Compare AI Model Costs
Compare one workload's cost across multiple providers and models side by side
Global Workload
What this tool helps you decide
Model comparison exists to answer one question: for my workload, which models are worth shortlisting on cost — and how much am I paying for a step up in capability? By fixing a single workload and pricing it across every model at once, the table turns an abstract “which model is best?” debate into a concrete cost gap you can weigh against the benefits that matter for your use case.
It deliberately does not crown a universal winner. The right model is use-case-dependent, so the tool gives you the cost axis and the framework below helps you reason about the rest.
How to use the result
Start by entering a workload that reflects a real feature — not a round number. Sort by monthly cost to see the spread, then use the “vs. cheapest” multiplier to judge whether a more capable model’s premium is justified. A model that costs 2× the cheapest option can still be the correct choice if it removes retries, shortens prompts, or raises answer quality enough to matter. Shortlist two or three candidates here, then validate them on your own prompts before deciding.
A decision framework beyond price
Price is one of several dimensions. Weigh these together rather than optimising any single one:
- Quality & reasoning: how hard is the task? Multi-step reasoning, code, and nuanced judgement favour more capable tiers; classification and extraction often run well on low-cost models.
- Latency: user-facing chat needs fast responses; batch jobs can tolerate slower, cheaper models.
- Context-window needs: long documents or large retrieved context rule out models with smaller windows regardless of price.
- Cost at expected volume: a small per-token difference compounds at high traffic — this is exactly what the table quantifies.
- Deployment & governance: data-residency, compliance, or self-hosting requirements can narrow the field before cost is even considered.
- Vendor concentration risk: depending on a single provider for everything creates pricing and availability exposure; keeping a tested fallback reduces it.
Illustrative decision scenarios
These show how the framework changes the “best” pick for different workloads. They are illustrative reasoning patterns, not measured benchmarks or performance claims.
High-volume classification
Routing tickets or tagging content at large scale where each decision is simple and independent.
- Very high request volume
- Short inputs and short outputs
- Task needs consistency, not deep reasoning
- Latency per call is not user-blocking
What this suggests: Cost at volume dominates. A low-cost model is usually the right default; reserve a capable model only for ambiguous cases.
Customer-facing reasoning
An assistant that must handle open-ended, multi-step questions accurately in real time.
- Moderate volume, latency-sensitive
- Quality errors are visible to customers
- Prompts include context that must be understood, not just matched
What this suggests: Quality and latency outweigh a small price gap. A balanced or flagship model often pays for itself by reducing escalations and rework.
Long-document analysis
Analysing contracts or reports that exceed the context window of cheaper models.
- Large input per request
- Lower volume, tolerant of higher latency
- Context-window size is a hard requirement
What this suggests: Context window is the gating factor. Filter to models that fit the document first, then compare cost among only those.
Decision factors & limitations
- The comparison prices an identical token workload; it does not measure quality, latency, or reliability, which you must validate yourself.
- Cheaper models may need more prompt engineering or produce longer outputs, narrowing the real saving.
- Free-tier or zero-cost entries reflect standard terms that can change or carry rate limits.
- Pricing is standard pay-as-you-go and excludes committed-use, enterprise, batch, and regional discounts.
- Official sources are checked on a recurring schedule. Unusual or structural changes are held for human review.
Sources & methodology
See how prices are collected, normalised, and reviewed, and which providers are currently verified:
Related tools & guides
How this calculation works
Define a single workload (input tokens, output tokens, request frequency) and this tool calculates the total cost across multiple models simultaneously. This allows you to quickly evaluate the financial impact of switching from a flagship model to a smaller, faster model.