Research · September 2026

AI API Pricing Index — September 2026

Name: AI API Pricing Index — September 2026 frozen snapshot
Creator: AICostBudget
License: https://creativecommons.org/licenses/by/4.0/

A reproducible September month-end snapshot of 81 public pricing records across 11 providers. Every statistic below is calculated from the frozen public projection captured at the 2026-09-30 UTC cutoff. The live Dataset may contain newer records.

Cutoff 2026-09-30 UTCLast verified Sep 30, 2026 How we verifyCurrency USD

Explore the live dataset Read findings

Public records81

Frozen public projection

Providers11

First-party and cloud APIs

Pricing components722

Conditional structured charges

Token-priced records68

At least one per-1M-token component

Cache-priced records58

Comparable cached-input scalar

September events15

Verified, not inferred

Key findings

The useful signal is in the pricing structure, not one cheapest-model ranking.

All medians use frozen public records with a compatible numeric value. Missing values are excluded, never treated as zero.

$1.32

Median input price

Across 69 records with a current input-token scalar; observed range $0.10–$30 per 1M tokens.

Median output price

Across 69 records with a current output-token scalar; observed range $0.10–$180 per 1M tokens.

5.0×

Median output/input ratio

Across 69 records with both prices. Output generation, not prompt ingestion, is commonly the higher list-price component.

90%

Median cache discount

Across 58 comparable records. The observed discount range is 75%–98%.

50%

Median Batch discount

Across 44 records with a calculation-default Batch input component; observed range 20%–50%.

Records with non-token charges

Document pages, storage, session duration, or time-based units cannot be safely flattened into a token-only price table.

Chart 1

Which providers contribute the most public records?

OpenAI19 records

Google Gemini16 records

Anthropic13 records

xAI11 records

Mistral AI9 records

DeepSeek4 records

Cohere3 records

Moonshot AI3 records

AWS1 records

Azure1 records

Google Cloud1 records

Source: frozen September 30, 2026 UTC public projection of the AICostBudget AI API Pricing Dataset. Methodology.

Chart 2

How do provider median input and output prices compare?

OpenAIInput $2Output $11n=18

Google GeminiInput $0.75Output $4.125n=14

AnthropicInput $5Output $25n=13

xAIInput $1.25Output $2.5n=9

Mistral AIInput $0.20Output $0.60n=7

DeepSeekInput $0.30Output $1.2n=3

CohereInput $1.5Output $5.75n=2

Moonshot AIInput $0.95Output $4n=3

Source: frozen September 30, 2026 UTC public projection of the AICostBudget AI API Pricing Dataset. Methodology.

Chart 3

How widely are cost-saving price modes published?

58Cached-input recordsMedian discount 90%

44Batch-priced recordsMedian discount 50%

A workload comparison that ignores cache and Batch pricing omits published savings modes for a large share of the tracked records.

Source: frozen September 30, 2026 UTC public projection of the AICostBudget AI API Pricing Dataset. Methodology.

Chart 4

Where does token-only comparison stop working?

Token-priced68 records

Tiered / long-context17 records

Non-token components13 records

Legacy status6 records

Retired status4 records

Source: frozen September 30, 2026 UTC public projection of the AICostBudget AI API Pricing Dataset. Methodology.

Chart 5 · Verified timeline

The table states the population and method so each result can be independently reproduced.

Statistic	Result	Population	Method
Frozen public records	81	All frozen projection rows	Count records
Median input price	$1.32	69 non-null input scalars	Median USD per 1M tokens
Median output price	$6	69 non-null output scalars	Median USD per 1M tokens
Median output/input ratio	5.0×	69 rows with both scalars	Median of output ÷ input per record
Median cached-input discount	90%	58 comparable rows	Median of 1 − cached input ÷ input
Median Batch input discount	50%	44 Batch-capable rows	Median of 1 − Batch input ÷ standard input
Structured pricing components	722	All public projection rows	Count pricing_components entries
September verified events	15	Events effective 2026-09-01 to 2026-09-30	Count typed events; categories remain separate

Provider comparison

Coverage and frozen medians by provider

Provider medians describe the records tracked here; they are not market share or usage-weighted prices.

Provider	Records	Priced input n	Median input	Median output	Cache rows	Batch rows
OpenAI	19	18	$2	$11	13	13
Google Gemini	16	14	$0.75	$4.125	10	8
Anthropic	13	13	$5	$25	13	9
xAI	11	9	$1.25	$2.5	9	5
Mistral AI	9	7	$0.20	$0.60	7	7
DeepSeek	4	3	$0.30	$1.2	3	0
Cohere	3	2	$1.5	$5.75	0	0
Moonshot AI	3	3	$0.95	$4	3	2
AWS	1	0	—	—	0	0
Azure	1	0	—	—	0	0
Google Cloud	1	0	—	—	0	0

Methodology

What this index measures—and what it does not.

Source and scope

All statistics are calculated from the frozen AICostBudget public projection at the 2026-09-30 UTC cutoff. The population is 81 public pricing records. The live Dataset and downloads may contain newer records; later live updates do not silently change this historical Research page.

Normalization

Comparable text-token scalars are normalized to USD per 1M tokens. Provider and model aliases do not create a second research observation. Null means unavailable or not applicable, never zero.

Cache, Batch, and tiers

Cached input and Batch discounts are calculated only when both the standard scalar and a compatible published component exist. Long-context and usage tiers remain separate structured records; thresholds price the whole matching request.

Non-token pricing

Document pages, per-minute sessions, and token-storage hours keep their native billing units. They count as non-token pricing architecture and are excluded from input/output token medians.

Time and history

September events use provider-announced or official-changelog effective dates when available. The available event history does not support an industry-wide annual price-decline claim.

Verification limits

Public list prices are planning inputs, not invoices. Regional pricing, negotiated contracts, taxes, routing channels, tokenization, and workload quality can change realized cost.

Full field definitions, official source groups, exclusions, licensing, and downloads are maintained on the AI API Pricing Dataset methodology page.

Cite this research

Copy a citation or use the underlying data.

Link to this dated analysis for the findings; cite the Dataset when reusing the machine-readable records.

Plain text

AICostBudget. "AI API Pricing Index — September 2026." AICostBudget, 2026. Frozen public projection at the September 30, 2026 UTC cutoff. https://aicostbudget.com/en/research/ai-api-pricing-index-september-2026

BibTeX

@article{aicostbudget_pricing_index_2026_09,
  author = {{AICostBudget}},
  title = {AI API Pricing Index --- September 2026},
  year = {2026},
  month = {September},
  url = {https://aicostbudget.com/en/research/ai-api-pricing-index-september-2026},
  note = {Based on the AICostBudget AI API Pricing Dataset frozen public projection; cutoff 2026-09-30 UTC}
}

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Use the evidence

Move from list price to workload cost.

Current list prices answer only the first question. Apply them to your own context, output, cache, retry, and routing assumptions before making a model decision.

Track verified model price changesSee dated events and official sources.Compare current model pricesFilter the live comparison table.Estimate your API workloadApply token volume and request assumptions.Compare switching impactModel the before-and-after workload cost.Model retry overheadMeasure failed attempts and fallbacks.