Qwen3-VL

Benchmarks, Claims, and Positioning

• Release praised for unusually extensive benchmarking; some appreciate lack of obvious cherry-picking, others argue many benchmarks are saturated or contaminated and should be retired.
• Several commenters accept that Qwen3‑VL may be SOTA among multimodal models, including versus proprietary ones, though others say it’s only marginally better than existing closed models.
• Desire for comparisons with other strong open models (e.g., GLM) and criticism of specific benchmarks like OSWorld as “deeply flawed.”
• One commenter notes little apparent architectural novelty (vision encoder + projector + autoregressive LLM), while another points to prior Qwen work like DeepStack as genuine innovation.

Multimodal Capabilities: Impressive and Fragile

• Strong real‑world reports: handles low‑quality, messy invoice images better than custom CV+OCR pipelines (OpenCV, Tesseract, GPT‑4o), and can output bounding boxes to improve OCR.
• Video demo (identifying goal timing, scorer, and method in a ~100‑minute match) impresses many.
• Others note limits: still struggles with edge cases like animals photoshopped with extra limbs, dice faces (D20), and other rare patterns; tends to “correct” images toward typical anatomy even when told they’re edited.
• General sentiment: excellent practical VLM, but far from robust general vision understanding; still highly dependent on what’s well represented in its training data.

Open Source Leadership, China, and Geopolitics

• Several see Qwen (and DeepSeek before it) as proof that open models are no longer “catching up” but actually leading in many areas.
• Strong appreciation for releasing such a large multimodal model as open weights, with some users already swapping it in for GPT‑4.1‑mini or similar in production agents at significantly lower token costs.
• Extensive debate about Chinese strategy:
  • Motives suggested include undercutting US AI incumbents, commoditizing models to sell hardware, ensuring strong Chinese‑language performance, talent competition, narrative control, and soft power.
  • Others argue Chinese labs have effectively “blank checks” via state priorities, with expectations of serving social control rather than profit.
  • Pushback against treating “the Chinese” as a single agent; some call that orientalist and say credit should go to specific teams, not a whole country.
  • Security concerns raised about sending data to Chinese‑hosted chat frontends, even if weights are open and can be run locally.

Model Zoo, Naming, and Product Confusion

• Confusion around Qwen’s lineup is a recurring complaint:
  • Qwen3‑VL‑235B‑A22B‑Instruct vs Qwen3‑VL‑Plus vs qwen‑plus‑2025‑09‑11 vs various “Omni” and “Next”/“Thinking” variants.
  • “Plus” generally understood as closed‑weight API models vs open‑weight downloadable ones, but users say it’s still unclear which API model is “better” for a given use case.
• Commenters note that opaque, marketing‑heavy model naming is widespread across AI vendors, though some think DeepSeek/Claude are clearer.

Developer Experience and Use Cases

• Users report:
  • Using the “Thinking” variants successfully for workflow automation and replacing GPT‑4.1‑mini in agentic systems with similar quality at lower cost.
  • Using Qwen multimodal for image captioning, meal/user photo tagging, and complex document understanding.
• Tools recommended for newcomers: LM Studio and AnythingLLM for easy local use; Qwen’s own chat site for quick tests (with security caveats).
• Some find smaller, older Qwen variants (e.g., QwQ / Qwen 2.5 VLM 7B) still preferable for specific tasks once fine‑tuned.

Cost, Pricing, and Efficiency

• Qwen3‑VL API pricing is reported as substantially cheaper than top proprietary models: roughly ~1/10 of one leading model and ~1/2–1/3 of another on a per‑token basis, depending on the source quoted.
• Users highlight big practical savings when swapping into existing workflows, with no obvious quality drop in their domains.
• Broader discussion about commoditization: some argue widespread high‑quality open models will pop the US AI stock bubble; others respond that value will just move up‑stack rather than disappear.

Running Large Models Locally

• Many are excited by the 235B open weights but question feasibility of self‑hosting:
  • FP16 size implies ~512GB RAM; even with quantization (e.g., q8 around ~235GB), consumer GPUs are far from sufficient without multiple very expensive cards.
  • 8× 32GB GPUs or datacenter cards (H200‑class) are considered out of reach for small players; multi‑node setups without NVLink suffer massive performance hits.
• Suggested “borderline feasible” local setups:
  • High‑RAM unified memory systems (e.g., 128GB+ GMKtec Evo 2 or 96GB+ Strix Halo / Framework Desktop) for smaller or MoE models, accepting modest tokens/s.
  • High‑bandwidth GPUs (e.g., 96GB workstation cards) or very wide‑channel DDR5 Threadripper‑class CPUs for CPU‑bound inference.
• Several warn that even expensive high‑RAM Macs or desktops will feel like “having a pen pal, not an assistant” for ≥70B dense models; MoE models fare better.
• Some argue that for most users, cloud inference remains more economical than spending ~$10k+ on fast local hardware.

Limitations, Skepticism, and Open Questions

• Skepticism about benchmark overuse, vision robustness, and lack of clear architectural breakthroughs.
• Questions remain about:
  • How Qwen3‑VL compares head‑to‑head with other new multimodal leaders (e.g., Omni models).
  • Whether smaller, more practical Qwen3‑VL variants will be released.
  • How to meaningfully evaluate vision‑language models beyond saturated leaderboards and hand‑picked demos.