Frontier AI · pay in EGP

Frontier AI, in EGP.

GLM-5.1 and Kimi K2.7 on one OpenAI-compatible API — for any app that needs an LLM. Billed in Egyptian pounds.

≈90%
of Claude Opus 4.8 on coding
~⅕
the price per token
EGP
no dollar card
Performance vs price

Frontier-class intelligence, a fifth of the price.

We benchmark on coding because it's the hardest public test of reasoning — but these models power any LLM workload: chat, agents, extraction, RAG.

Coding score (SWE-Bench Verified): higher is better Output per 1M tokens: shorter is cheaper
Model
Coding score
Price · output / 1M
EGP
GLM-5.1Z.ai · live
Coding score
78%
Output / 1M
EGP 320
Kimi K2.7Moonshot · live
Coding score
80%
Output / 1M
EGP 290
Claude Opus 4.8Anthropic
Coding score
89%
Output / 1M
EGP 1,250
USD only
GPT-5.5OpenAI
Coding score
89%
Output / 1M
EGP 1,500
USD only

Frontier-class coding at a fifth of the price, with no international card. Coding scores are independently-verified SWE-Bench Verified results for the last measured releases (GLM-5, Kimi K2.6); the GLM-5.1 and K2.7 served on Tarmeez are vendor-reported a notch higher, with GLM-5.1 topping SWE-Bench Pro at 58.4. Prices are output per 1M tokens at ≈50 EGP/USD.

Pricing

Per token, in pounds.

Per 1M tokens. New accounts get EGP 100 free.

ModelInputOutput
GLM-5.1 reasoning & codeEGP 100EGP 320
Kimi K2.7 Code coding & agentsEGP 70EGP 290
DeepSeek V4 Flash budget & fastEGP 10EGP 20
GLM-5.2 coding-first, 1M contextComing soon

Note: GLM-5.2 opens when its open weights ship, around June 20.

Three steps

Live in minutes.

01

Get a key

Point any OpenAI SDK at Tarmeez.

02

Top up in pounds

Balance shown in EGP, always.

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03

Call the models

Switch models with one string. Pay per token.

On the roadmap

Next: hosted in Egypt, data stays in Egypt.

Egyptian-hosted models are coming soon: an in-country tier under Egypt's data-protection law, built for banks and government that need their data to stay home.

Join waitlist
Start free

Your first EGP 100 are on us.

Run GLM-5.1 and Kimi K2.7 on your own workloads.

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