{"slug":"sglang","name":"SGLang","domain":"sglang.ai","verdict":"As of 2026-07-17, ChatGPT, Claude, Gemini, Grok collectively rank SGLang #2 of 7 for llm inference server for high-throughput serving (one of 7 leaderboards it appears on). Source: https://modelsagree.com/product/sglang (modelsagree.com, CC BY 4.0).","best_rank":2,"categories":7,"entries":[{"slug":"best-llm-inference-server-for-high-throughput-serving","title":"Best LLM inference server for high-throughput serving","rank":2,"of":7,"score":17,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":2,"Gemini":2,"Grok":2},"reason":"Excellent throughput and latency from RadixAttention, prefix caching, speculative decoding, disaggregated prefill/decode, and strong distributed/MoE serving; narrowly leads for demanding modern workloads with repeated prefixes or structured generation.","reasons":[{"model":"ChatGPT","reason":"Excellent throughput and latency from RadixAttention, prefix caching, speculative decoding, disaggregated prefill/decode, and strong distributed/MoE serving; narrowly leads for demanding modern workloads with repeated prefixes or structured generation."},{"model":"Claude","reason":"Frequently tops independent throughput/latency benchmarks, especially for structured output, multi-turn agentic workloads, and heavy prefix reuse thanks to RADIX-tree KV caching (RadixAttention) and an efficient zero-overhead scheduler; strong large-scale disaggregated prefill/decode and expert-parallel deployments (it powered publicized DeepSeek-scale serving); near-tie with vLLM — it wins on raw performance for cache-heavy workloads, loses on breadth of model/hardware coverage and ecosystem maturity."},{"model":"Gemini","reason":"The performance leader for agentic workflows, long-context RAG, and structured generation. Its RadixAttention architecture manages the KV cache as a radix tree, enabling automatic and dynamic prefix caching across separate user requests, which yields massive throughput increases under high prefix overlap. It also has a fast runtime specifically optimized for structured JSON/regex constraint parsing. (Near-tie with vLLM, ranked second only because of slightly less mature multi-hardware support)."},{"model":"Grok","reason":"Excels in structured/JSON output, agentic/multi-turn workloads with RadixAttention prefix caching (often edges vLLM on shared-prefix traces, e.g., 6.8k vs 6.2k tok/s), strong MoE support, competitive throughput, and native structured gen advantages that matter for many production apps. FIX: Slightly narrower general model coverage and higher ops complexity than vLLM for simple cases."}],"fixes":[{"model":"ChatGPT","fix":"Aggressive development and tuning complexity make upgrades and production operations less predictable than vLLM."},{"model":"Claude","fix":"Smaller ecosystem and narrower model/hardware support than vLLM; more sharp edges when serving unusual architectures or non-NVIDIA accelerators."},{"model":"Gemini","fix":"Steeper learning curve and higher configuration complexity, making it overkill and harder to optimize for simple workloads with unique, non-overlapping prompts."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-llm-inference-server-for-high-throughput-serving.json"},{"slug":"best-open-source-llm-serving-stack","title":"Best open-source LLM serving stack","rank":2,"of":7,"score":16,"appearances":4,"modelRanks":{"ChatGPT":2,"Claude":2,"Gemini":2,"Grok":2},"reason":"Near-tied with vLLM and often faster for prefix-heavy, structured, agentic, multimodal, and large-MoE workloads through RadixAttention, aggressive kernels, speculative decoding, and disaggregated serving.","reasons":[{"model":"ChatGPT","reason":"Near-tied with vLLM and often faster for prefix-heavy, structured, agentic, multimodal, and large-MoE workloads through RadixAttention, aggressive kernels, speculative decoding, and disaggregated serving."},{"model":"Claude","reason":"Performance leader in many real workloads — RadixAttention prefix caching makes it exceptional for multi-turn chat, agentic loops, and shared-prefix batch inference, with best-in-class structured/constrained output; validated at extreme scale (it's the reference stack for large DeepSeek deployments and used by major labs). Near-tie with vLLM: pick SGLang when your traffic has heavy prefix reuse or JSON-constrained decoding, vLLM for ecosystem breadth."