tokevenAlibaba (Qwen) · Fast / Budget · 1M context window
Qwen 3.8 Flash pricing for product descriptions
$0.000780 per call at 500 input / 1,500 output tokens.
Per call
$0.000780
10K calls/mo
$7.8
Cache savings
89% off input
Batch savings
50% off all
Calculate your Qwen 3.8 Flash costs
Adjust token counts, volume, and discount toggles to model your product descriptions spend. Prices verified daily against provider pricing pages.
Configure your usage
Estimated cost
Quick start
Call Qwen 3.8 Flash with the OpenAI Python SDK pointed at DashScope:
import os
from openai import OpenAI
client = OpenAI(
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
api_key=os.environ["DASHSCOPE_API_KEY"],
)
response = client.chat.completions.create(
model="qwen3.8-flash",
max_tokens=1500,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.000780 per call at standard rates
# Input: $0.15/MTok
# Output: $0.47/MTokHow Qwen 3.8 Flash compares on cost
Per-call cost for all models scoring 50+ on product descriptions benchmarks. Hover for details.
Highest-quality affordable option: Gemini 3.6 Flash at $0.006000/call (7.7x the cost, 76/100 quality). Qwen 3.8 Flash is already the cheapest choice for product descriptions.
What drives product descriptions cost
Catalogue-scale generation: modest output per item, multiplied across thousands of SKUs. Per-call price is small and total spend is set by catalogue size, which makes this a batch API workload almost by definition: descriptions are rarely needed within seconds. Shared brand and formatting context belongs in the cached prefix.
For Qwen 3.8 Flash specifically, that means output tokens cost 3.1x more than input, at $0.47 vs $0.15 per MTok. At this use case’s 500/1,500 token split, output accounts for 90% of the $0.000780 per-call cost.
Complete Qwen 3.8 Flash pricing breakdown
Standard token rates
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $0.15 | 500 |
| Output | $0.47 | 1,500 |
Prompt caching: 89% off input tokens
Caching drops input cost from $0.15 to $0.02/MTok. For repeated system prompts, that saves $0.000067 per call.
Batch API: 50% off all tokens
Submit requests asynchronously and receive 50% off all token costs. This works best for offline pipelines where latency requirements are flexible.
Monthly cost projections
| Monthly volume | Cost per call | Standard monthly | Cached monthly | You save with caching |
|---|---|---|---|---|
| 10,000 calls | $0.000780 | $7.8 | $7.13 | $0.6697 |
| 50,000 calls | $0.000780 | $39 | $35.65 | $3.35 |
| 100,000 calls | $0.000780 | $78 | $71.3 | $6.7 |
| 500,000 calls | $0.000780 | $390 | $356.51 | $33.49 |
| 1,000,000 calls | $0.000780 | $780 | $713.03 | $66.97 |
Qwen 3.8 Flash quality benchmarks for product descriptions
Benchmark data for product descriptions is pending for Qwen 3.8 Flash. Test against your own evaluation suite before moving to production.
Benchmark data is not yet available for Qwen 3.8 Flash. We recommend testing against your own evaluation criteria.
Alternative models for product descriptions
11 models score 80+ on product descriptions benchmarks, sorted by input rate. Cheapest: Gemini 3.6 Flash (669% more, 76/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs Qwen |
|---|---|---|---|---|---|---|
| Gemini 3.6 Flash | Gemini | $0.75 | $3.75 | 76/100 | $0.006000 | 669% more |
| Gemini 3.7 Flash | Gemini | $0.75 | $3.75 | 82/100 | $0.006000 | 669% more |
| Gemini 3.8 Flash | Gemini | $0.75 | $3.75 | 83/100 | $0.006000 | 669% more |
| Gemini 3.5 Flash | Gemini | $1.50 | $9.00 | 75/100 | $0.0143 | 1727% more |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 77/100 | $0.0190 | 2336% more |
| Grok 4.6 | Grok | $2.00 | $6.00 | 78/100 | $0.0100 | 1182% more |
| Grok 4.7 | Grok | $2.00 | $6.00 | 75/100 | $0.0100 | 1182% more |
| GPT 5.4 | GPT | $2.50 | $15.00 | 79/100 | $0.0238 | 2945% more |
| GPT 5.6 Sol | GPT | $4.00 | $20.00 | 76/100 | $0.0320 | 4003% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 81/100 | $0.0400 | 5028% more |
| Claude Fable 5.1 | Claude | $10.00 | $50.00 | 78/100 | $0.0800 | 10156% more |
About Qwen 3.8 Flash
Provider
Alibaba (Qwen)
Tier
Fast / Budget
Context
1M tokens
Released
Aug 2026
Prices from help.aliyun.com/en/model-studio (qwen3.8-flash, read 2026-10-11). Cache hit: $0.016/MTok input. Not yet benchmarked, so never recommended.
