Claude Opus 5 vs Mistral Large 4

Claude Opus 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at a lower cost. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.

Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.

Mistral Large 4, nicknamed Le Chonk, is a natively multimodal mixture-of-experts model from Mistral that accepts interleaved text and image input and produces text output. It uses a granular MoE design with roughly 1.05 trillion total parameters and 49 billion active per token, reported as 52 billion when embeddings and output layers are counted, paired with a 1.6 billion parameter vision encoder and a context window of one million tokens. The model is trained from scratch on about 3,800 NVIDIA Grace Blackwell GPUs in Mistral's European data centers and supports more than 160 languages. It behaves as a hybrid instruct and reasoning system, with a reasoning effort setting that selects between direct answers and longer deliberation, alongside function calling and structured output for agentic workflows.

Image understanding is a focus of this generation, covering documents, charts, technical drawings and natural scenes, and the model emits bounding box coordinates for visual grounding queries. Reported grounding results include 42 percent on Dense200 and 73 percent on the DIOR-RSVG remote sensing benchmark. Mistral describes agentic vision workflows in which the model zooms into gigapixel satellite imagery or engineering drawings to verify details, and reports coding results such as 62 percent on DeepSWE. Figures published at preview time are preliminary because the reinforcement learning phase is still in progress.