Your workload is the benchmark
Choose open models with production evidence, not leaderboard confidence alone. Leaderboards help you decide what to test. They do not tell you which model clears your quality bar, fits your latency budget, handles your tools, scales with your traffic, and protects your margin.
Token Factory helps teams evaluate open models against the workload they actually run, then serve the right model through the right production path.
We set up the benchmark with you — your models, your eval set, your latency and cost targets — and hand back a scorecard.
What you need to know to choose a model
Model selection grounded in production constraints. Here are the factors that matter when choosing a model, and how we benchmark them.
Why workload beats leaderboard
A model can win a benchmark and lose your product.
The right open model depends on the shape of the workload: prompt length, output format, tool behavior, quality threshold, latency budget, traffic pattern, and retry tolerance.
A workload benchmark helps answer the questions that matter before production volume moves:
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Which model is good enough for this task?
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What latency does the product actually feel?
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What is the cost per successful outcome?
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What usually fails, and how often?
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Which endpoint path makes the workload production-ready?
How the benchmark works
Bring a representative workload, eval set, or production task.
Define the quality bar, latency target, and cost metric that matter.
Compare candidate open models against those constraints.
Choose the right model and endpoint path for production testing.
What you get
Before you migrate volume, test the workload.
What you bring
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Current model or provider
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Candidate open models
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Target workload or eval set
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Quality bar
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Latency budget
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Traffic shape
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Context length and tool requirements
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Production timeline
What you receive
A practical recommendation for which model to test, serve, or move toward a dedicated endpoint path.
Open models, served through production paths
Token Factory gives teams a familiar API for serving open models, plus production paths for workloads that need predictable capacity, support planning, or dedicated deployment patterns.
Browse the full open-model lineup, with context windows, regions, and per-token pricing, before you book anything.
Questions and answers
No. A clean eval set helps, but the benchmark can start from a representative workload and a clear definition of success.
Do not choose the model in the abstract
Bring one workload. We will help benchmark the open-model path that can actually work in production.