GPT-4.1 mini vs Mistral Large 2 (Nov '24)

Intelligence Index

MMLU-Pro

GPQA

AIME

Which is cheaper, GPT-4.1 mini or Mistral Large 2 (Nov '24)?

GPT-4.1 mini is cheaper on list price. GPT-4.1 mini costs $0.4/M input and $1.6/M output tokens. Mistral Large 2 (Nov '24) costs $2.0/M input and $6.0/M output tokens. On combined list price, GPT-4.1 mini is 4.0x cheaper than Mistral Large 2 (Nov '24). However, list prices alone can be misleading because different models use different numbers of tokens for the same task. Check the effective cost comparison above, which adjusts for token efficiency using benchmark data.

Which scores higher on benchmarks, GPT-4.1 mini or Mistral Large 2 (Nov '24)?

GPT-4.1 mini has a higher Intelligence Index (22.4) compared to Mistral Large 2 (Nov '24) (15.1). The Intelligence Index is a composite score from three industry-standard benchmarks: MMLU-Pro (general knowledge and reasoning), GPQA (graduate-level science), and AIME (mathematical problem solving). A higher score means the model produces more accurate and capable responses across a broad range of tasks. This composite approach is more reliable than any single benchmark because it measures different types of capability.

Which model is better value for money, GPT-4.1 mini or Mistral Large 2 (Nov '24)?

Mistral Large 2 (Nov '24) offers better value at $0.16 per intelligence point compared to GPT-4.1 mini at $3.04 per intelligence point. Cost per intelligence point measures how much you pay for each unit of benchmark performance, calculated as the combined token cost divided by the Intelligence Index score. When token efficiency data is available, this calculation uses effective prices (adjusted for the fact that different models consume different numbers of tokens for the same task) rather than raw list prices. A lower cost per intelligence point means you get more capability per dollar.

Which has a larger context window, GPT-4.1 mini or Mistral Large 2 (Nov '24)?

GPT-4.1 mini supports 200,000 tokens compared to Mistral Large 2 (Nov '24) with 128,000 tokens. The context window determines how much text (including your prompt, conversation history, and documents) the model can process in a single request. A larger context window is important for tasks like document summarization, long-form analysis, and multi-turn conversations with extensive history. If your use case involves processing large inputs, the context window may be a deciding factor.