Model profile
Alibaba

Qwen: Qwen3 VL 32B Instruct

Qwen: Qwen3 VL 32B Instruct is a budget-priced multimodal generalist from Alibaba with partial runtime data, extended context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalN/A latencyExtended contextBudget pricing
Intelligence
11.0

Benchmark blend

Coding
0.514

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.16

Budget tier

Decision snapshot
46

Qwen: Qwen3 VL 32B Instruct currently reads as a budget multimodal option with extended context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
N/A
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
11.0
11

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.16
86

$0.64 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3 VL 32B Instruct in one narrative

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

Use-case specificCoding score 51Math score 68Vision enabled

Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...

Identity

Alibaba multimodal profile

Positioning

Long-context research / Multimodal with extended context and partially published 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.

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

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

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
11.0
MMLU Pro
79.1%
GPQA
67.1%
HLE
6.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.514
SciCode
30.1%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
68.3
AIME 2025
68.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
39.2%
TAU2
29.2%
TerminalBench Hard
8.3%
LCR
34.3%

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
131K Tokens
Vision
Enabled
Modalities
image, text
Tokenizer
Qwen
Max Completion
32768
Moderation
No
Supported Parameters
frequency_penaltylogprobsmax_tokenspresence_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
imagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.16
Output
per 1M output tokens
$0.64
Blended
AA 3:1 mix
$0.28

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.