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Side-by-side: what they do, what they cost, what Kai actually thinks. Pass up to 4 tools via ?tools=claude,chatgpt,gemini.
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Bolt.new (StackBlitz)
A
FlashQLA
A
GitHub Copilot
B
Claude
S
TaglinePrompt to deployed full-stack app in the browser.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.Microsoft/GitHub's autocomplete. Deep VS Code + JetBrains integration.Anthropic's flagship — best reasoning + longest useful context.
CategoryCodingDev PlatformCodingChatbots
PricingFree + $20-$200/moFree (MIT License, open-source)Free (limited) + $10/mo Pro + $19/mo BusinessFree + $20/mo Pro + team/enterprise
Best forPMs, founders, non-devs shipping MVPs.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.Teams with GitHub already. Devs who don't want to change IDEs.Long writing, code, careful thinking, documents over 50 pages.
Strengths
  • Full-stack generation + live preview
  • Deploy to Netlify in one click
  • Works in-browser — no install
  • 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
  • Great enterprise story
  • Works in your existing IDE
  • Chat + autocomplete
  • Best-in-class writing + nuanced reasoning
  • 1M context on Opus
  • Artifacts for code/docs
  • Lowest hallucination rate in my testing
Weaknesses
  • Quality ceiling for complex apps
  • Can get into loops for non-trivial bugs
  • 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
  • Less agentic than Cursor/Claude Code
  • Model quality varies
  • Image generation is weak
  • No native web search on all tiers
Kai's verdictA-tier. Best for fast prototypes. Competitive with Lovable — try both.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.)B-tier. Solid for autocomplete but the category moved past it. Pick Cursor unless you can't.S-tier for reasoning and writing. If you only pay for one chatbot, pay for this one — especially for long work.
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