Model profile
Alibaba

Qwen: Qwen3 VL 235B A22B Instruct

Qwen: Qwen3 VL 235B A22B Instruct is a budget-priced multimodal generalist from Alibaba with partial runtime data, large context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalN/A latencyLarge contextBudget pricing
Intelligence
14.4

Benchmark blend

Coding
0.594

Dev workflow signal

Context
262K Tokens

Large

Input Price
$0.40

Budget tier

Decision snapshot
52

Qwen: Qwen3 VL 235B A22B Instruct currently reads as a budget multimodal option with large 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
14.4
14

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
262K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.40
86

$1.60 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3 VL 235B A22B 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 59Math score 71Vision enabled

Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table...

Identity

Alibaba multimodal profile

Positioning

Long-context research / Multimodal with large 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
14.4
MMLU Pro
82.3%
GPQA
71.2%
HLE
6.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.594
SciCode
35.9%
Math

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

Math Index
70.7
AIME 2025
70.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
42.7%
TAU2
35.1%
TerminalBench Hard
6.8%
LCR
32.0%

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
262K Tokens
Vision
Enabled
Modalities
image, text
Tokenizer
Qwen3
Max Completion
32768
Moderation
No
Supported Parameters
frequency_penaltylogit_biaslogprobsmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
imagetext
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.40
Output
per 1M output tokens
$1.60
Blended
AA 3:1 mix
$0.70

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

OR Cache Read
$0.00

Metadata

Raw source tables at the end

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