Alibaba (Qwen) · Fast / Budget · 1M context window
$0.000826 per call at 3,000 input / 800 output tokens.
Per call
$0.000826
10K calls/mo
$8.26
Cache savings
89% off input
Batch savings
50% off all
Adjust token counts, volume, and discount toggles to model your json extraction spend. Prices verified daily against provider pricing pages.
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=800,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.000826 per call at standard rates
# Input: $0.15/MTok
# Output: $0.47/MTokPer-call cost for all models scoring 50+ on json extraction benchmarks. Hover for details.
Highest-quality affordable option: Gemini 3 Flash at $0.003900/call (4.7x the cost, 86/100 quality). Qwen 3.8 Flash is already the cheapest choice for json extraction.
Structured JSON output is compact and predictable, so cost is set almost entirely by how much source text you send. The schema itself is identical on every call and belongs in the cached prefix. This is also a task where a smaller model often matches a larger one: the output space is constrained by the schema, which limits how wrong a cheaper model can go.
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 3,000/800 token split, output accounts for 46% of the $0.000826 per-call cost.
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $0.15 | 3,000 |
| Output | $0.47 | 800 |
Caching drops input cost from $0.15 to $0.02/MTok. For repeated system prompts, that saves $0.000402 per call.
Submit requests asynchronously and receive 50% off all token costs. This works best for offline pipelines where latency requirements are flexible.
| Monthly volume | Cost per call | Standard monthly | Cached monthly | You save with caching |
|---|---|---|---|---|
| 10,000 calls | $0.000826 | $8.26 | $4.24 | $4.02 |
| 50,000 calls | $0.000826 | $41.3 | $21.21 | $20.09 |
| 100,000 calls | $0.000826 | $82.6 | $42.42 | $40.18 |
| 500,000 calls | $0.000826 | $413 | $212.08 | $200.92 |
| 1,000,000 calls | $0.000826 | $826 | $424.15 | $401.85 |
Benchmark data for json extraction 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.
2 models score 80+ on json extraction benchmarks, sorted by input rate. Cheapest: Gemini 3 Flash (372% more, 86/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs Qwen |
|---|---|---|---|---|---|---|
| Gemini 3 Flash | Gemini | $0.50 | $3.00 | 86/100 | $0.003900 | 372% more |
| Claude Haiku 4.5 | Claude | $1.00 | $5.00 | 88/100 | $0.007000 | 747% more |
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.
| 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.
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
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
Product Descriptions
$0.000780/call
A typical json extraction call using Qwen 3.8 Flash costs about $0.000826 per call. This estimate uses 3,000 input tokens and 800 output tokens at standard rates. Standard rates are $0.15 per million input tokens and $0.47 per million output tokens.
Our benchmark data does not yet include a json extraction ranking for Qwen 3.8 Flash. We recommend testing it against your own evaluation criteria before production usage.
The most affordable model for json extraction above 80/100 quality is Gemini 3 Flash. It comes from Gemini at $0.50 per million input tokens. Output costs $3.00 per million tokens with a quality score of 86/100. Gemini 3 Flash costs more per call than Qwen 3.8 Flash but scores higher on quality benchmarks for this task type.
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 $0.000826 to about $0.000044 per call.
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 json extraction workload prioritizes quality, cost, or speed.
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 json extraction, typical calls use 3,000 input and 800 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.
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.