Cheap depends on how often you work
An occasional developer may spend less with a free workspace and a capped provider key than with a monthly subscription. A daily power user may get better value from a paid plan's included allowance. An offline hobbyist may prefer local inference after buying capable hardware. There is no honest single cheapest assistant across those situations. Start by estimating tasks per month and the model capability needed for them. A weak model that fails repeatedly can be more expensive in time and retries than a stronger one.
Free workspace, paid model
Cline's individual extension is free, and Licuse's BYOK workspace is free. Both still need an inference route for remote AI work. Licuse currently supports OpenRouter, OpenAI and Anthropic keys; Cline supports a broader catalog. Set a low provider budget, run representative tasks and inspect the invoice. For light use, this structure can be economical because there is little fixed commitment. For heavy use, variable cost can rise quickly. A free download is not the same as a zero-cost month of agent activity.
Local inference is not zero cost
A local model route can avoid remote token billing and keep inference on your hardware when configured correctly. Cline supports local runtimes such as Ollama. The computer still has a purchase price and operating cost, and setup takes time. Model quality and speed vary with hardware and task. If a local model cannot solve a multi-file bug reliably, the saved API spend may be offset by manual work. Benchmark the exact model and repository before declaring the workflow cheapest.
Included plans can win at volume
A paid editor or agent plan can be cheaper when its allowance covers a large amount of routine work. The bill is easier to predict, though limits and possible overage remain. Compare one month of actual use against the fixed fee. If a plan's allowance is routinely unused, a BYOK route may be cheaper. If provider invoices regularly exceed the plan and its workflow is as good, the plan may be a better choice. Recheck current tier details because the break-even point changes when models or allowances change.
Choose models by task
Use a model strong enough for the decision at hand. A small model may be fine for an explanation or mechanical edit, while a complex architecture change may benefit from a stronger one. Avoid sending huge unrelated context with every request. Stop retry loops when the agent's approach is wrong and inspect the failing test. These habits can save more than switching products. Measure cost per accepted task, because a low token rate is only useful when the model can complete the work.
Privacy and setup are part of value
The lowest invoice may involve a data route you cannot use for a sensitive project or a setup your team cannot maintain. Provider retention, editor routing, local hardware and account controls should be considered alongside price. A tool that fits your existing editor may save migration time. Another may make code review clearer. Do not suppress these tradeoffs just to produce a neat cheapest ranking. Write down the constraints first, then compare only options that satisfy them.
A low-risk buying sequence
Begin with a small task set and a firm budget cap. Test a code question, a focused patch and a multi-file change. Record model cost, result quality and review time. If the provider route is affordable, keep it until real usage warrants a subscription. If a local model is appealing, trial it on the same tasks before investing in hardware. Revisit the decision after a month. The cheapest assistant for you is the one that produces verified work within your actual budget and constraints.
Start with a small capped experiment and scale only after you know the cost per successful task.
Sources & further reading
References support the concepts and product descriptions discussed above. Features, prices and account eligibility can change; check the live documentation. Inclusion here is not a hands-on test or vendor endorsement.