Laguna-S-2.1 Evaluation: Exceptional Speed and Tool-Calling but Prone to Hallucinations
A hands-on evaluation of Poolside's new Laguna-S-2.1 model against Qwen3.5-122B on an RTX Pro 6000 GPU reveals it is the fastest 100B+ model tested, with superior tool-calling capabilities. However, the model struggles with grounding under pressure, leading to factual hallucinations. While Laguna-S-2.1 represents a significant step forward in local agentic coding capabilities and inference speed, its tendency to invent facts makes it risky for fully autonomous agents. Developers must weigh its class-leading tool mechanics against the reliability of alternatives like Qwen3.5-122B for production tasks. Laguna-S-2.1 achieved 109 tokens/second using NVFP4 quantization on vLLM and successfully executed tool chains six levels deep. However, it scored lower in grounding (0.80 vs. Qwen's 0.97), requiring at least 8k tokens for its thinking process to avoid empty outputs.
## BACKGROUND
vLLM is a popular open-source engine designed for high-throughput and memory-efficient LLM inference. NVFP4 is a 4-bit floating-point quantization format introduced with NVIDIA's Blackwell GPU architecture, allowing models to run with lower memory requirements while maintaining high accuracy. Tool calling and agentic workflows refer to an LLM's ability to interact with external APIs and execute multi-step tasks autonomously.