Best Local AI Models for Function Calling & Structured Output
Reliably emitting JSON, tool calls, structured data on demand.
For function calling and structured output, Mistral 7B Instruct v0.3 is the clear winner, offering top-tier quality and efficient resource usage. If you need a bit more capacity, Llama 3.1 8B Instruct is a close second.
Function calling and structured output require AI models that can reliably generate precise and well-formatted JSON or other structured data. Users should prioritize models with high accuracy and low latency, especially when running locally to avoid the overhead of API calls and ensure data privacy. Local models offer better control over performance and security, making them ideal for applications where speed and confidentiality are paramount.
Top picks
- #1
Mistral 7B Instruct v0.37.3B · apache-2.0 · min 4.6GB
Top choice for precision and efficiency in function calling and structured output.
Mistral 7B Instruct v0.3 stands out as the best model for function calling and structured output due to its exceptional quality (100%) and manageable size (7.3B parameters). With a minimum VRAM requirement of 4.6GB, it strikes a balance between performance and resource consumption. Licensed under Apache-2.0, it is open-source and suitable for a wide range of applications. Its strength lies in its ability to generate highly accurate and structured outputs, making it ideal for tasks that demand precision and reliability. While it may not be the smallest model, its efficiency and accuracy make it a top pick.
- #2
Llama 3.1 8B Instruct8B · llama3.1 · min 5.1GB
A close second with top-tier quality and slightly higher VRAM requirements.
Llama 3.1 8B Instruct is a strong contender with a perfect quality score (100%) and a moderate size (8B parameters). It requires a minimum of 5.1GB VRAM, which is slightly more than Mistral 7B but still within reach for many users. Licensed under the Llama 3.1 license, it offers robust performance and reliability. This model excels in generating structured data and handling complex function calls, making it a solid choice for users who need a bit more capacity without sacrificing accuracy.
- #3
Qwen 2.5 14B14B · apache-2.0 · min 8.9GB
High-quality and powerful, but with higher VRAM demands.
Qwen 2.5 14B Instruct is a powerhouse with a quality score of 98% and a large parameter count (14B). It requires a significant 8.9GB VRAM, making it suitable for users with more advanced hardware. Licensed under Apache-2.0, it is open-source and versatile. This model is particularly strong in generating detailed and structured outputs, making it ideal for applications that require deep understanding and precise formatting. However, its high VRAM requirement may limit its accessibility for some users.
- #4
Gemma 3 12B12B · gemma · min 7.3GB
Highly capable but resource-intensive.
Gemma 3 12B is another high-quality model with a 98% quality score and a substantial parameter count (12B). It demands 7.3GB VRAM, which is considerable but still feasible for users with mid-to-high-end hardware. Licensed under the Gemma license, it is well-suited for applications that require detailed and structured outputs. This model excels in generating complex and accurate data, but its resource requirements may be a limiting factor for some users.
- #5
Qwen 2.5 7B Instruct7.6B · apache-2.0 · min 5.3GB
Balanced performance with moderate VRAM needs.
Qwen 2.5 7B Instruct offers a balanced combination of quality (98%) and size (7.6B parameters). It requires 5.3GB VRAM, making it accessible to a broader range of users. Licensed under Apache-2.0, it is open-source and flexible. This model is effective in generating structured outputs and handling function calls, making it a reliable choice for users who need a balance between performance and resource usage. While it may not match the top-tier models in terms of absolute quality, it provides a solid middle ground.
Hardware guidance
For function calling and structured output, users should aim for at least 8GB of VRAM to handle most models comfortably. Mid-range systems with 12GB VRAM can run larger models like Qwen 2.5 14B and Gemma 3 12B, while high-end systems with 16GB or more VRAM can handle even the most resource-intensive models with ease. Systems with 8GB VRAM are ideal for models like Mistral 7B Instruct v0.3 and Llama 3.1 8B Instruct, providing a good balance between performance and cost.
When to skip local
While local models offer many advantages, there are scenarios where a hosted API might be preferable. For example, if you have limited hardware resources or need to scale quickly, a hosted API can provide consistent performance without the need for local infrastructure. Consider cloud-based solutions like Anthropic's Claude or OpenAI's GPT-3.5 for these use cases.
Need a guide for a different use case? See all 50 buyer's guides →