# vLLM vs SGLang: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: SGLang wins on multi-turn agent loops and structured JSON decoding with up to 1.4x higher throughput via RadixAttention KV-cache prefix reuse (94.5), while vLLM wins on broadest model-architecture day-0 support and enterprise Ray/Kubernetes ecosystem maturity (94.0).

- **Category**: Production Multi-GPU LLM Inference Engine Shootout
- **Canonical URL**: https://exactbaseline.com/vs/vllm-vs-sglang
- **1-Click $49 Builder Repo Hardening & Launch Kit (Stripe Checkout)**: https://exactbaseline.com/buy/instant_boost
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## vLLM Key Advantages
- PagedAttention continuous batching and FP8 quantization across 100+ model architectures
- Day-0 support for new open-weight releases on NVIDIA, AMD ROCm, and TPU
- Battle-tested OpenAI-compatible server with tensor and pipeline parallelism

## SGLang Key Advantages
- RadixAttention automatically caches shared system-prompt and multi-turn agent KV prefixes
- Up to 4.1x faster constrained JSON schema decoding via compressed finite-state machines
- Lower time-to-first-token (TTFT) on agentic tool-calling workloads