AI Model Pricing Comparison Chart

A quick-reference chart of how AI API pricing breaks down in 2026 — save this if you want the shape of current pricing without running a calculator every time. For exact, model-specific numbers, use the AI Model Cost Calculator; this page is the fast mental-model version.

How pricing tiers break down (August 2026)

Ranges reflect publicly published pricing across major providers as of August 2026 and will drift over time as providers update rates — check the Cost Calculator for current, model-specific numbers.

Long-context pricing cliffs

Several providers charge a higher rate once a request crosses a specific input-token threshold, and the higher rate typically applies to the entire request, not just the tokens past the line. Google’s Gemini 3.1 Pro doubles its rate at 200K input tokens; OpenAI’s GPT-5.5 and GPT-5.6 models apply a 2x input / 1.5x output multiplier past 272K tokens. Anthropic’s current lineup doesn’t use stepped context pricing — the quoted rate holds regardless of context length. If your workload regularly sits near one of these thresholds, trimming unnecessary context before sending the request is often cheaper than accepting the higher tier.

A worked example

Take a workload sending 30 million input tokens and 6 million output tokens a month on a mid-tier model priced at $2 input / $10 output per million tokens: (30 × $2) + (6 × $10) = $60 + $60 = $120/month at standard rates. If half the input is a repeated system prompt eligible for caching at roughly 90% off, the cached portion drops to about $0.20/M, pulling input cost down to roughly $33 and the total to about $93/month — a genuine reduction from one optimization, before even considering batch processing or a cheaper model tier.

Two discounts most teams miss

Prompt caching

Commonly discounts repeated or static context by up to ~90% on cached input tokens. This applies to any workload with a stable system prompt, a reused reference document, or repeated conversation scaffolding — chatbots, RAG pipelines with a fixed knowledge base, or any tool that resends the same instructions on every call.

Batch processing

Commonly around ~50% off both input and output tokens for workloads that don’t need a real-time response — classification, offline summarization, bulk content generation, or overnight data processing. If your workload can tolerate a delay of a few hours, this is one of the easiest discounts to claim.

Subscription vs. API: a quick decision guide

Cost-cutting checklist

• Match model tier to task complexity — don’t default to flagship for routine work
• Enable prompt caching for any workload with repeated context
• Use batch processing for anything that doesn’t need a real-time response
• Re-check pricing before renewing — rates shift more often than most people expect
• Measure cost per completed task, not just cost per token

Frequently asked questions

How often should I recheck these numbers?

AI pricing changes frequently — checking monthly, or before any major renewal or scaling decision, is a reasonable cadence.

Do these ranges include prompt caching or batch discounts?

No, the table above reflects standard rates. Apply the caching and batch discounts on top if your workload qualifies — they can meaningfully change which tier is most cost-effective for you.

Why do these ranges use tiers instead of naming specific models?

Named-model pricing goes stale within weeks as providers ship new versions — a chart pinned to “GPT-5.5” or “Claude Sonnet 5” would need constant rewrites just to stay accurate. Tier ranges built from cross-checked market data age more gracefully, since a new model launch typically slots into an existing tier rather than inventing a new price point entirely.

Which tier should I default to if I’m not sure?

Mid-tier is the reasonable default for most general-purpose work — it’s priced closely enough to Budget that the difference is rarely worth the quality tradeoff, and reserving Frontier for tasks that actually need it keeps blended costs down. Start there and move up only when a specific task demonstrably needs more capability, rather than defaulting to Frontier out of caution.

Do these ranges apply to every provider, or just the largest ones?

They’re built from the major providers with meaningful market share — OpenAI, Anthropic, and Google primarily. Smaller or newer providers sometimes price well outside these ranges in either direction, so treat this chart as a reference for the mainstream market rather than an exhaustive survey of every API on the market.

Why cost per token isn’t the whole picture

A model with a lower per-token price isn’t automatically the cheaper choice in practice. Two other factors change the real-world math:

Output verbosity

Some models default to longer, more verbose answers than others for the same prompt. Since output tokens are usually priced 3-5x higher than input tokens, a model that’s 20% cheaper per token but answers 40% more verbosely can end up costing more per completed task, not less.

Retry rate

A budget model that gets a task right on the first try is cheaper than a flagship model that also gets it right on the first try. But if the budget model needs a follow-up prompt or two to reach an acceptable answer, those extra round trips add both cost and latency that the sticker-price comparison doesn’t capture. This is why cost-conscious teams increasingly measure cost per completed task rather than cost per token — it accounts for both of these effects at once.

How to use this chart in a real budgeting conversation

The tier table above is most useful as a starting filter, not a final answer. A practical way to use it:

1. Sort your workload by complexity first. Routine tasks (classification, extraction, short summaries) can usually start in the Budget or Mid-tier row. Complex, multi-step, or high-stakes tasks should start in the Frontier row.
2. Estimate your token volume for each category using the Token Counter, since the dollar impact of a tier choice scales directly with volume.
3. Apply caching and batch discounts where your workload qualifies — these often move a task from one effective cost tier to a cheaper one without changing the model at all.
4. Re-verify with the live Cost Calculator before finalizing a budget, since the ranges here are directional and provider pricing shifts over time.

A note on how these ranges were built

The ranges in the table reflect publicly published API pricing across the major providers, cross-checked against multiple independent pricing trackers rather than a single source, to avoid publishing a stale or provider-specific outlier as if it were representative of the whole market. We intentionally show ranges rather than single point figures, because within any given tier, individual model prices can vary by 2-3x depending on context window size, release recency, and provider positioning.