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Sora
A
Claude Code
S
FlashQLA
A
Rows
A
TaglineOpenAI's video model. Long clips, cinematic quality.Anthropic's CLI agent. Opus-powered, operates on your repo directly.Qwen's open-source GPU kernel library that squeezes 2–3× more speed out of linear attention on NVIDIA Hopper hardware — if you're lucky enough to own one.Spreadsheets with AI + live integrations baked in.
CategoryVideoCodingDev PlatformData
PricingIncluded with ChatGPT Plus/ProPart of Claude Pro/Max/Team plansFree (MIT License, open-source)Free + $19-$89/user/mo
Best forChatGPT subscribers experimenting with cinematic shots.Developers who want an agent, not autocomplete. Large refactors, tests, docs.ML engineers and researchers running Qwen3.x linear-attention models on H100/H200 clusters who need to close the gap between theoretical GDN efficiency and actual hardware throughput.Ops teams, marketers, anyone building dashboards from multiple sources.
Strengths
  • Up to 20-sec clips at 1080p
  • Strong physics + scene composition
  • Storyboard feature for longer narratives
  • Remix existing videos
  • Runs locally, edits your actual files
  • Strong on large codebases with 1M context
  • Great at multi-step tasks
  • 2–3× forward-pass and ~2× backward-pass speedup over FLA Triton kernels on Hopper GPUs
  • Gate-driven automatic intra-card context parallelism boosts SM utilization in long-sequence, small-head-count regimes without manual config
  • Hardware-friendly algebraic reformulation reduces Tensor Core, CUDA Core, and SFU overhead with no numerical precision loss
  • MIT licensed and fully open-source — drop it straight into Qwen3.x training and inference pipelines
  • Pull live data from Stripe, Slack, Google Analytics, etc.
  • AI functions inside cells
  • Modern UX
Weaknesses
  • Stricter content policy than competitors
  • Hit-or-miss on complex motion
  • Text-in-video still struggles
  • Terminal-based — learning curve
  • Can't be used without Claude subscription
  • Extremely narrow hardware requirement: SM90+ only (H100/H200, DGX Spark) with CUDA 12.8+ and PyTorch 2.8+ — useless outside Hopper-class clusters
  • GDN/Qwen-specific: not a drop-in replacement for FlashAttention-style softmax kernels, and won't help you if you're not running linear-attention Qwen models
  • Very new, minimal community adoption or third-party validation yet
  • Not a full Excel replacement for heavy users
  • Integrations best on paid tiers
Kai's verdictA-tier. Amazing when it works, frustrating when it doesn't. Runway still more reliable for pros.S-tier if you live in the terminal. Different shape than Cursor — complementary, not replacement.A genuinely impressive, laser-focused kernel optimization from the Qwen team — real speedups on real hardware — but its utility is gated behind Hopper GPUs and Qwen's GDN architecture, making it a niche power tool rather than a broadly useful library. (Verdict pending Phi's full review.)A-tier. The most interesting spreadsheet in years. Great for ops dashboards.
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