Other models from Alibaba (Qwen)
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| Qwen 3.8 Max | Flagship | $2.00 | $6.00 | 1M |
You can compare Qwen 3.8 Flash head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.
Qwen 3.8 Flash pricing for other use cases
See how Qwen 3.8 Flash costs change across different workload types.
Code Generation
$0.002180/call
Unit Test Generation
$0.002800/call
SQL Generation
$0.000695/call
API Generation
$0.003270/call
Code Refactoring
$0.002480/call
Code Review
$0.001690/call
PR Review
$0.002610/call
Security Review
$0.002075/call
Bug Detection
$0.001690/call
Document Summarization
$0.001970/call
PDF Summarization
$0.002955/call
Meeting Notes
$0.002140/call
Email Summarization
$0.000685/call
Chatbot
$0.000770/call
RAG Pipeline
$0.001605/call
Customer Support Bot
$0.000920/call
FAQ Bot
$0.000601/call
Data Extraction
$0.001070/call
JSON Extraction
$0.000826/call
Invoice Parsing
$0.000582/call
Resume Parsing
$0.000751/call
Chain of Thought
$0.002800/call
Legal Analysis
$0.003380/call
Math Reasoning
$0.001635/call
Financial Analysis
$0.003080/call
Content Moderation
$0.000122/call
Sentiment Analysis
$0.000122/call
Categorization
$0.000190/call
Intent Detection
$0.000092/call
Copywriting
$0.001015/call
Blog Writing
$0.003910/call
Marketing Copy
$0.001530/call
Related pages
Frequently asked questions
How much does Qwen 3.8 Flash cost for product descriptions per API call?
A typical product descriptions call using Qwen 3.8 Flash costs about $0.000780 per call. This estimate uses 500 input tokens and 1,500 output tokens at standard rates. Standard rates are $0.15 per million input tokens and $0.47 per million output tokens.
Is Qwen 3.8 Flash the best model for product descriptions?
Our benchmark data does not yet include a product descriptions ranking for Qwen 3.8 Flash. We recommend testing it against your own evaluation criteria before production usage.
What is the cheapest model for product descriptions with good quality?
The most affordable model for product descriptions above 80/100 quality is Gemini 3.6 Flash. It comes from Gemini at $0.75 per million input tokens. Output costs $3.75 per million tokens with a quality score of 76/100. Gemini 3.6 Flash costs more per call than Qwen 3.8 Flash but scores higher on quality benchmarks for this task type.
How can I reduce my Qwen 3.8 Flash costs for product descriptions?
Three strategies lower Qwen 3.8 Flash costs for this task type effectively. Prompt caching gives a 89% discount on input tokens when reusing system prompts. The batch API gives 50% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.000780 to about $0.000042 per call.
How does Qwen 3.8 Flash pricing compare to other Alibaba (Qwen) models?
Alibaba (Qwen) has 1 other models alongside Qwen 3.8 Flash today: Qwen 3.8 Max at $2.00/$6.00 per million tokens (flagship). The right choice depends on whether your product descriptions workload prioritizes quality, cost, or speed.
What context window does Qwen 3.8 Flash support?
Qwen 3.8 Flash supports a context window of 1M tokens per API call. This sets the maximum combined length of your input prompt and generated response. For product descriptions, typical calls use 500 input and 1,500 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.
Track what you spend on AI
This calculator estimates costs based on published rates only. Connect your API usage to see where your money goes and which models drive your spend.