Unlimited AI Access.
One Flat Price.
Stop counting tokens. Just build.
Access unlimited Qwen3.6, DeepSeek-V4 & more for a flat CHF 39/mo.
Full privacy — no prompt logging, no training on your data, just reliable low-latency AI access.
Perfect for vibe coding sessions and 24/7 agents — no token counting (fair-use).
Start using AI Router in seconds.
Instant API key • Cancel anytime
One account, one invoice, multi-seat.
Contact Us arrow_forwardPricing tailored to team size and needs
Flat Rate Pricing
CHF 39/mo
No hidden fees. No token counting.
Context length
262K
Tokens context window — massive capacity.
Compatibility
100%
Drop-in replacement for OpenAI.
Developer Friendly.
Built for production workloads.
Integration takes minutes, not days. We maintain full compatibility with the OpenAI SDK, so you can switch your base URL and API key to start saving immediately.
OpenAI Compatible
Drop-in replacement for your existing client. Just change the base URL.
High Throughput
Dedicated capacity ensures consistent latency and tokens per second.
262K Context
262K token context window for RAG and document processing.
Drop-in replacement. Same SDK. Same calls. No migration.
Why AI Router Switzerland
AI Router Switzerland is designed for developers and AI enthusiasts who want to focus on building, testing, and running AI workflows without worrying about token limits or overages. Our "unlimited" API means you can:
- Run long coding sessions or 24/7 agents without interruptions.
- Integrate AI into local tools, IDEs, or autonomous agents seamlessly.
- Enjoy Swiss-hosted privacy — no prompt logging, no training on your data. Only light metadata analysis is performed to ensure consistent performance for everyone.
Combined with generous operational limits (3 parallel requests, 240 requests/min, 10M tokens/min), low-latency infrastructure, and OpenAI-compatible APIs, AI Router provides a reliable and worry-free environment for experimentation, development, and production-grade agent workflows.
Available Models
Powerful AI models ready for production workloads.
Best for
Agentic coding, repository-level reasoning, RAG, document analysis
Strengths
Agentic orchestration, repo-level coding, long-context workflows, production-ready stability
AIME 2026
Mathematical problem solving
GPQA Diamond
Graduate-level scientific reasoning
SWE-bench Verified
Real-world software engineering
Humanity’s Last Exam
Multi-disciplinary research evaluation
LiveCodeBench v6
Real-world coding benchmark
MMLU-Pro
General knowledge & reasoning
MMMU-Pro
Multimodal understanding & reasoning
HMMT 2026 Feb
Mathematical problem solving
The gold standard for open-weight models. Qwen3.6-27B brings a unique hybrid architecture combining Gated DeltaNet memory with traditional attention, giving it superior agentic coding and repository-level reasoning. With thinking preservation across conversation turns and support for 119 languages, it's built for developers who need stability and real-world utility.
Best for
Reasoning, coding, agentic tasks
Strengths
Fast MoE inference, top coding & reasoning benchmarks, cost-efficient deep reasoning
AIME 2026
Mathematical problem solving
GPQA Diamond
Graduate-level scientific reasoning
SWE-bench Verified
Real-world software engineering
Humanity’s Last Exam
Multi-disciplinary research evaluation
LiveCodeBench v6
Real-world coding benchmark
MMLU-Pro
General knowledge & reasoning
MMMU-Pro
Multimodal understanding & reasoning
HMMT 2026 Feb
Mathematical problem solving
Our newest addition. DeepSeek-V4-Flash is a 284B Mixture-of-Experts model that activates just 13B parameters per token, delivering frontier reasoning and coding with highly efficient inference. Its hybrid CSA + HCA attention architecture and MoE design make it exceptionally fast on agentic workloads, while deep thinking mode provides thorough reasoning for complex problems. If you need raw benchmark performance, this is the pick.
Embedding & Speech-to-Text
Best for
Agent memory indexing, RAG pipelines
Strengths
Semantic search, code retrieval, knowledge base indexing
State-of-the-art text embedding model designed for retrieval, ranking, and similarity tasks. With 2560-dimensional vectors, 32K context length, and support for 100+ languages including programming languages, it excels at text retrieval, code retrieval, classification, and clustering.
Best for
Real-time transcription, voice agents, meeting notes
Strengths
Robust to noise & accents, 99+ languages, zero-shot transcription
OpenAI Whisper large-v3-turbo running on dedicated GPU infrastructure. Low-latency speech-to-text with broad language support and high accuracy across domains.
What's Included
Frequently Asked Questions
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