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Qwen · Efficient

Qwen3.5 Flash

Added Feb 24, 2026

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Use in the app

Qwen3.5 Flash is the fastest and cheapest native Qwen 3.5 vision-language model with a 1M-token context window.

  • Vision
  • Video input
Input
Text, Images, Video
Output
Text
Discovery tier
Efficient

Pricing

USD per 1M tokens
Input
$0.10
Output
$0.40

Reference prices as published by the model provider. API example

Specifications

Context window
991.8K
Max output
65.5K

Side by side

Qwen3.5 Flash compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Qwen3.5 FlashQwenThis modelGPT 6 LunaOpenAIRemove
ProviderQwenOpenAI
Discovery tierEfficientEfficient
AddedFeb 24, 2026Sep 22, 2026
Limits
Context window991.81K1.05MBest
Max output65.54K128KBest
Average output—120 tokens
Pricing, USD per 1M tokens
Input$0.10$0.10
Output$0.40Best$0.50
Cache read—$0.01
Cache write—$0.13
Performance
Throughput—296.3 tok/s
Time to first token—3.8 s
Request success (24h)100.0%Best99.7%
Benchmark indexes
Intelligence Index—29.9
Capabilities
ReasoningNoYes
ToolsNoYes
VisionYesYes
Structured outputNoYes
PDF inputNoYes
Video inputYesNo
Reasoning effort—none, low, medium, high, xhigh, max

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Qwen3.5 Flash
  • GPT 6 Luna

Intelligence Index

  • Qwen3.5 Flash—
  • GPT 6 Luna29.9
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 40.5%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 10.2%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 1,097 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 1,263 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 13.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 78.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 18.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 28.3%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 2.5%

  • SciCode

    Scientific computing in Python

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 50.9%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.5 Flash: —
    • GPT 6 Luna: 78.3%

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.

Prompt cache hits
0.0%
Request success (24h)
100.0%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 99.3%
  • 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.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.5-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/qwen3.5-flash",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.