},{"model":"Gemini","reason":"Near-tie with vLLM for top spot, particularly outperforming it in structured outputs (JSON/regex constraints) and agentic multi-turn loops due to native RadixAttention prefix caching."},{"model":"Grok","reason":"superior low-latency structured output, radix attention for shared prefixes, strong for agentic and multimodal workloads with high concurrency"}],"fixes":[{"model":"ChatGPT","fix":"Its fast-moving, optimization-first stack is less predictable across arbitrary models and configurations than vLLM."},{"model":"Claude","fix":"Smaller ecosystem and community than vLLM — fewer third-party integrations, less battle-tested docs, and narrower hardware support, so more engineering self-sufficiency is required."},{"model":"Gemini","fix":"Higher configuration complexity and smaller community support compared to the default vLLM ecosystem."},{"model":"Grok","fix":"broader ecosystem integrations and easier multi-GPU setup for non-experts"}],"updated":"2026-07-13","rank_history":{"days":["2026-07-11","2026-07-12","2026-07-13"],"ranks":[2,2,2]},"reasoning_shift":[{"model":"ChatGPT","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Multimodal and large-MoE workloads","q":"multimodal, and large-MoE workloads"},{"t":"Aggressive kernels","q":"aggressive kernels"},{"t":"Less predictable across arbitrary models","q":"less predictable across arbitrary models and configurations than vLLM"}],"dropped":[{"t":"Low-overhead scheduler","q":"a low-overhead scheduler"},{"t":"Reasoning workloads","q":"especially for reasoning and structured-generation workloads"},{"t":"Breadth of integrations and tooling","q":"Match vLLM’s breadth of integrations, documentation, and operational tooling"}]},{"model":"Gemini","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Near-tie with vLLM","q":"Near-tie with vLLM for top spot"},{"t":"Higher configuration complexity","q":"Higher configuration complexity"},{"t":"Smaller community support","q":"smaller community support compared to the default vLLM ecosystem"}],"dropped":[{"t":"AMD and TPU parity","q":"Standardize out-of-the-box support and performance parity for AMD GPUs and TPUs."}]},{"model":"Claude","from":"2026-07-12","to":"2026-07-13","added":[{"t":"Shared-prefix batch inference","q":"shared-prefix batch inference"},{"t":"Large DeepSeek deployments","q":"it's the reference stack for large DeepSeek deployments"},{"t":"More engineering self-sufficiency required","q":"more engineering self-sufficiency is required"}],"dropped":[{"t":"Zero-overhead scheduler","q":"a zero-overhead scheduler"},{"t":"xAI and RL pipelines","q":"battle-proven at frontier scale (xAI, large RL pipelines)"}]}],"api":"https://modelsagree.com/api/v1/best/best-open-source-llm-serving-stack.json"},{"slug":"best-llm-inference-server-for-self-hosting","title":"Best LLM inference server for self-hosting","rank":2,"of":7,"score":15,"appearances":4,"modelRanks":{"ChatGPT":2,"Claude":2,"Gemini":2,"Grok":3},"reason":"Often the performance leader for cache-heavy agents, long shared prompts, structured generation, and large MoE deployments; RadixAttention, aggressive prefix reuse, disaggregated serving, speculative decoding, and strong multi-GPU scaling make it a near-tie with vLLM","reasons":[{"model":"ChatGPT","reason":"Often the performance leader for cache-heavy agents, long shared prompts, structured generation, and large MoE deployments; RadixAttention, aggressive prefix reuse, disaggregated serving, speculative decoding, and strong multi-GPU scaling make it a near-tie with vLLM"},{"model":"Claude","reason":"Matches or beats vLLM on high-concurrency and structured/agentic workloads thanks to RadixAttention prefix reuse and a fast constrained-decoding path; battle-tested at very large deployments and the throughput/latency choice when concurrency and structured output dominate; near-tie with vLLM."},{"model":"Gemini","reason":"A high-performance inference engine optimized for structured generation and agentic workflows. Its RadixAttention enables automatic prefix caching, drastically reducing time-to-first-token in multi-turn chat, RAG, and programmatic LLM tasks. Near-tied with vLLM, it ranks second only because its non-NVIDIA hardware support is less mature."},{"model":"Grok","reason":"Exceptional for structured/constrained generation and agentic workflows with RadixAttention, strong benchmarks in complex output scenarios while maintaining good throughput"}],"fixes":[{"model":"ChatGPT","fix":"Its fast-moving optimization stack requires more version pinning, benchmarking, and workload-specific tuning than vLLM"},{"model":"Claude","fix":"Smaller ecosystem, thinner docs, and a steeper operational ramp than vLLM — less turnkey for a first production deployment."