Nicces

Where your models run, and whose law applies

Every way to run AI inference, with prices and jurisdictions.
Compiled by Niclas Broman · Back to the stack tracker · Nicces on Substack
Prices verified 13 September 2026
Which model does the job and whose law applies to your data are two separate questions. They used to come as one package, because if you wanted Claude you called Anthropic. Open weights broke that link, and most of the writing about AI costs still treats the two as one thing. This page keeps them apart. All prices are list rates per million tokens, input first, output second. Batch, cache and long context tiers are excluded and listed separately at the bottom.
United States Europe China Chinese model, European infrastructure Yours

Called directly from the provider

You call their service. Their country's law applies to the processing.
ModelProviderIn / Out
Claude Fable 5.1Anthropic, US$10 / $50
Claude Opus 5Anthropic, US$5 / $25
GPT-5.6 SolOpenAI, US$4 / $20
Kimi K3Moonshot, China$3 / $15
Gemini 3.1 ProGoogle, US$2 / $12
Claude Sonnet 5Anthropic, US$2 / $10
Mistral Medium 3.5Mistral, France$1.50 / $7.50
Grok 4.5xAI, US$2 / $6
Claude Haiku 4.5Anthropic, US$1 / $5
GLM 5.2Zhipu, China$1.40 / $4.40
Gemini 3.7 FlashGoogle, US$0.75 / $3.75
Mistral Large 3Mistral, France$0.50 / $1.50
GPT-5.6 LunaOpenAI, US$0.20 / $1.20
DeepSeek V4-ProDeepSeek, China$0.44 / $0.87
Mistral Small 4Mistral, France$0.15 / $0.60
Grok 4 FastxAI, US$0.20 / $0.50
Gemini Flash-LiteGoogle, US$0.10 / $0.40
DeepSeek V4-FlashDeepSeek, China$0.14 / $0.28
Amazon Nova MicroAmazon, US$0.035 / $0.14
Qwen3.7 FlashAlibaba, China$0.03 / $0.13
Ministral 3, 3BMistral, France$0.10 / $0.10

Gemini 3.1 Pro and both Grok models charge double past 200K tokens of context.

Same models, EU region of a US provider

Azure Frankfurt, Bedrock Ireland and similar.
OptionJurisdictionPrice
US frontier via EU regionStill US. The CLOUD Act reaches data held anywhere by a US companySame as direct
AWS European Sovereign CloudGerman operating entity, stronger controls, some residual exposurePremium

Data residency and legal jurisdiction are not the same thing. Where the servers sit does not change which government can compel the company holding the keys.

Open weights run by a European provider

You pick the model. They run it. European law applies.
ProviderWhat they runNote
Mistral, serving GLM 5.2Zhipu's model on Mistral's infrastructure, France$1.40 / $4.40
Verda, HelsinkiLLM Gateway, sells to Finnish finance and healthcareOn request
ScalewayFrance, runs Mistral workloadsOn request
OVHcloudFrance, largest European-owned cloudOn request
NebiusAmsterdam, data centres in FinlandOn request
IONOSGermanyOn request
HetznerGermanyOn request
EuroHPC AI FactoriesEU public compute, 19 sites selectedFree

EuroHPC is worth knowing about and almost nobody uses it. AI startups and SMEs can get GPU hours on European AI Factories at no charge through the access calls. Playground access is always open with a small allocation and approval inside two working days. Fast Lane goes up to 50,000 GPU hours with approval inside four working days. One caveat your accountant will raise: free compute has measurable value and its state aid treatment is unsettled.

This is the row most cost comparisons leave out. A Chinese-built model running on European machines puts your data under European law at close to Chinese prices.

Running it yourself

Usually more expensive than people expect, unless the volume is very high or the data cannot leave.
OptionWhat decides itReality check
Rented GPU, open weightsUtilisation. A card costs the same idle as busy50-60% load needed
Own hardware, on premiseJurisdiction. Nothing leaves the building5-12M tokens/day to break even vs frontier
Workstation or MacZero marginal cost after purchasePays back in 3-8 months at €200/mo spend
On device, small modelsGood enough for sorting, tagging, extractiongpt-oss-20b fits in 16GB

One worked example: 50 million tokens a day costs about $2,250 a month on a cheap service and about $5,175 on four A10G cards. Against budget services, self-hosting almost never wins on cost. Against the most expensive models it can, at high and steady volume. Open models run roughly a generation behind the best closed ones, which for most ordinary work does not matter.

The levers that move the numbers

These change the bill more than the choice of model does.
Batch processingMinus 50% for work that does not need an answer immediately
Cached inputUp to minus 90% when the same system prompt repeats, which it usually does
EU-only processingPlus 10% at Mistral. This is the published price of European jurisdiction
Enterprise supportPlus 75% at Mistral, comparable elsewhere
QuantisationCuts memory needed by two to four times, usually costs one to three benchmark points

Licences, before you build on anything

Open weights are not all open in the same way, and several have tightened.
ModelTerms
gpt-oss 20b and 120bApache 2.0, properly permissive in both sizes
Mistral open modelsApache 2.0 for research and individual use. Production needs a commercial licence
GLMMoved off clean MIT terms, conditions now apply to large hosts