How is cost per call estimated?

Each model has input and output token prices. The tool assumes a representative call size for the task type, prices it, and keeps only models whose estimated per-call cost is at or under your budget.

What does the capability score mean?

It is a relative score combining each model's strength on the chosen task with your speed-versus-quality preference. It ranks models that already passed your budget and context filters; it is a guide, not an absolute benchmark.

Why does context window act as a hard filter?

A model that cannot hold your input is unusable regardless of price or quality, so any model below your minimum window is removed before scoring rather than merely penalized.

What if nothing fits my budget?

The tool tells you the cheapest capable option and how far over budget it is, so you can decide whether to raise the budget, shrink the task, or accept a less capable tier.

Should I always pick the top recommendation?

The top pick is the most capable within your constraints, but the runner-up is often dramatically cheaper for a small capability drop. Check both — for high-volume workloads the cheaper tier usually wins on total cost.

What is the Model Tier Recommender by Budget?

Enter a maximum cost per call, a task type, and a minimum context window to get the most capable LLM that fits your budget, ranked by capability within the cost constraint with a clear runner-up. It runs free in your browser on Gera Tools, with nothing uploaded.

Model Tier Recommender by Budget

Name: Model Tier Recommender by Budget
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

Model tier recommender by budget

There is no single “best” LLM — only the best one for your task, your context needs, and your budget. This recommender takes a hard cost-per-call ceiling, a minimum context window, a task type, and a speed-versus-quality preference, then returns the most capable model that satisfies all of those constraints, with a ranked list so you can see the trade-offs.

How it works

Each model in the tool carries its context window and input/output token prices. You set a budget and task; the tool estimates a representative call cost for that task, then filters out any model that is over budget or below your minimum window — those are hard constraints, not penalties. The survivors are scored on task-specific capability blended with your speed-or-quality preference, and ranked. If nothing fits, it surfaces the cheapest capable option and how far over budget it sits so you can make an informed call.

Tips and notes

Budget is per call, not per month. Multiply by your expected volume to see the real bill.
Check the runner-up. It is frequently far cheaper for a marginal quality drop — ideal at scale.
Pick the task honestly. “Reasoning” and “simple extraction” lead to very different recommendations.
Window is non-negotiable. A cheaper model that cannot hold your input is no bargain — the tool excludes it for that reason.

How task type affects the recommendation

Choosing the right task type is the most important input because model capability is not uniform across task categories. A model that excels at coding may not lead on open-ended summarisation, and a model with strong general reasoning can underperform a specialised smaller model on simple classification.

Reasoning / complex analysis — Multi-step problems, mathematical reasoning, long-chain inference. Frontier models (larger parameter counts, trained with extended thinking) typically outperform smaller tiers significantly here. Budget constraint is most likely to be in tension with quality for this task type.

Coding — Code generation, debugging, refactoring. Several mid-tier models perform very close to frontier models on common programming tasks because there is abundant training data. The quality gap between tiers is smaller here than in open-domain reasoning, making mid-tier models competitive even at tight budgets.

Summarisation — Condensing long documents. Context window is the critical constraint. Models with small context windows cannot process long source documents at all, so the window filter removes them before scoring. Within the surviving candidates, quality differences between tiers are often small enough that the cheapest option is usually fine.

Simple extraction — Pulling structured fields from text, entity recognition, yes/no classification. Smaller and cheaper models typically perform well on clearly defined extraction tasks. Frontier model capability is largely wasted here, and the cheapest model that fits the window and budget is usually the right recommendation.

Interpreting speed vs. quality preference

The speed preference shifts scoring toward models with high output throughput (tokens per second) even if they score slightly lower on task quality. This is relevant for real-time, user-facing features where latency is noticed. The quality preference weights the capability score more heavily, accepting slower models if they score notably higher on the task. For batch or background jobs, pure quality often wins; for interactive chat, balanced or speed-weighted settings are more appropriate.