},{"model":"Gemini","fix":"Unsuitable for deployments requiring broad non-GPU hardware compatibility (like TPU or CPU-only clusters) where vLLM dominates."},{"model":"Grok","fix":"Mature ecosystem and broader hardware compatibility beyond its current focus"}],"updated":"2026-07-13","rank_history":{"days":["2026-06-29","2026-06-30","2026-07-08","2026-07-09","2026-07-10","2026-07-12","2026-07-13"],"ranks":[2,3,2,2,2,2,2]},"reasoning_shift":[{"model":"Gemini","from":"2026-07-12","to":"2026-07-13","added":[{"t":"non-NVIDIA hardware support is less mature","q":"its non-NVIDIA hardware support is less mature"},{"t":"broad non-GPU hardware compatibility","q":"Unsuitable for deployments requiring broad non-GPU hardware compatibility (like TPU or CPU-only clusters) where vLLM dominates."}],"dropped":[{"t":"framework stability","q":"Improve framework stability"},{"t":"developer documentation","q":"developer documentation"},{"t":"ecosystem integrations","q":"ecosystem integrations"}]},{"model":"Claude","from":"2026-07-12","to":"2026-07-13","added":[{"t":"fast constrained-decoding path","q":"a fast constrained-decoding path"},{"t":"steeper operational ramp","q":"a steeper operational ramp than vLLM"},{"t":"less turnkey","q":"less turnkey for a first production deployment"}],"dropped":[{"t":"narrower hardware support","q":"narrower hardware support"}]},{"model":"ChatGPT","from":"2026-07-12","to":"2026-07-13","added":[{"t":"cache-heavy agents and long shared prompts","q":"cache-heavy agents, long shared prompts"},{"t":"large MoE deployments","q":"large MoE deployments"},{"t":"requires workload-specific tuning","q":"requires more version pinning, benchmarking, and workload-specific tuning than vLLM"}],"dropped":[{"t":"efficient scheduler","q":"an efficient scheduler"},{"t":"strong multimodal serving","q":"strong multimodal and distributed serving"},{"t":"Broaden model compatibility and operational tooling","q":"Broaden model compatibility and operational tooling"}]},{"model":"Grok","from":"2026-07-08","to":"2026-07-12","added":[{"t":"broader hardware compatibility","q":"broader hardware compatibility beyond its current focus"}],"dropped":[{"t":"grammar support","q":"grammar support"},{"t":"continuous batching and OpenAI API","q":"continuous batching and OpenAI API"},{"t":"production tooling and docs","q":"Mature production tooling, docs"}]}],"api":"https://modelsagree.com/api/v1/best/best-llm-inference-server-for-self-hosting.json"},{"slug":"best-open-source-llm-inference-servers-for-kubernetes","title":"Best open-source LLM inference servers for Kubernetes","rank":2,"of":8,"score":13,"appearances":4,"modelRanks":{"ChatGPT":2,"Claude":5,"Gemini":2,"Grok":2},"reason":"Near-tied with vLLM and sometimes faster for prefix-heavy, structured-generation, multimodal, or reasoning workloads thanks to RadixAttention, aggressive caching, speculative decoding, and strong distributed serving","reasons":[{"model":"ChatGPT","reason":"Near-tied with vLLM and sometimes faster for prefix-heavy, structured-generation, multimodal, or reasoning workloads thanks to RadixAttention, aggressive caching, speculative decoding, and strong distributed serving"},{"model":"Gemini","reason":"Industry-leading performance for complex agentic workflows, multi-turn conversations, and structured generation, leveraging RadixAttention for fast automatic prefix caching and scheduler-integrated constrained decoding. Near-tie with vLLM on runtime efficiency."},{"model":"Grok","reason":"RadixAttention delivers clear throughput and TTFT wins on prefix-heavy workloads (multi-turn, RAG, agents), excellent structured/JSON output and MoE handling, competitive overall performance with OpenAI API and solid Kubernetes deployability"},{"model":"Claude","reason":"Rapidly matured into a top-tier engine — RadixAttention KV-cache reuse gives standout performance for multi-turn, shared-prefix, and structured/agentic workloads, often matching or beating vLLM there; OpenAI-compatible and increasingly deployed in serious production."