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Qwen: Qwen3 VL 235B A22B Instruct Coding Benchmark

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...

Context131,072tokens
Max Output32,768tokens
Inputmodalities
Price$0.26/1M input

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Coding Performance

Coding benchmarks and performance metrics for development tasks

Security

Enkrypt AI red-team scores for Qwen: Qwen3 VL 235B A22B Instruct. Each value is the share of successful attacks on a 0–100 scale — lower is safer.

Overall risk
27.0

Lower is safer

Safety

29.3/ 100

Composite safety risk from Enkrypt red-team evaluations. Lower is safer.

NIST

27.0/ 100

Average attack success across NIST-mapped tests: bias, harm, toxicity, CBRN, and insecure code.

OWASP

30.0/ 100

Weighted average of the same tests using OWASP Top 10 for LLMs 2025 risk rankings.

Attack categories

Percentage of successful attacks in each Enkrypt red-team category.

Jailbreak18.1/ 100

Share of jailbreak tests that bypassed the model's safety constraints.

Bias72.6/ 100

Share of tests that elicited biased responses.

Harmful content29.4/ 100

Share of tests that produced dangerous, violent, or hateful content.

Toxicity5.5/ 100

Share of tests that produced toxic or abusive content.

CBRN17.5/ 100

Share of tests that elicited chemical, biological, radiological, or nuclear assistance.

Insecure code9.8/ 100

Share of tests that produced vulnerable or malicious code.

Score
Value
Overall risk
27.0 / 100
Safety
29.3 / 100
NIST
27.0 / 100
OWASP
30.0 / 100
Jailbreak
18.1 / 100
Bias
72.6 / 100
Harmful content
29.4 / 100
Toxicity
5.5 / 100
CBRN
17.5 / 100
Insecure code
9.8 / 100

Security scores from the Enkrypt AI Safety Leaderboard · Last checked Sep 17, 2026

Real-World Usage

Real-world usage statistics from the Kilo Code community

Weekly Token Usage

No ranking data available for this model yet.

Real-world metrics from the Kilo Code Leaderboard

Pricing

Cost per 1 million tokens

Input Tokens
$0.26
per 1M tokens
Output Tokens
$1.04
per 1M tokens

Example Cost

Analyzing a 10,000 line codebase (≈40k input tokens, 10k output tokens) costs approximately $0.0208

Coding Capabilities

Features and parameters relevant to coding tasks

Coding Features

Function Calling
Can call external functions/APIs
Tool Choice
Control over function selection
Structured Outputs
JSON schema validation
Reasoning Tokens
Extended thinking for complex problems

Pricing details from OpenRouter

Technical Details

Architecture and implementation specifications

Specifications
Model ID
qwen/qwen3-vl-235b-a22b-instruct
Created
September 23, 2025
Tokenizer
Qwen3
Input Modalities
TextImage
Context Window
131,072 tokens
Max Completion Tokens
32,768 tokens
Input Price
$0.26 per 1M tokens
Output Price
$1.04 per 1M tokens
Content Moderation
Disabled

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