Model profile
Alibaba
New in 2026

Qwen: Qwen3.5-9B

Qwen: Qwen3.5-9B is a budget-priced multimodal generalist from Alibaba with fast runtime profile, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalFast latencyLarge contextBudget pricing
Intelligence
32.4

Benchmark blend

Coding
25.3

Dev workflow signal

Context
256K Tokens

Large

Input Price
$0.07

Budget tier

Decision snapshot
61

Qwen: Qwen3.5-9B currently reads as a budget multimodal option with large context and a fast runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Multimodal
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial AnalysisVision signal

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
32.4
32

General reasoning and benchmark headroom.

Limited
Speed
142 tok/s
100

TTFT 0.26s

Above average
Context
256K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.07
86

$0.17 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3.5-9B in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score 25Math score N/AVision enabled

Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...

Identity

Alibaba multimodal profile

Positioning

Long-context research / Multimodal with large context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Vision-capable routing opens up multimodal review and extraction workflows.

  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

  • Long-context summarization, repo analysis, and policy or document review.

Explore Next

Similar profiles worth opening next

Alibaba

Qwen: Qwen3.6 Plus

Intelligence
50.0
Context
1000K Tokens
Input Price
$0.50
Alibaba

Qwen: Qwen3.5 397B A17B

Intelligence
45.0
Context
262K Tokens
Input Price
$0.60
Alibaba

Qwen3.5 27B (Reasoning)

Intelligence
42.1
Context
N/A
Input Price
$0.30

Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
32.4
GPQA
80.6%
HLE
13.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
25.3
SciCode
27.5%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
66.7%
TAU2
86.8%
TerminalBench Hard
24.2%
LCR
59.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
256K Tokens
Vision
Enabled
Modalities
image, text, video
Tokenizer
Qwen3
Max Completion
32768
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
imagetextvideo
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.07
Output
per 1M output tokens
$0.17
Blended
AA 3:1 mix
$0.10

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.