}],"fixes":[{"model":"ChatGPT","fix":"Its rapidly changing runtime and smaller operational ecosystem create more upgrade risk and debugging work than vLLM"},{"model":"Claude","fix":"Younger ecosystem and thinner native K8s tooling than vLLM/Triton; you build more of the deployment scaffolding yourself, and its edge narrows on simple single-shot generation."},{"model":"Gemini","fix":"Rapid release velocity and evolving architecture lead to occasional breaking API changes and higher operational maintenance overhead compared to more conservative inference runtimes."},{"model":"Grok","fix":"Narrower model coverage and slightly less mature general-purpose ecosystem/docs than vLLM for non-prefix or rapidly changing model sets"}],"updated":"2026-08-10","rank_history":{"days":["2026-08-03","2026-08-10"],"ranks":[2,2]},"api":"https://modelsagree.com/api/v1/best/best-open-source-llm-inference-servers-for-kubernetes.json"},{"slug":"best-batch-inference-api-for-large-scale-llm-processing","title":"Best batch inference API for large-scale LLM processing","rank":6,"of":10,"score":4,"appearances":1,"modelRanks":{"Grok":2},"reason":"Edges vLLM in structured gen, prefix caching (RadixAttention), and certain high-concurrency/MoE workloads; strong real throughput (often highest in tuned batch traces) with native structured output advantages valuable for batch processing pipelines; pairs well with Ray for large-scale.","reasons":[{"model":"Grok","reason":"Edges vLLM in structured gen, prefix caching (RadixAttention), and certain high-concurrency/MoE workloads; strong real throughput (often highest in tuned batch traces) with native structured output advantages valuable for batch processing pipelines; pairs well with Ray for large-scale."}],"fixes":[{"model":"Grok","fix":"Slightly narrower general model coverage and newer/less battle-tested than vLLM for every scenario."}],"updated":"2026-07-17","api":"https://modelsagree.com/api/v1/best/best-batch-inference-api-for-large-scale-llm-processing.json"},{"slug":"best-llm-structured-output-tool","title":"Best structured output tool for LLMs","rank":7,"of":10,"score":2,"appearances":1,"modelRanks":{"Gemini":4},"reason":"(SGLang is in a near-tie with Outlines.) It is a high-performance serving framework that integrates constrained decoding directly into the inference engine (via backends like XGrammar). Combined with RadixAttention for KV cache optimization, it offers unmatched generation throughput and speed for structured agentic workloads.","reasons":[{"model":"Gemini","reason":"(SGLang is in a near-tie with Outlines.) It is a high-performance serving framework that integrates constrained decoding directly into the inference engine (via backends like XGrammar). Combined with RadixAttention for KV cache optimization, it offers unmatched generation throughput and speed for structured agentic workloads."}],"fixes":[{"model":"Gemini","fix":"It is a full serving runtime rather than a lightweight client-side library, requiring the developer to deploy and maintain specialized backend infrastructure."}],"updated":"2026-07-13","rank_history":{"days":["2026-07-12","2026-07-13"],"ranks":[null,6]},"api":"https://modelsagree.com/api/v1/best/best-llm-structured-output-tool.json"},{"slug":"best-batch-inference-apis-for-synthetic-data-generation","title":"Best batch inference APIs for synthetic data generation","rank":9,"of":9,"score":2,"appearances":1,"modelRanks":{"Gemini":4},"reason":"Superior choice for multi-step, agentic, or heavily constrained synthetic data generation (JSON schemas and grammars) due to RadixAttention prefix caching, which dramatically accelerates batch jobs sharing prompt prefixes. Assumes synthetic workflows rely heavily on long system prompts or multi-turn agent execution traces.","reasons":[{"model":"Gemini","reason":"Superior choice for multi-step, agentic, or heavily constrained synthetic data generation (JSON schemas and grammars) due to RadixAttention prefix caching, which dramatically accelerates batch jobs sharing prompt prefixes. Assumes synthetic workflows rely heavily on long system prompts or multi-turn agent execution traces."}],"fixes":[{"model":"Gemini","fix":"Steeper optimization curve and smaller ecosystem integration footprint compared to vLLM for straightforward, non-structured single-turn batch generation."}],"updated":"2026-08-10","api":"https://modelsagree.com/api/v1/best/best-batch-inference-apis-for-synthetic-data-generation.json"}],"page":"https://modelsagree.com/product/sglang","check":"https://modelsagree.com/check?q=SGLang","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}