Ant Ling launched Ling-3.2-flash on Nov 5, 2026 — 560B/25B active MoE with 1M context, native image+text+video, OpenAI/Anthropic-compatible APIs, free for two weeks. Vendor benches claim Opus 5 (max) wins; treat as lab figures.
Ant Ling’s platform changelog ↗ dated November 5, 2026 lists the native multimodal model Ling-3.2-flash:
560B total / 25B active MoE parameters
1M context window
Native image + text + video input (no external vision module called out in the models docs)
Four focus areas: complex visual understanding, multimodal long-horizon Agent, 3D understanding & interaction, and medical multimodal
Chat experience plus OpenAI- and Anthropic-compatible API protocols
Free for the first 2 weeks after launch
Secondary coverage from TechNode (Nov 6) ↗ and Neural Brew (Nov 6) ↗ matches those headline specs. Model selection detail lives on the Ling models docs ↗; multimodal I/O guidance is in the multimodal understanding tutorial ↗.
Documented video I/O constraints (per Ant Ling multimodal docs at research time): MP4 / MOV / WMV, clips ≤30s, 1 video per request, frames sampled at 2 fps capped at 32 frames. Images are JPEG/PNG with base64 size limits in the same docs — check the live tutorial before you wire a production pipeline.
Pricing after the promo: the free window is explicit in the changelog. A durable “original price” for this SKU was not listed in the Nov 5–6 reviews we checked — do not invent a $/M rate. DesignArena separately cited $0.30 / $0.90 per 1M tokens for Preference-vs-Price framing (see quote below); treat that as DesignArena’s arena price cite, which may differ from Ant Ling’s post-promo list price.
Open weights: there is no open-weights announcement for 3.2. Prior context only: Ling-3.1-flash-1019 was MIT on Hugging Face — that does not transfer to 3.2.
Vendor evals: Ant Ling’s launch post claims wins vs “Opus 5 (max)” on OmniDocBench 1.5 / ZEROBench(Sub) / Vision2Web. Those are lab figures, not independently verified third-party boards. Wait for external evals before you rewrite a model shortlist.
Medical multimodal: treat this as an analysis aid, not a clinical device or diagnostic product. AdSense-safe and product-safe framing only.
Official launch:
“Introducing Ling-3.2-flash, Ant Ling's native multimodal model: 560B total / 25B active parameters.
Stronger complex visual understanding, long-horizon multimodal agents, 3D understanding & interaction, and medical intelligence.
Outscores Opus 5 (max) in release evaluations:”— @AntLingAGI ↗ (~959 likes / ~369k impressions at research time)
DesignArena leaderboard + price framing (attribute to DesignArena, not our lab):
“BREAKING: Ling-3.2-flash by @AntLingAGI lands at #10 on Image to Frontend Arena with an Elo of 1238! This model also establishes a new Pareto frontier for Preference vs Price at $0.30 input / $0.90 output per 1M tokens, the cheapest for any model at this performance.”
— @DesignArena ↗ (~113 likes)
Skeptical take on vendor benches:
“@AntLingAGI just shipped Ling-3.2-flash (560B/25B active) and claims it beats Opus 5 on OmniDocBench + Vision2Web. Trust the vendor benches… or wait for third-party evals?”
Hands-on free-chat note (anecdotal, not a bench):
“Just tested Ling 3.2 Flash for free through their chat platform.
Built a third person shooting game with it. Took a few extra prompts to improve the result, but overall, pretty decent for a 560B parameter model.
Is Ling 3.2 Flash better than Tencent HY4? 🤔”
Multimodal agent builders with OpenAI/Anthropic-compatible clients: drop-in protocol support plus native image/video is the practical unlock — especially during the two-week free window. Use the free period to run your own harness, not to rubber-stamp vendor OmniDocBench slides.
Teams comparing Chinese MoE flash models: put Ling-3.2-flash next to your existing Qwen / DeepSeek / Kimi evals on your docs, UIs, and agent loops. DesignArena’s #10 Image-to-Frontend Elo 1238 is a useful public signal for frontend generation — still not a substitute for your regression suite.
Caveats that matter: vendor benches vs Opus 5 (max); medical = analysis aid only; no open weights for 3.2 yet; post-promo list price unknown as of Nov 6 reviews. Video has hard clip/fps caps — plan chunking.
Adjacent stories on this desk: Kimi Hosted Agents, Kimi K3.1 Pro open weights, and Gemini Omni 1.2 Flash.
For the wider open-model map see best open-source LLMs 2026. For assistant comparisons see ChatGPT vs Claude vs Gemini. For agent stacks see best AI agent frameworks 2026.