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Qwen · Open weights

Qwen3.5 9B

Added Mar 10, 2026Open weights

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Use in the app

Qwen3.5 9B is a multimodal foundation model from the Qwen 3.5 family, built for efficient reasoning, coding, and visual understanding in a compact 9B architecture. Requests containing video cost $0.17 per million input tokens and $0.25 per million output tokens.

  • Reasoning
  • Tools
  • Vision
  • Video input
Input
Text, Images, Video
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.05
Output
$0.15

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
65.5K
Average output (7 days)
1.4K tokens
Longer than 84% of 465 models

Side by side

Qwen3.5 9B compared

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

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Qwen3.5 9BQwenThis modelDeepSeek V3.1 ThinkingDeepSeekRemove
ProviderQwenDeepSeek
Discovery tierOpen weightsOpen weights
AddedMar 10, 2026Jan 1, 2024
Limits
Context window262.14KBest128K
Max output65.54K65.54K
Average output1.4K tokens483 tokens
Pricing, USD per 1M tokens
Input$0.05Best$0.20
Output$0.15Best$0.70
Performance
Throughput—14.2 tok/s
Time to first token—2.5 s
Request success (24h)99.8%100.0%Best
Benchmark indexes
Intelligence Index13.313.5Best
Coding Index23.5—
Math Index—89.7
Capabilities
ReasoningYesNo
ToolsYesYes
VisionYesNo
Video inputYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • Qwen3.5 9B
  • DeepSeek V3.1 Thinking

Intelligence Index

  • Qwen3.5 9B13.3
  • DeepSeek V3.1 Thinking13.5

Qwen3.5 9B: Better than 33% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Qwen3.5 9B: 23.5
    • DeepSeek V3.1 Thinking: —

  • Math Index

    Composite of the math evaluations

    • Qwen3.5 9B: —
    • DeepSeek V3.1 Thinking: 89.7

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Qwen3.5 9B: 46.0%
    • DeepSeek V3.1 Thinking: 56.7%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Qwen3.5 9B: 9.4%
    • DeepSeek V3.1 Thinking: 14.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • Qwen3.5 9B: 78.6%Top
    • DeepSeek V3.1 Thinking: 77.9%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Qwen3.5 9B: —
    • DeepSeek V3.1 Thinking: 85.1%

  • IFBench

    Following precise output instructions

    • Qwen3.5 9B: 37.8%
    • DeepSeek V3.1 Thinking: 41.5%Top

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Qwen3.5 9B: —
    • DeepSeek V3.1 Thinking: 78.4%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Qwen3.5 9B: —
    • DeepSeek V3.1 Thinking: 89.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Qwen3.5 9B: 18.2%
    • DeepSeek V3.1 Thinking: 25.0%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Qwen3.5 9B: 85.1%Top
    • DeepSeek V3.1 Thinking: 37.4%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Qwen3.5 9B: 46.0%
    • DeepSeek V3.1 Thinking: 56.7%Top

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
14.3%
Request success (24h)
99.8%
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 96.3%
  • 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/qwen3.5-9b",
    "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/qwen3.5-9b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Qwen

Other Qwen models, starting with the same family.