The Qwen3 series offers a multi-layered lineup including text processing, image understanding, speech recognition, text-to-speech, real-time dialogue, code generation, and commercial flagship models.
However, pricing structures vary significantly by model, with differences in billing units (tokens, seconds, characters), tiered pricing based on input length, and regional price variations.
This article organizes the pricing structures for each Qwen3 model and summarizes approaches to cost estimation.
Table of Contents
- Common Rules Across Qwen3 Pricing
- Qwen3 Max Pricing Levels
- Speech Recognition Model: Qwen3 ASR Pricing
- Text-to-Speech Model: Qwen3 TTS Pricing
- Vision Model: Qwen3 VL Pricing
- Open-Weight Models: Qwen3 LLM, Omni, and Coder Pricing
- Cost Estimation Approaches
- Key Points When Checking Pricing
- Summary
Common Rules Across Qwen3 Pricing
First, let’s organize the basic rules common to all models.
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Model Studio activation itself is free; billing only occurs during API usage
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Free tiers may be set for each model, with quantities and periods varying by region and timing
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Always refer to pricing tables for your usage region; base calculations on actual regions like International Singapore
Qwen3 Max Pricing Levels
Qwen3 Max is the commercial flagship text model, an API model used for high-difficulty tasks and agent applications.
Billing Method
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Pay-per-token with different rates for input and output
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Tiered pricing applies based on input token count
International Region Pricing Examples
Below are representative price ranges for International Singapore (per million tokens).
- 0–32K tokens
Input $1.20, Output $6.00
- 32K–128K tokens
Input $2.40, Output $12.00
- 128K–252K tokens
Input $3.00, Output $15.00
With context caching support, the design makes it easy to reduce effective input costs when reusing the same context in long-form RAG or extended dialogues.
Speech Recognition Model: Qwen3 ASR Pricing
Qwen3 ASR is an automatic speech recognition model that converts audio to text, supporting both batch processing of recorded files and real-time processing.
Billing Method
- Both recorded files and real-time processing are billed by the second
International Region Pricing Examples
- File recognition: qwen3 asr
Approximately $0.000035/second
Free tier of ~36,000 seconds (10 hours) granted, available for a period from activation date
- Real-time recognition: qwen3 asr flash realtime
Approximately $0.000090/second (varies by region)
Context bias functionality is available for speech recognition, enabling improved accuracy for proper nouns like company and product names.
Text-to-Speech Model: Qwen3 TTS Pricing
Qwen3 TTS is a text-to-speech model that converts text to audio, available in standard and real-time versions.
Billing Method
- Billed by character count
For example, letters, symbols, and spaces count as one character; Chinese characters count as two
International Region Pricing Examples
- qwen3 tts flash (standard)
Approximately $0.10–0.114682/10,000 characters
Free tier of ~2,000 characters granted
- qwen3 tts flash realtime (real-time version)
Approximately $0.13/10,000 characters as reference
About 17 voice types are available, with multilingual and dialect support.
Vision Model: Qwen3 VL Pricing
Qwen3 VL is a vision model that returns text content from images, PDFs, and videos. It can be used for structuring invoices and reports, understanding screenshots, and summarizing videos.
Billing Method
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Pay-per-token with different rates for input and output
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Tiered pricing for long inputs applies to most plans
Model Studio pricing tables show tier-based pricing where rates change according to input token bands, along with free tier availability.
Note that when using third-party providers like OpenRouter, independent pricing structures apply, so prioritize checking the actual provider’s pricing.
Open-Weight Models: Qwen3 LLM, Omni, and Coder Pricing
Qwen3’s text LLM, Omni, and Coder include models provided as open weights, with cost characteristics varying by usage method.
- Self-hosting
No model usage fees; only infrastructure costs like GPU
- Using as managed API via Model Studio
Token-based billing applies per model
Below, we organize pricing examples using representative models.
Qwen3 Omni Pricing Examples
Omni supports real-time audio input/output with separate rates for text, audio, and image inputs. Below are international region prices using qwen3-omni-flash as an example.
