The Cheapest Way to Use Qwen
Updated: Aug 3
Across industries, job functions, and academia, more teams are building their own AI agent assistants and putting them to work. But the longer you run them, the harder it is to ignore one unavoidable reality: cost. An API invoice larger than your monthly subscription fee, quietly accumulating call by call, has become a familiar sight. AI agents don't call a model once per task. They call it tens or even hundreds of times per job -- planning, invoking tools, verifying results, then looping back again. The smarter the agent, the more API calls it makes. Higher cost is a natural consequence.
So the question becomes: is the cost of daily API usage actually sustainable? If your unit economics go negative the more you use AI, integrating it into production becomes impractical no matter how capable the model is. In the agentic AI era, what teams actually need is not a better model -- it's cheaper inference. Air API was built to solve exactly this problem.
Open-Source Models: The Practical Choice for the Agent Era
Closed-source APIs like GPT and Claude deliver strong performance, but costs compound quickly under high-volume agentic workloads. Stories of developers racking up hundreds of dollars in a single day of agent testing are common in developer communities. Open-source models offer a fundamentally different cost structure:
Infrastructure providers set token pricing directly, making rates significantly lower than closed-source alternatives.
Apache 2.0 licensing removes commercial use restrictions, eliminating legal risk in production deployments.
Publicly available model weights mean no vendor lock-in.
The challenge with self-hosted open-source models is everything else: GPU procurement, environment configuration, and scaling infrastructure all add their own costs and time. Air API is a serverless API service that delivers the cost advantages of open-source models while removing the infrastructure barrier entirely.
Qwen: Built for Agentic Workloads
Air API launched with the Qwen model family as its first offering because Qwen's architecture aligns directly with the demands of agentic workloads.
Alibaba's Qwen series has become one of the fastest-growing open-source model families, and the reasons are concrete:
MoE architecture keeps per-call costs low. Only a subset of total parameters activates per token. Even when an agent makes hundreds of repeated calls, each call uses minimal compute. This structural efficiency is a significant advantage for agentic cost management.
Up to 262K context windows extend agent memory. Long conversation histories, tool call results, and entire code repositories fit in a single context. Agents maintain full context across complex, multi-step tasks without losing the thread.
Native multimodal processing across text, image, and video. A single model handles diverse input types without a separate multimodal pipeline, simplifying agent architecture.
Serving Qwen on AIEEV's distributed GPU infrastructure reduces the cost of an already efficient model even further. Air API is the simplest way to access this combination.
Qwen Models Available on Air API
Air API currently offers five Qwen models with distinct use cases and cost profiles. Here is what each model does well and where it fits.
Qwen3.6-35B-A3B
The flagship model of the Qwen3.6 series, built on a Mixture-of-Experts (MoE) architecture. Out of 35 billion total parameters, only 3 billion activate per token, delivering frontier-level performance with minimal compute. It adds thinking mode, video understanding, and multi-token prediction (MTP), tuned for agentic coding and tool-use workloads.
The flagship model of the Qwen3.5 series, built on a Mixture-of-Experts (MoE) architecture. Out of 35 billion total parameters, only 3 billion activate per token -- delivering frontier-level performance with minimal compute. Benchmark results show it outperforming models 7x its size on coding, reasoning, and multimodal tasks.
Recommended for | AI agent developers, coding assistant teams, services requiring long-document analysis |
Parameters | 35B total (3B active, MoE architecture) |
Context window | 262,144 tokens natively, extensible to roughly 1,010,000 tokens (source: huggingface) |
Max output | 32,768 tokens recommended / 81,920 tokens for complex tasks (source: huggingface) |
License | Apache 2.0 |
Pricing | Input (/1M Tokens) : ₩210 ($0.14) Output (/1M Tokens): ₩1,500 ($1.00) |
Strengths
Only 3B active parameters per token keeps inference cost very low -- the most economical choice at this performance tier
262,144-token native context, extensible to roughly 1M tokens -- handles full-length documents and large codebases
Native multimodal support for text, image, and video, plus thinking mode and agentic coding/tool-use capabilities
Limitations
MoE architecture offers less fine-tuning stability compared to Dense models
In some edge cases, accuracy may be marginally lower than comparable Dense models
👉👉 See detailed specs and usage examples for Qwen3.6-35B-A3B in the official docs.
Qwen3.5-9B
A Dense 9B parameter model that outperforms models 13x its size (GPT-OSS-120B) on major benchmarks. It scores 82.5 on MMLU-Pro (vs. 80.8) and 91.5 on IFEval (vs. 88.9), making it the strongest performer in the sub-10B class.
