Qwen · Efficient
Qwen3.6 Flash
Qwen3.6 Flash is Alibaba's fast native vision-language model in the Qwen 3.6 family. It improves over 3.5 Flash with stronger coding/agent performance and better spatial intelligence, including object localization and detection.
- Vision
- Video input
- Input
- Text, Images, Video
- Output
- Text
- Discovery tier
- Efficient
Pricing
USD per 1M tokens- Input
- $0.19
- Output
- $1.16
- Cache read
- $0.02
- Cache write
- $0.24
Reference prices as published by the model provider. API example
Specifications
- Context window
- 991.8K
- Max output
- 65.5K
Side by side
Qwen3.6 Flash compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Qwen3.6 FlashQwenThis model | DeepSeek V4 Flash Vision ExpDeepSeekRemove | |
|---|---|---|
| Provider | Qwen | DeepSeek |
| Discovery tier | Efficient | Efficient |
| Added | Apr 17, 2026 | Aug 21, 2026 |
| Limits | ||
| Context window | 991.81K | 1.05MBest |
| Max output | 65.54K | 384KBest |
| Average output | — | 1.3K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.19Best | $0.22 |
| Output | $1.16 | $0.65Best |
| Cache read | $0.02 | $0.0069Best |
| Cache write | $0.24 | — |
| Performance | ||
| Throughput | — | 131.9 tok/s |
| Time to first token | — | 2.9 s |
| Request success (24h) | 100.0%Best | 99.7% |
| Benchmark indexes | ||
| Intelligence Index | — | 34.3 |
| Coding Index | — | 69.1 |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | No | Yes |
| Vision | Yes | Yes |
| Structured output | No | Yes |
| Video input | Yes | No |
| Reasoning effort | — | none, low, high, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Qwen3.6 Flash
- DeepSeek V4 Flash Vision Exp
Intelligence Index
- Qwen3.6 Flash—
- DeepSeek V4 Flash Vision Exp34.3
Indexes
Coding Index
Composite of the coding evaluations
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 69.1
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 54.0%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 23.0%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 1,257 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 1,433 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 11.0%
AA-LCRv1.1
Reasoning across long documents
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 79.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 38.6%
GPQA Diamond
Graduate-level science questions
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 90.8%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 12.1%
SciCode
Scientific computing in Python
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 50.3%
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Qwen3.6 Flash: —
- DeepSeek V4 Flash Vision Exp: 79.7%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Live routing data
Performance and reliability
Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.
- Request success (24h)
- 100.0%
- Reliable
- Healthy (98% or more)
- Degraded (90–98%)
- Down (under 90%)
- No requests
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen/qwen3.6-flash",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="qwen/qwen3.6-flash",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)Qwen
Related models
Other Qwen models, starting with the same family.