Qwen3 VL 235B A22B Instruct
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...
Updated 4 h ago · first seen 11 Sept 2026
model_01M294WWBMVD5T7V2DC1TS8YJT
Overview
Identity
- Canonical model
- Yesidentity confidence: mediumOne row per real model release. Artifacts (checkpoints, quantisations, conversions) and folded evaluation variants point here.
- Official checkpoints
- official_checkpoints = hf_repo identifiers carried by the model itself; artifacts are separate entities pointing here through canonical_id.
- Artifacts
- 1 quantization0 official · 1 third-partySeparate entities (checkpoint · quantization · conversion · packaging) pointing to this model through canonical_id.
- Provider deployments
- 1
- API aliases
- qwen/qwen3-vl-235b-a22b-instructqwen3-vl-235b-a22b-instructqwen3-vl-235b-a22b-reasoningIdentifiers under which providers and evaluators refer to this model.
- Folded evaluation variants
- 1Effort / thinking variants (…-high, …-non-reasoning) are result configurations of this model, not separate models. Their old URLs redirect here.
Openness
Open weights— weights downloadable; 7 dimensions unknown.
Weights downloadable under a permissive or Creative Commons licence allowing commercial use; code or data may be missing.
Weights
Yes
Inference code
—
Training code
—
Training data
—
Dataset
—
Commercial use
—
Redistribution
—
Derivatives
—
dimensions marked null are unknown, not false
Key facts
- Release date
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Status
Source:Artificial AnalysisT2observed 13 h agomedium
- Knowledge cutoff
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Openrouter id
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
Architecture
- Tokenizer
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Hugging Face repo
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
Capabilities
Modalities
- Modalities
- imagetext
- Input
- imagetext
- Output
- text
Capabilities
Tool calling
Yes
OpenRouter public model & pricing listing · T2
Structured output
Yes
OpenRouter public model & pricing listing · T2
Reasoning
No
Artificial Analysis · T2
Vision
Yes
OpenRouter public model & pricing listing · T2
Audio
Unavailable
Fine-tuning available
Unavailable
- Context window
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Max output
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Knowledge cutoff
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
- Tokenizer
Source:OpenRouter public model & pricing listingT2observed 14 h agomedium
Benchmarks28
Compare with another model →Comparable same task and conditions · Partially comparable same task, conditions differ (effort, temperature, judge) · Not comparable different variant or metric
Current rows only, grouped by benchmark → canonical metric → comparability group (task configuration). Effort variants folded into this model appear as rows of the same group. 28 current rows in total. “vs leader” compares with the current leader of the benchmark's primary group only; other groups are not directly comparable. Comparability rules →
Providers & Pricing1
All offers in the price terminal →USD per 1M tokens as published by each provider; native units (per-request fees, flex/priority tiers) are kept verbatim. Rows are append-only — every price change is kept in the history below. Cost of a workload →
Price history
Output price · USD / 1M tokens 1 provider
- OpenRouter
- OpenRouterfirst observed $1.911 Sept 2026
Input price · USD / 1M tokens 1 provider
- OpenRouter
- OpenRouterfirst observed $0.2111 Sept 2026
Lineage
Open in Graph →Versions & Artifacts1
Version history
Context windowfirst observation only
11 Sept 2026current
Knowledge cutofffirst observation only
11 Sept 2026current
Max outputfirst observation only
11 Sept 2026current
Opennessfirst observation only
11 Sept 2026current
Statusfirst observation only
11 Sept 2026current
Each hop is a claim: click a value for its source, tier and observation time. Nothing is overwritten — a new observation closes the previous claim.
Artifacts 1
quantization 1
- amd/Qwen3-VL-235B-A22B-Instruct-MXFP4AMD · BF16/F8_E4M3/U8127.4 GB
Timeline14
Full timeline →Qwen3 VL 235B A22B Instruct scores 11.36% on Terminal-Bench
artificial_analysisQwen3 VL 235B A22B Instruct scores 54.09% on τ²-bench
artificial_analysisQwen3 VL 235B A22B Instruct scores 68.73% on MMMU-Pro
artificial_analysisQwen3 VL 235B A22B Instruct scores 56.46% on IFBench
artificial_analysisQwen3 VL 235B A22B Instruct scores 11.91% on Humanity's Last Exam
artificial_analysisQwen3 VL 235B A22B Instruct scores 77.17% on GPQA Diamond
artificial_analysisQwen3 VL 235B A22B Instruct scores 13.44 on Artificial Analysis Intelligence Index
artificial_analysisQwen3 VL 235B A22B Instruct scores 6.82% on Terminal-Bench
artificial_analysisQwen3 VL 235B A22B Instruct scores 35.09% on τ²-bench
artificial_analysisQwen3 VL 235B A22B Instruct scores 67.57% on MMMU-Pro
artificial_analysisQwen3 VL 235B A22B Instruct scores 42.65% on IFBench
artificial_analysisQwen3 VL 235B A22B Instruct scores 6.63% on Humanity's Last Exam
artificial_analysisQwen3 VL 235B A22B Instruct scores 71.21% on GPQA Diamond
artificial_analysisQwen3 VL 235B A22B Instruct scores 9.94 on Artificial Analysis Intelligence Index
artificial_analysis
Change history
Statusstatus2
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
Provenance
Attributed facts
25
Source tiers
T225
Freshest observation
4 h ago
Conflicts
None
Source documents 2
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.
Data quality (60/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →