We’re looking at moving from our current setup where we have individual subscriptions to Claude, GPT-4, and a couple of specialized models. The main appeal of consolidating is obvious—one subscription, cleaner billing. But I’m struggling to understand how to actually compare which model performs best for our workflows without completely rebuilding everything for each test.
Let me be specific about the problem: we have three main workflows—content generation, data analysis, and document classification. Each one probably has a different optimal model. Right now, to test which one is cheapest and fastest for each task, we’d have to either run them all simultaneously (expensive) or rebuild each workflow three times.
I’ve read that some platforms let you swap models within the same workflow for testing, which would be a game changer if it’s true. But I’m curious about the practical side: when you test like this, how do you actually measure cost per task? Are you looking at total tokens, execution time, accuracy metrics, or some combination?
Has anyone actually done side-by-side testing across models in a single workflow and figured out the cost story?
We did exactly this for a document analysis workflow. The setup was the key—we built a single workflow with a conditional that let us route to different models based on a parameter.
Instead of three separate workflows, one workflow with branches. Run it three times, swap the model parameter each time. Same input data, clean comparison.
For measuring cost: we tracked three things—API tokens actually consumed (not estimated), execution time for the task, and manual spot checks on output quality. The cost per task came from dividing total tokens used by number of executions.
What we found surprised us. GPT-4 was fastest for our use case but not cheapest per output. Claude was actually more reliable for edge cases but slower. A smaller specialized model was serviceable for basic documents but failed on anything complex. One model wasn’t the answer—we ended up using different models for different document types depending on complexity.
The consolidation advantage kicked in when we didn’t need three separate API accounts and billing relationships. Everything ran through one execution-based subscription model, which made tracking cost per model super straightforward.
Testing model performance across the same workflow taught us that token count is misleading as a cost measure. Two models might use similar tokens but cost differently per token, and processing time matters if you’re doing this at scale.
We logged execution metrics for each model: tokens consumed, execution duration, and whether the output was acceptable (we validated this manually for a sample).
Using a single workflow with model-switching parameters meant we could A/B test everything consistently. Same input data, same workflow structure, only the model changed. Results were much cleaner statistically than rebuilding three times.
Cost comparison became straightforward once we looked at cost per successful output, not just raw token usage. One model might be cheaper per token but need more iterations to get quality output. That changes the economics entirely.
Single workflow with conditional routing for models is the right approach. You’ll see how different models handle the same data without variance from workflow changes or input timing.
Measuring cost effectively requires tracking: tokens consumed (input and output separately), execution duration, and most importantly, whether the output met your quality threshold. A cheaper model that produces unusable results isn’t cheaper—it’s more expensive because you need retry logic or human intervention.
Key insight we discovered: specialized models often beat general models on specific tasks at lower cost. For data classification, we found a smaller model trained specifically for financial documents actually outperformed the larger general models and cost less. But that only emerged when we tested them in the same workflow with consistent data.
One workflow, swap model parameter, log tokens and time. Cheaper not always better—quality matters. We found different models won for different document types.
This is actually one of the cleanest use cases for consolidating into a platform with multiple models available.
Built a workflow for our email classification task using this exact pattern. Set up the workflow once with conditional branches, then tested it with Claude, GPT-4, and a smaller model. Same input data, same validation, just swapping which model ran each branch.
The beauty of having 400+ models available in one subscription is that you’re not paying for separate API relationships. All the cost data flows through one platform, which makes comparison incredibly clean. You can see execution logs showing which model was called, tokens used, duration—all in one place.
For our use case, we realized the smaller model was handling 80% of cases perfectly at a fraction of the cost, and we only needed the larger model for the edge cases. That split would have been a nightmare to manage across separate subscriptions.
The execution-based pricing model makes this even simpler because you’re not locked into per-model costs. Test as much as you need, track which model gives you the best output per dollar, then lock in your workflow configuration.