Qwen3.5 Flash / 채팅
The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the 3 series, these models deliver a leap forward in performance for both pure text and multimodal tasks, offering fast response times while balancing inference speed and overall performance.
Playground Chat
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Qwen 3.5 FlashLow-Latency Chat with Optional Search
Qwen 3.5 Flash combines low-latency chat with optional enable_search grounding, streaming defaults, seed control, and standard sampling for product surfaces.
At a Glance
Qwen 3.5 Flash Capabilities
Fast grounded chat for product surfaces.
Optional Live Search
enable_search can ground answers in fresh web results when the task needs current facts.
Streaming Default
stream defaults to true for paint-as-you-go chat UX.
Seed & Sampling
Optional seed plus temperature and top-p for reproducible, tunable replies.
Stop Sequences
Bound generation for parsers and structured product flows.
How It Works
Grounded flash chat path.
Decide Search Need
Enable enable_search when answers need current facts.
Stream Tokens
Consume SSE for immediate UI paint.
Pin Seed for Eval
Use seed when building regression or golden sets.
Bound Output
Apply stop sequences for structured flows.
Qwen 3.5 Flash Domains
Where speed meets freshness.
Knowledge Assist
Search-grounded answers for support and docs.
Research Chat
Current-events synthesis with streaming UX.
Product Q&A
Grounded product facts for storefronts.
Internal Wikis
Seed-reproducible answers for eval sets.
Prompt Tips
Keep Flash grounded and fast.
Request citations or source names when enable_search is on.
Skip search for rewrites and tone edits to save latency.
Seeds make golden-set comparisons reproducible.
Qwen 3.5 Flash Quickstart
Low-latency grounded chat.
curl -X POST "https://api.powertokens.ai/v1/chat/completions" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3.5-flash",
"messages": [
{"role": "user", "content": "What are the latest best practices for prompt caching in production LLM stacks?"}
],
"stream": true,
"temperature": 0.7
}'Technical Specifications
Confirmed parameters and runtime execution protocols.
가격 상세
이 모델의 실제 요금은 API 요청에서 전달된 특정 매개변수를 기반으로 동적으로 계산됩니다. 아래는 구체적인 조합과 해당 가격입니다:
| 모달리티 | 입력 크레딧 | 출력 크레딧 | 입력 가격 | 출력 가격 | 암시적 캐시 적중 | 명시적 캐시 적중 | 캐시 생성 |
|---|---|---|---|---|---|---|---|
| Standard | 100/ 1M Tokens | 400/ 1M Tokens | $0.100 | $0.400 | -- | $0.010 10/ 1M Tokens | $0.125 125/ 1M Tokens |
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Frequently Asked Questions
Everything you need to know before integrating this model.
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