Mistral Small 4: A Good Alternative to Qwen3.5 122B and Nemotron 3 Super?

2026-03-12 · Source: The Kaitchup – AI on a Budget · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, short

Summary

Mistral AI has released Mistral Small 4, a new Mixture-of-Experts (MoE) model with 119B total parameters and 6.5B active parameters. This model unifies instruct and reasoning capabilities, offering a `reasoning_effort` switch with "none" (instruct mode) and "high" settings, similar to Qwen3.5's `enable_thinking`. While it underperforms Qwen3.5 122B on benchmarks like LiveCodeBench, its instruct mode generates fewer tokens, making it suitable for simple prompts. Architecturally, Mistral Small 4 is denser than Qwen3.5 122B, using 128 experts and a low-rank MLA for reduced KV cache size, requiring 5.49 GiB in BF16 or 2.75 GiB in FP8 for 256k tokens. Additionally, NVIDIA introduced Nemotron 3 Nano 4B and Nemotron Cascade 2 30B-A3B, with Cascade 2 outperforming Qwen3.5 35B in accuracy while being faster and having a smaller KV cache.

Key takeaway

For AI Architects evaluating new large language models, Mistral Small 4 offers a unified instruct/reasoning model with efficient KV cache, but its benchmark performance is lower than Qwen3.5. You should consider NVIDIA's Nemotron Cascade 2 for superior accuracy and speed, especially if memory efficiency and NVFP4 support are critical for your deployment on constrained hardware.

Key insights

Mistral Small 4 unifies instruct and reasoning, while NVIDIA's new Nemotron models offer high performance with efficient KV cache.

Principles

• Unified models simplify deployment.
• MLA reduces KV cache memory.
• Sparsity impacts memory and performance.

Method

Mistral Small 4 integrates reasoning via a `reasoning_effort` switch, while its architecture uses 128 experts and a low-rank MLA to optimize KV cache memory consumption for long contexts.

In practice

• Use Mistral Small 4 for lightweight chat.
• Consider Nemotron Cascade 2 for high accuracy.
• Evaluate NVFP4 checkpoints for efficiency.

Topics

• Mistral Small 4
• Mixture-of-Experts
• KV Cache Optimization
• Model Quantization
• Nemotron Models

Best for: AI Architect, MLOps Engineer, NLP Engineer, Machine Learning Engineer, AI Engineer, Data Scientist

Related on AIssential

• The Fastest and Cheapest 120B LLM? — Nemotron 3 Super offers superior efficiency and cost-effectiveness in NVFP4, despite Qwen3.5's slightly higher base accuracy.
• Introducing Mistral Small 4 — Mistral Small 4 unifies multiple specialized AI capabilities into a single, versatile model.
• New Ministral 3 14B vs Mistral Small 3.2 24B Review — Larger parameter models often perform better on complex document parsing, even with higher quantization.
• NVIDIA Releases Nemotron-Cascade 2: An Open 30B MoE with 3B Active Parameters, Delivering Better Reasoning and Strong Agentic Capabilities — NVIDIA's Nemotron-Cascade 2 MoE model excels in reasoning and agentic tasks via Cascade RL and MOPD.
• The KV-Cache of Small MoEs: Qwen3, Qwen3.5, GLM 4.7 Flash, and Nemotron 3 Nano Compared — MoE models achieve inference efficiency through diverse attention architectures and KV cache optimization strategies.
• NVIDIA’s New AI Just Changed Everything — NVIDIA's Nemotron 3 Super offers a powerful, open-source AI assistant with significant speed improvements via novel architectural and mathematical techniques.

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Kaitchup – AI on a Budget.