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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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Gemini
A
FlashQLA
A
Claude Code
S
Adobe Firefly
A
TaglineGoogle's answer. Best integrated with Workspace + free for a lot.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.Anthropic's CLI agent. Opus-powered, operates on your repo directly.Commercially safe image gen, deeply integrated with Photoshop.
CategoryChatbotsDev PlatformCodingImage
PricingFree + $20/mo Advanced (bundled with 2TB Drive)Free (MIT License, open-source)Part of Claude Pro/Max/Team plansFree + included with Creative Cloud
Best forAnyone already on Google, research tasks, summarizing long documents.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.Developers who want an agent, not autocomplete. Large refactors, tests, docs.Anyone in Creative Cloud. Brands that need copyright clarity.
Strengths
  • Native Google Workspace integration
  • Very long context (1M+)
  • Deep Research feature
  • Free tier is generous
  • 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
  • Runs locally, edits your actual files
  • Strong on large codebases with 1M context
  • Great at multi-step tasks
  • Trained on licensed content — commercially safe
  • Generative Fill in Photoshop is incredible
  • Native to Adobe ecosystem
Weaknesses
  • Writing quality trails Claude
  • Over-refusals on edge content
  • UI is cluttered
  • 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
  • Terminal-based — learning curve
  • Can't be used without Claude subscription
  • Aesthetic ceiling below Midjourney
  • Tied to Adobe subscription
Kai's verdictA-tier. The Deep Research feature is genuinely useful. Don't sleep on it if you're already paying Google.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.)S-tier if you live in the terminal. Different shape than Cursor — complementary, not replacement.S-tier inside Photoshop (Generative Fill). B-tier standalone.
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