Cost per 10K Tokens: Code Review with Llama 4 Maverick vs. Mistral Small 3

Comparing Open and Optimized Models: Llama 4 Maverick vs. Mistral Small 3 for 10,000 Tokens

This analysis benchmarks the cost and potential performance of two distinct models, Llama 4 Maverick (400B) and Mistral Small 3, for processing approximately 10,000 tokens. This token volume is ideal for evaluating the initial costs associated with analyzing smaller code changes, individual functions, or brief documentation snippets, relevant for academic researchers and developers prototyping AI code review assistants.

Llama 4 Maverick (400B): Open-Weight Power

Meta AI’s Llama 4 Maverick (400B) represents a significant advancement in open-weight models, offering a vast 1,000,000 token context window. Its pricing is set at $0.15 per 1 million input tokens, making the cost for 10,000 tokens approximately $0.0015. This model supports text, vision, and tools, positioning it as a versatile option for integrated development environments where visual context or automated actions might be beneficial.

• Model Name: Llama 4 Maverick (400B)
• Provider: Meta AI
• Context Window: 1,000,000 tokens
• Pricing (per 1M tokens): $0.15 (input) / $0.60 (output)
• Estimated Cost for 10K Tokens: ~$0.0015
• Key Capabilities: text, vision, tools

Mistral Small 3: European Efficiency

Mistral AI’s Mistral Small 3 is designed for efficiency and affordability within the European AI landscape. It offers a 128,000 token context window and is priced at a very competitive $0.1 per 1 million input tokens. For 10,000 tokens, the estimated cost is just $0.001. Like Llama 4 Maverick, it includes capabilities for text, vision, and tools, making it a strong contender for similar applications.

• Model Name: Mistral Small 3
• Provider: Mistral AI
• Context Window: 128,000 tokens
• Pricing (per 1M tokens): $0.10 (input) / $0.30 (output)
• Estimated Cost for 10K Tokens: ~$0.001
• Key Capabilities: text, vision, tools

Code Review Benchmarking

Both Llama 4 Maverick and Mistral Small 3 offer excellent value for developers and researchers working with small-scale AI deployments. Mistral Small 3 is marginally cheaper per 10K tokens, making it a prime candidate for extremely budget-constrained projects. Llama 4 Maverick, while slightly more expensive per token, provides a significantly larger context window and the benefits associated with an open-weight model, which can be appealing for customization and long-term research.

For code review assistants focusing on small diffs, both models can effectively process the input and utilize their tool-calling capabilities. Researchers might prefer Llama 4 Maverick for its larger context and open nature, while those prioritizing absolute lowest cost might lean towards Mistral Small 3. The inclusion of ‘vision’ capabilities in both could be explored for future enhancements, such as analyzing screenshots of UI code or diagrams.

Best Use Cases: Cost-effective text and multimodal analysis, especially for open-weight enthusiasts or those seeking highly optimized, low-cost solutions for prototyping and small-scale AI applications.