Alibaba (Qwen) · Flagship · 1M context window
$0.0320 per call at 4,000 input / 4,000 output tokens.
Per call
$0.0320
10K calls/mo
$320
Cache savings
88% off input
Batch savings
50% off all
Adjust token counts, volume, and discount toggles to model your code refactoring spend. Prices verified daily against provider pricing pages.
Call Qwen 3.8 Max 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-max",
max_tokens=4000,
messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.0320 per call at standard rates
# Input: $2.00/MTok
# Output: $6.00/MTokPer-call cost for all models scoring 50+ on code refactoring benchmarks. Hover for details.
Cheapest viable option: Claude Haiku 4.5 at $0.0240/call (25% less, 72/100 quality).
Refactoring sends existing code and gets a rewritten version back, so input and output are roughly symmetric, unusual for a code task. That symmetry means neither rate dominates and total cost tracks the size of the file. The practical lever is scope: refactoring one module per call rather than a whole package keeps you off the long-context pricing tier, where some providers re-rate the entire request.
For Qwen 3.8 Max specifically, that means output tokens cost 3.0x more than input, at $6.00 vs $2.00 per MTok. At this use case’s 4,000/4,000 token split, output accounts for 75% of the $0.0320 per-call cost.
| Direction | $/MTok | Tokens per call |
|---|---|---|
| Input | $2.00 | 4,000 |
| Output | $6.00 | 4,000 |
Caching drops input cost from $2.00 to $0.25/MTok. For repeated system prompts, that saves $0.007000 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 | $0.0320 | $320 | $250 | $70 |
| 50,000 calls | $0.0320 | $1,600 | $1,250 | $350 |
| 100,000 calls | $0.0320 | $3,200 | $2,500 | $700 |
| 500,000 calls | $0.0320 | $16,000 | $12,500 | $3,500 |
| 1,000,000 calls | $0.0320 | $32,000 | $25,000 | $7,000 |
Benchmark data for code refactoring is pending for Qwen 3.8 Max. Test against your own evaluation suite before moving to production.
Benchmark data is not yet available for Qwen 3.8 Max. We recommend testing against your own evaluation criteria.
5 models score 80+ on code refactoring benchmarks, sorted by input rate. Cheapest: Claude Haiku 4.5 (25% less, 72/100 quality).
| Model | Provider | Input $/MTok | Output $/MTok | Quality | Per call | vs Qwen |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | Claude | $1.00 | $5.00 | 72/100 | $0.0240 | 25% cheaper |
| Claude Opus 5.5 | Claude | $4.00 | $20.00 | 79/100 | $0.0960 | 200% more |
| Claude Opus 5 | Claude | $5.00 | $25.00 | 72/100 | $0.1200 | 275% more |
| Claude Fable 5.1 | Claude | $10.00 | $50.00 | 74/100 | $0.2400 | 650% more |
| Claude Fable 5 | Claude | $10.00 | $50.00 | 72/100 | $0.2400 | 650% more |
Provider
Alibaba (Qwen)
Tier
Flagship
Context
1M tokens
Released
Aug 2026
Prices from help.aliyun.com/en/model-studio (qwen3.8-max, read 2026-10-11). Cache hit: $0.25/MTok input. Not yet benchmarked, so never recommended.
| Model | Tier | Input $/MTok | Output $/MTok | Context |
|---|---|---|---|---|
| Qwen 3.8 Flash | Fast / Budget | $0.15 | $0.47 | 1M |
You can compare Qwen 3.8 Max 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 Max costs change across different workload types.
Code Generation
$0.0280/call
Unit Test Generation
$0.0360/call
SQL Generation
$0.009000/call
API Generation
$0.0420/call
Code Review
$0.0220/call
PR Review
$0.0340/call
Security Review
$0.0270/call
Bug Detection
$0.0220/call
Document Summarization
$0.0260/call
PDF Summarization
$0.0390/call
Meeting Notes
$0.0280/call
Email Summarization
$0.009000/call
Chatbot
$0.0100/call
RAG Pipeline
$0.0210/call
Customer Support Bot
$0.0120/call
FAQ Bot
$0.007800/call
Data Extraction
$0.0140/call
JSON Extraction
$0.0108/call
Invoice Parsing
$0.007600/call
Resume Parsing
$0.009800/call
Chain of Thought
$0.0360/call
Legal Analysis
$0.0440/call
Math Reasoning
$0.0210/call
Financial Analysis
$0.0400/call
Content Moderation
$0.001600/call
Sentiment Analysis
$0.001600/call
Categorization
$0.002500/call
Intent Detection
$0.001200/call
Copywriting
$0.0130/call
Blog Writing
$0.0500/call
Marketing Copy
$0.0196/call
Product Descriptions
$0.0100/call
A typical code refactoring call using Qwen 3.8 Max costs about $0.0320 per call. This estimate uses 4,000 input tokens and 4,000 output tokens at standard rates. Standard rates are $2.00 per million input tokens and $6.00 per million output tokens.
Our benchmark data does not yet include a code refactoring ranking for Qwen 3.8 Max. We recommend testing it against your own evaluation criteria before production usage.
The most affordable model for code refactoring above 80/100 quality is Claude Haiku 4.5. It comes from Claude at $1.00 per million input tokens. Output costs $5.00 per million tokens with a quality score of 72/100. Switching from Qwen 3.8 Max to Claude Haiku 4.5 saves about 25% per API call.
Three strategies lower Qwen 3.8 Max costs for this task type effectively. Prompt caching gives a 88% 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 $0.0320 to about $0.002000 per call.
Alibaba (Qwen) has 1 other models alongside Qwen 3.8 Max today: Qwen 3.8 Flash at $0.15/$0.47 per million tokens (fast / budget). The right choice depends on whether your code refactoring workload prioritizes quality, cost, or speed.
Qwen 3.8 Max supports a context window of 1M tokens per API call. This sets the maximum combined length of your input prompt and generated response. For code refactoring, typical calls use 4,000 input and 4,000 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.