Model profile
qwen
New in 2026

Qwen: Qwen3.5-9B

Qwen: Qwen3.5-9B is a budget multimodal generalist from qwen with a 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
N/A

Dev workflow signal

Context
262K Tokens

Large

Input Price
$0.10

Budget tier

Decision snapshot
62

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
OpenRouterVision 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
133 tok/s
93

TTFT 0.68s

Above average
Context
262K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.10
86

$0.15 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 40Math score 36Vision 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 with early fusion of multimodal tokens, allowing the model to process and reason across text and images within the same context.

Identity

qwen 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.

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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%

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
262K Tokens
Vision
Enabled
Modalities
text, image, video->text
Price architecture
Input
per 1M input tokens
$0.10
Output
per 1M output tokens
$0.15
Blended
AA 3:1 mix
N/A

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.