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If you have been following the AI leaderboards lately, you have likely noticed a new name constantly trading blows with GPT-4o and Claude 3.5 Sonnet: Qwen.
Developed by Alibaba Cloud, the Qwen model family (specifically Qwen 2.5 and Qwen 3) has exploded in popularity for one simple reason: unbeatable price-to-performance. In 2025, Qwen is widely considered the “king of coding and math” among open-weight models, frequently outperforming Llama 3.1 in complex reasoning tasks while being significantly cheaper to run.
Because Alibaba released the weights for these models, you aren’t forced to use a single proprietary API. This has created a competitive market where providers race to offer the lowest price. This guide cuts through the noise to give you the definitive pricing strategy for Qwen.
If you just want the quick answer on where to go to save the most money, here is your cheat sheet.
| Best For… | Provider Recommendation | Why? |
| Lowest Price & Best Variety | DeepInfra | Offers near-at-cost pricing for the widest range of Qwen models, including Coder and Vision variants. |
| Proprietary Models (Qwen-Max) | Alibaba Cloud | The only place to access the closed-source “Qwen-Max” model, which has slightly higher reasoning caps. |
| Easiest to Start | Together AI / OpenRouter | User-friendly aggregators with great documentation, though sometimes slightly more expensive than DeepInfra. |
| Developers using RAG | DeepInfra | Supports Context Caching, which creates massive savings for document-heavy apps. |
Before looking at the price tags, it’s crucial to understand what you’re paying for. AI providers charge per token.
Think of a token as a piece of a word. Roughly, 1,000 tokens equals about 750 words.
The “Chat History” Trap: For a chatbot to “remember” a conversation, you must re-send the entire chat history with every new message. This means your Input Token usage grows with every turn, making low input prices the most critical factor for cost savings.
DeepInfra has established itself as the “power user’s choice” for Qwen. Because they run on bare-metal infrastructure without the massive overhead of a general-purpose cloud, they offer rates that are often 50-80% cheaper than major competitors.
You can view their full list of Qwen models here: DeepInfra Qwen Models.
Here is the current pricing breakdown for the most popular Qwen options on their platform:
| Model Name | Best Use Case | Context Window | Input Price (per 1M) | Output Price (per 1M) |
| Qwen2.5-72B-Instruct | Overall Best. Rivals GPT-4o in reasoning. The gold standard for open-source intelligence. | 32K | $0.23 | $0.23 |
| Qwen2.5-Coder-32B | Coding. Specifically fine-tuned for programming, debugging, and SQL generation. | 32K | $0.20 | $0.20 |
| Qwen2-VL-72B-Instruct | Vision. Can “see” images to analyze charts, screenshots, and PDFs. | 32K | $0.35 | $0.35 |
| Qwen2.5-14B-Instruct | Mid-Range. The “Goldilocks” model—smarter than small models, faster than 72B. | 32K | $0.10 | $0.10 |
| Qwen2.5-7B-Instruct | Speed & Cost. Extremely fast. Perfect for classification, summarization, or simple bots. | 32K | $0.03 | $0.03 |
| Qwen2-57B-A14B | Mixture of Experts (MoE). A highly efficient model that only activates part of its brain per token. | 32K | $0.16 | $0.16 |
Note: Prices are per 1 million tokens. A 32K context window allows the model to process roughly 24,000 words in a single prompt.
Why this matters: At $0.23 per million tokens, Qwen 2.5 72B is roughly 1/10th the price of GPT-4o ($2.50/1M input), despite having very similar benchmark scores in math and coding.
Alibaba Cloud is the creator of Qwen. While their platform is excellent, it is generally more complex to navigate than Western API wrappers. However, you must use them if you need Qwen-Max.
| Model | Type | Input Price (per 1M) | Output Price (per 1M) |
| Qwen-Max | Proprietary Flagship | ~$1.60 | ~$6.40 |
| Qwen-Plus | Balanced | ~$0.40 | ~$1.20 |
| Qwen-Turbo | Fast & Cheap | ~$0.10 | ~$0.30 |
Note: Prices are approximate USD conversions. Regional restrictions (like Singapore-only data centers) may apply for international users.
The proprietary Qwen-Max is powerful, but with output costs over 25x higher than the open-source 72B model on DeepInfra, it is hard to justify for most applications unless you need that specific edge in reasoning.
This is the secret weapon for building cheap AI apps.
Imagine you have a 50-page employee handbook. You want employees to be able to ask questions about it. Without caching, you have to pay to send that 50-page handbook (approx. 25k tokens) to the model every single time a user asks a question.
Context Caching lets you upload the handbook once. The provider keeps it ready in memory.
If you are building a “Chat with PDF” tool or a bot with a long system prompt, caching can lower your bill by 90%. DeepInfra supports this feature for their Qwen models.
Let’s translate these abstract numbers into actual monthly bills.
Estimated Cost:
(Compare this to ~$100+ on GPT-4o).
Estimated Cost:
For 95% of developers and businesses, the days of paying expensive premiums for top-tier AI are over. Qwen 2.5 72B offers “intelligence” that rivals the world’s best models at a price that is nearly negligible.
By choosing the right model and provider, you can build production-grade AI applications for the price of a few lattes a month.
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