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DeepSeek
S
FlashQLA
A
Rows
A
Perplexity
S
TaglineChinese open-weight powerhouse. Crazy cheap, genuinely smart.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.AI search done right. Cited answers, not chat theater.
CategoryChatbotsDev PlatformDataResearch
PricingFree web + ultra-cheap API (~$0.14/M input tokens)Free (MIT License, open-source)Free + $19-$89/user/moFree + $20/mo Pro
Best forDevelopers + cost-conscious builders. Anyone fine with self-hosting.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.Replacing Google for any question where you want a cited answer in seconds.
Strengths
  • Open weights you can self-host
  • Strong reasoning + math
  • Near-free API pricing
  • DeepSeek-V3 / R1 are serious models
  • 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
  • Sources every claim
  • Fast, current answers
  • Pro Search runs multi-step research
  • Spaces for persistent context
Weaknesses
  • Data goes to servers in China — privacy concerns for business use
  • Chinese policy filters
  • English polish trails Western models
  • 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
  • Not a general chatbot
  • Answers can be shallow on complex topics
Kai's verdictS-tier for price/performance. A-tier for consumer use. If you build apps, this is the budget pick.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.S-tier for search. I use it before Google now. If you're still Googling everything, try this for a week.
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