Could an open-source LLM handle work you currently send to a paid proprietary model? CNAPS Studio's Ask Once, Compare Three AIs card gives the same prompt to Gemma 4 31B, Qwen 3.6 and Gemma 4 E2B, then shows their responses side by side. Start with a task your team repeats and compare the answers before choosing a model.
You can try 3 free runs across all CNAPS Studio cards without an account.
Why compare open-source LLMs now?
AI spending adds up as teams move from experiments to everyday work. Menlo Ventures estimated enterprise generative AI spending at $37 billion in 2025, up from $11.5 billion in 2024. That is market-wide growth, not evidence that every provider raised its prices. For an individual team, more users and repeated requests can still mean a larger bill.
Open-source LLMs offer another route. The Gemma 4 family and Qwen 3.6 model used here have downloadable weights under Apache 2.0. You can evaluate alternatives to a proprietary API and choose how to deploy them.
For a focused task such as support drafting, summarisation or extracting fields, a smaller open model may deliver the quality you need at lower cost. Hosting, usage volume and review time determine the actual saving. The useful question is which model meets your requirements at an acceptable total cost.
One support question, three responses
The card's sample asks for a short brand reply to a customer whose order arrived with a broken seal. All three models produce fluent responses advising the customer not to use the product and to contact the company.
The differences are practical. In this example, Gemma 4 31B asks for a packaging photo and order number, then promises a free replacement. Qwen 3.6 offers a replacement or refund. Gemma 4 E2B produces a coherent draft too, but leaves a support-contact placeholder.
These are useful starting drafts, including the smaller E2B model's answer. Before using any of them, check the promises against your actual support policy and supply the missing contact details. This example compares response behaviour, not production accuracy or measured cost savings.
Use a prompt from your own workflow
Here is the sample input:
We're choosing a model for a customer support bot. A customer writes: "My order arrived with the seal broken but the product looks fine. Is it safe to use?" Reply as the brand in under 60 words.
For your own comparison, include the policy, facts and output format the model must follow. A support test might forbid offering refunds without approval. An extraction test might require specific fields and no additional text. Keep the prompt identical across models so the differences are easier to judge.
How to try it
- Open the card. Go to Ask Once, Compare Three AIs and read the sample responses. Click a response to expand it.
- Enter your task. Replace the text in Input with your own prompt. The card recommends English prompts.
- Generate new responses. Select Generate a new one, then click Run. The control indicates that generation uses a free run or credits, depending on your account state.
- Compare against your requirements. Check accuracy, instruction following, tone and how much editing each response needs. The card gives a usual time estimate of 30 seconds to 2 minutes; that is UI guidance, not a measured benchmark.
Can an open-source LLM replace our current model?
It can be a good alternative for a defined workload. Test several representative prompts, including difficult cases, and compare the results with your current model. Count corrections and retries alongside inference costs; a cheaper response that needs extensive repair may not be cheaper overall.
You do not need to build a workflow or set up model servers to make this first comparison in CNAPS Studio. Start with one recurring task and see whether an open model already does enough of it well.
