Compare AI tools

	Groq S	GitHub Copilot B	FlashQLA A	OpenRouter S
Tagline	The fastest AI inference in the world. Crazy low latency.	Microsoft/GitHub's autocomplete. Deep VS Code + JetBrains integration.	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.	One API, every model. Pay-as-you-go, no subscriptions.
Category	Dev Platform	Coding	Dev Platform	Dev Platform
Pricing	Free tier + pay-as-you-go API	Free (limited) + $10/mo Pro + $19/mo Business	Free (MIT License, open-source)	Pay per token — model-dependent
Best for	Developers who need sub-100ms LLM responses.	Teams with GitHub already. Devs who don't want to change IDEs.	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 experimenting across models. Apps that want fallback logic.
Strengths	500+ tokens/sec on Llama/Mixtral — feels instant Custom LPU hardware Great free tier	Great enterprise story Works in your existing IDE Chat + autocomplete	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	300+ models from one endpoint Automatic fallbacks between providers No subscription — just pay what you use
Weaknesses	Open-weight models only (no Claude/GPT) Less flexibility on custom configs	Less agentic than Cursor/Claude Code Model quality varies	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	Slight markup over direct API Some provider features not exposed
Kai's verdict	S-tier for speed. When latency is the product, start here.	B-tier. Solid for autocomplete but the category moved past it. Pick Cursor unless you can't.	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 for model-shopping. I use this for every prototype before committing.
Link	Open →	Open →	Open →	Open →