Alibaba (Qwen) · Fast / Budget · 1M context window
$0.000920 per call at 3,000 input / 1,000 output tokens.
Per call
$0.000920
10K calls/mo
$9.2
Cache savings
89% off input
Batch savings
50% off all
Adjust token counts, volume, and discount toggles to model your customer support bot 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=1000,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.000920 per call at standard rates
# Input: $0.15/MTok
# Output: $0.47/MTokPer-call cost for all models scoring 50+ on customer support bot benchmarks. Hover for details.
Highest-quality affordable option: GPT 5.6 Luna at $0.001800/call (2.0x the cost, 85/100 quality). Qwen 3.8 Flash is already the cheapest choice for customer support bot.
Support bots combine RAG's retrieved context with a chatbot's growing history, so they carry both cost curves at once. Volume is high and margins per conversation are thin, which makes the per-call rate matter more than in almost any other use case here. Caching the knowledge base and product context is the single highest-leverage change.
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 3,000/1,000 token split, output accounts for 51% of the $0.000920 per-call cost.
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $0.15 | 3,000 |
| Output | $0.47 | 1,000 |
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 | $0.000920 | $9.2 | $5.18 | $4.02 |
| 50,000 calls | $0.000920 | $46 | $25.91 | $20.09 |
| 100,000 calls | $0.000920 | $92 | $51.82 | $40.19 |
| 500,000 calls | $0.000920 | $460 | $259.08 | $200.93 |
| 1,000,000 calls | $0.000920 | $920 | $518.15 | $401.85 |
Benchmark data for customer support bot 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.
12 models score 80+ on customer support bot benchmarks, sorted by input rate. Cheapest: GPT 5.6 Luna (96% more, 85/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs Qwen |
|---|---|---|---|---|---|---|
| GPT 5.6 Luna | GPT | $0.20 | $1.20 | 85/100 | $0.001800 | 96% more |
| Claude Haiku 4.5 | Claude | $1.00 | $5.00 | 86/100 | $0.008000 | 770% more |
| Gemini 3.5 Flash | Gemini | $1.50 | $9.00 | 84/100 | $0.0135 | 1367% more |
| Claude Sonnet 5 | Claude | $2.00 | $10.00 | 89/100 | $0.0160 | 1639% more |
| GPT 5.6 Terra | GPT | $2.00 | $12.00 | 92/100 | $0.0180 | 1857% more |
| Gemini 3.1 Pro | Gemini | $2.00 | $12.00 | 87/100 | $0.0180 | 1857% more |
| GPT 5.4 | GPT | $2.50 | $15.00 | 91/100 | $0.0225 | 2346% more |
| Claude Sonnet 4.6 | Claude | $3.00 | $15.00 | 88/100 | $0.0240 | 2509% more |
| GPT 5.6 Sol | GPT | $4.00 | $20.00 | 91/100 | $0.0320 | 3378% more |
| Claude Opus 4.8 | Claude | $5.00 | $25.00 | 85/100 | $0.0400 | 4248% more |
| GPT 5.5 | GPT | $5.00 | $30.00 | 90/100 | $0.0450 | 4791% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 88/100 | $0.0800 | 8596% 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
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
Product Descriptions
$0.000780/call
A typical customer support bot call using Qwen 3.8 Flash costs about $0.000920 per call. This estimate uses 3,000 input tokens and 1,000 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 customer support bot ranking for Qwen 3.8 Flash. We recommend testing it against your own evaluation criteria before production usage.
The most affordable model for customer support bot above 80/100 quality is GPT 5.6 Luna. It comes from GPT at $0.20 per million input tokens. Output costs $1.20 per million tokens with a quality score of 85/100. GPT 5.6 Luna 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 $0.000920 to about $0.000049 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 customer support bot 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 customer support bot, typical calls use 3,000 input and 1,000 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.