Recommended for | Cost-sensitive startups, multimodal chatbot developers, real-time services requiring fast response |
Parameters | 9B (Dense) |
Context window | 262K tokens (expandable to 1M) |
Max output | 32,768 tokens recommended / 81,920 tokens for complex tasks |
License | Apache 2.0 |
Pricing | Input (/1M Tokens) : ₩150 ($0.10) Output (/1M Tokens): ₩225 ($0.15) |
Strengths
Outperforms models 13x larger -- large-model quality at small-model cost
Native multimodal support for text, image, and video across 201 languages
Expandable from 262K to 1M token context for ultra-long document processing
Limitations
Hallucination rates are relatively higher on fact-based tasks -- RAG pipelines are recommended
Performance on document-specific tasks (table extraction, handwriting recognition) is lower than specialized models
👉👉 See detailed specs and usage examples for Qwen3.5-9B in the official docs.
Qwen3-32B
A Dense 32.8B parameter model with a hybrid reasoning architecture that switches between thinking and non-thinking modes. Thinking mode handles complex logical reasoning, math, and coding, while non-thinking mode delivers fast responses for conversational tasks.
Recommended for | Teams handling both complex reasoning and fast-response workloads, services that need a balance of cost and performance |
Parameters | 32.8B (Dense) |
Context window | 32,768 tokens natively, extensible to 131,072 tokens with YaRN (source: huggingface) |
Max output | 32,768 tokens (recommended) |
License | Apache 2.0 |
Pricing | Input (/1M Tokens) : ₩120 ($0.08) Output (/1M Tokens): ₩420 ($0.28) |
Strengths
Hybrid reasoning with switchable thinking/non-thinking modes -- tune for accuracy or speed as needed
Context extensible to 131,072 tokens with YaRN
Multilingual Dense model supporting 100+ languages
Limitations
Native context is limited to 32,768 tokens without extension
Text-only Dense model -- no vision/image input support
👉👉 See detailed specs and usage examples for Qwen3-32B in the official docs.
Qwen3-Embedding-8B
An embedding-only model that converts text into vectors for search, RAG, and similarity analysis. It produces up to 4,096-dimensional vectors, with output dimensions configurable between 32 and 4,096.
Recommended for | Teams building RAG pipelines, semantic search/recommendation systems, multilingual document embedding |
Parameters | 8B |
Max input length | 32,768 tokens (source: huggingface) |
Embedding dimensions | Up to 4,096 (configurable from 32 to 4,096) |
License | Apache 2.0 |
Pricing | Input (/1M Tokens) : ₩15 ($0.01) Output: ₩0 ($0.00) -- no output charge for embedding models |
Strengths
Multilingual embeddings covering 100+ languages plus multiple programming languages
Configurable vector size from 32 to 4,096 dimensions to balance storage cost and retrieval accuracy
Low cost at ₩15 ($0.01) per 1M input tokens, no output charge
Limitations
Embedding-only -- cannot generate text responses; must be paired with a separate LLM
Max input length of 32,768 tokens means very long documents require chunking
👉👉 See detailed specs and usage examples for Qwen3-Embedding-8B in the official docs.
Qwen3-TTS
The current open-source lineup is a 12Hz-based 0.6B and 1.7B TTS series supporting 3-second voice cloning and natural language voice control. It covers 10 languages including Korean and English, achieves first-packet latency as low as 97ms for real-time streaming, and offers three model tiers -- Base, CustomVoice, and VoiceDesign -- to support a wide range of voice service scenarios.
Recommended for | Voice AI developers, content creators, multilingual voice guidance system teams |
Parameters | 1.7B (Flagship) - (Base / CustomVoice / VoiceDesign) 0.6B (Lightweight) - (Base / CustomVoice) |
Supported languages | 10 (Korean, English, Chinese, Japanese, German, French, Russian, Portuguese, Spanish, Italian) |
License | Apache 2.0 |
Pricing | ₩120 ($0.08)/ 1,000 characters |
Strengths
Supports voice cloning from 3-second audio samples (Base), natural language voice design (1.7B VoiceDesign), and preset voice style control (CustomVoice)
Real-time streaming with first-packet latency of 97ms (0.6B) and 101ms (1.7B) across 10 languages including Korean
Apache 2.0 license with no commercial use fees -- significant cost savings at scale compared to ElevenLabs
Limitations
VoiceDesign is available on the 1.7B model only -- lightweight deployments are limited to Base and CustomVoice on 0.6B
Language support is limited to 10 languages (compared to ElevenLabs' 29 and OpenAI TTS's 57)
CustomVoice preset voices are limited to 9 options
English voice output retains a slight dubbed quality -- may not meet requirements for premium English voice use cases
👉👉 See detailed specs and usage examples for Qwen3-TTS in the official docs.
Get Started Now
Air API runs on AIEEV's distributed GPU infrastructure. Because it connects idle GPUs around the world rather than relying on physical data centers, it delivers the same models at lower cost. Integrate AI into your product without worrying about the bill.
