Mistral Large with Function Calling - Review and Code

Mistral Large positions itself as a strong alternative to top closed models by pairing solid reasoning performance with native function calling—while also being cheaper to build and offering an on-prem option for sensitive deployments. The model’s appeal isn’t framed as hype; it’s presented as a practical choice for teams that want reliable instruction-following, tool use, and the ability to keep data in-house.

A central thread is how Mistral Large avoided “dumbing down” through heavy RLHF. The transcript credits that approach to better instruction adherence and reasoning quality, with particular emphasis on math/decision tasks such as GSM 8K. Benchmarks are discussed, but with skepticism: MMLU is treated as an imperfect yardstick, and the comparison is criticized for omitting some relevant models. Even so, the discussion suggests Mistral Large lands near the top tier when broader reported results are considered.

The model’s deployment story is another major differentiator. Mistral Large is described as proprietary—served via the Mistral AI API and also available through Azure—yet Mistral also provides a path to run it on-prem. That matters for organizations that can’t send sensitive data to external services, including large enterprises and hedge funds. The transcript also notes that Mistral’s founder, Arthur Mensch, has claimed the model cost about 20 million euros to make, implying a faster, lower-cost development cycle than some competitors. The practical consequence: more frequent iteration, more opportunities to learn from what works and what doesn’t, and quicker fine-tuning or new model launches.

On capabilities, Mistral Large is described as multimodal in a limited sense—focused on Western European languages rather than Cyrillic or Asian languages—plus a 32k context window. Instruction following is highlighted as a strength, including the ability for developers to set moderation policies through how the model is instructed. The transcript also flags a key implementation question for teams: whether that instruction-following approach makes the model easier or harder to jailbreak, especially compared with more heavily aligned systems.

Hands-on testing via LangChain is used to show response behavior and speed. The model is portrayed as paying attention to system prompts in ways that affect output structure—sometimes producing step-by-step reasoning differently than other providers. It’s also described as capable of generating emails, character-like responses, and code, with GSM 8K performance standing out as particularly strong.

The most concrete section is function calling. Using JSON schemas for tools, the workflow lets the model decide when to call a tool (“auto” selection) and return structured arguments. A restaurant-style example demonstrates multi-turn tool use: the model asks follow-up questions when required parameters are missing (e.g., booking day/time), then issues a tool call once it has enough information. Tool responses are appended back into the conversation so the model can produce a final user-facing confirmation (e.g., “Friday 8pm… no need to reconfirm”). The transcript concludes that Mistral Large is worth testing for teams already using OpenAI-style function calling, since the integration pattern can be adapted with relatively small code changes.