Input Pricing
| Input Format | Rate (USD/million tokens) |
|---|---|
| Text input | 0.52 |
| Audio input | 4.57 |
| Image input | 0.94 |
Output Pricing
| Input Type | Output Format | Rate (USD/million tokens) |
|---|---|---|
| Text only input | Text output | 1.99 |
| Input including image or audio | Text output | 3.67 |
| Input including image or audio | Audio output | 18.13 (text output free) |
Since rates vary depending on input and output formats, requirement-based design becomes important.
Qwen3 Coder Pricing Examples
The API version of Qwen3 Coder provides token billing tables by region. Below are prices using qwen3-omni-coder for international regions as an example.
Input Pricing
| Input Tokens (per request) | Input Rate (USD/million tokens) |
|---|---|
| 0 < Tokens ≤ 32K | 1.0 |
| 32K < Tokens ≤ 128K | 1.8 |
| 128K < Tokens ≤ 256K | 3.0 |
| 256K < Tokens ≤ 1M | 6.0 |
Output Pricing
| Input Tokens (per request) | Output Rate (USD/million tokens) |
|---|---|
| 0 < Tokens ≤ 32K | 5.0 |
| 32K < Tokens ≤ 128K | 9.0 |
| 128K < Tokens ≤ 256K | 15.0 |
| 256K < Tokens ≤ 1M | 60.0 |
Higher pricing tiers apply with longer contexts, making prompt design and context reuse strategies directly impact costs.
Qwen3 LLM Pricing Examples
Dense and MoE models also use token-based billing when using Model Studio API. Here we use qwen3-next-80b-a3b-thinking as a representative example. Actual pricing varies by weight size and Thinking/Non-Thinking mode differences.
| Item | Details |
|---|---|
| Input Cost | $0.15/million tokens |
| Output Cost | $1.20/million tokens |
| Free Tier | 1 million tokens combined input/output |
| Free Tier Validity | Set period from Model Studio activation |
Larger models and Thinking-mode models tend to have higher rates, while smaller models and Non-Thinking mode have lower rates.
Cost Estimation Approaches
To accurately estimate AI model usage costs, it’s important to understand the billing mechanisms and translate actual usage patterns into numbers. Here we organize approaches separately for text/image models and audio models.
Text and Image Models
Text LLMs, Qwen3 VL, and the text portion of Omni fundamentally use token-based billing. Key approaches include:
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Multiply input token count by rate
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Multiply output token count by rate
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Identify which tier applies based on input token tiering
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Models with context caching become more advantageous with context reuse
At the planning stage, assume input and output lengths for a representative single request, then multiply that cost by estimated monthly request volume for easier estimation.
Audio-Related Models
Speech recognition and text-to-speech use seconds or character counts as billing units, with different calculation methods from token billing.
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Qwen3 ASR bills based on recording seconds
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Qwen3 TTS bills based on text character count, with Chinese characters counting as two
Particularly for languages like Japanese that use many kanji, note that the actual billable character count varies depending on character type composition even for the same text.
Key Points When Checking Pricing
Finally, let’s organize points to always verify during estimation and implementation consideration.
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Pricing varies by region, so always refer to pricing tables for your intended usage region
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Free tier quantities and validity periods may be updated with campaigns or releases
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Open-weight models are exempt from API billing when self-hosted, but switch to token billing when using Model Studio versions
Summary
Qwen3 pricing structures differ by model—token-based, second-based, or character-based billing—with additional factors like input length tiering, free tiers, and regional price variations.
Start by organizing whether your data format is text, image/video, or audio, and confirm the applicable model’s billing method. Then, by assuming per-request input and output volumes and multiplying by model-specific rates, you can grasp approximate costs.
At the PoC stage, it’s reassuring to use small sample data close to actual use cases, measure real costs, and set budget caps. Based on those results, adjust model selection, prompt design, and call frequency control as needed to leverage the Qwen3 series cost-effectively.