling-3.0-flash-VL identifies UI elements, plans each step, and operates the interface through clicks, typing, scrolling, and app switching. It can collect web data, organize it in Excel, and generate a chart in one https
1.2K views · 21 likes
· 2 reposts · 1 replies
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0:31
Set the Style with One Image: Vision Driven Web Design
Give Ling-3.0-flash-VL a website screenshot or an everyday photo as a visual reference. It can apply the style to a new site, including its layout, color palette, typography, and UI components. https://t.co/litv6CsSbc
1.5K views · 20 likes
· 2 reposts · 1 replies
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1:00
Coding with Visual Feedback
Give Ling-3.0-flash-VL a screenshot or design reference. It generates the site, renders it in a browser, captures the result, compares it with the reference, and updates the code. This creates a repeatable build, inspect, and revise workflow. https://
1.7K views · 24 likes
· 4 reposts · 1 replies
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0:43
Ling-3.0-flash-VL adds image and video inputs to Ling-3.0-flash. A ViT extracts visual features, a two layer MLP projector aligns them with text, and VideoRoPE encodes space and time. Its 42 layer hybrid backbone mixes KDA and Gated MLA at 5:1 and supports a 1M token context. htt
2.6K views · 37 likes
· 3 reposts · 1 replies
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Ling-3.0-flash-VL scores 42 on the Artificial Analysis Intelligence Index, up from 38 for Ling-3.0-flash, suggesting that native multimodal synergy elevates base intelligence. https://t.co/No0wP8NUiD
2.8K views · 43 likes
· 1 reposts · 1 replies
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Today, we are releasing Ling-3.0-flash-VL, built on Ling-3.0-flash with visual understanding and visual agent capabilities. It performs well across visual perception, STEM reasoning, document intelligence, multimodal agent tasks, frontend coding, and medical report https://t.co/n
437.4K views · 536 likes
· 93 reposts · 56 replies
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На фоне аккаунтов своего размера
24 постов за последние 90 дней рядом с диапазоном 10K–100K подписчиков. показывается большему числу людей, чем аккаунты того же размера.
Медианные просмотры1 048этот аккаунт924медиана для 10K–100K
Охват, %7.81%этот аккаунт3.62%медиана для 10K–100K
Вовлечённость, %1.54%этот аккаунт1.52%медиана для 10K–100K
| Показатель | Этот аккаунт | Медиана для 10K–100K | Отношение |
|---|---|---|---|
| Медианные просмотры на пост | 1 048 | 924 | 1.13× |
| Охват (просмотры ÷ подписчики) | 7.81% | 3.62% | 2.16× |
| Вовлечённость | 1.54% | 1.52% | 1.01× |
Другие в этом диапазоне → Сравнить с другим аккаунтом → Как считаются эти ориентиры →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
2.1K4 Sep
1.4K
1.1K
1.4K
888
2515 Sep
459
436
1
431.2K14 Sep
777
868
405
168
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
1.20%4 Sep
1.15%
1.13%
1.85%
1.58%
1.20%5 Sep
1.09%
0.23%
0.00%
0.07%14 Sep
2.83%
2.88%
3.46%
1.79%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes61.3%1 527 in total
Reposts9.6%239 in total
Replies6.5%162 in total
Quotes3.2%80 in total
Bookmarks19.4%483 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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