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ModelDeprecatedOpen weightsIdentity probable

Qwen3 VL 8B Instruct

Qwenfamily · Qwen3huggingface.co/Qwen/Qwen3-VL-8B-Instruct

Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...

Open in Graph
data quality72

Updated 23 min ago · first seen 11 Sept 2026

model_01M294WWAD8V5ZJY2EYQWD4RF8

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-8b-instructqwen3-vl-8b-instructqwen3-vl-8b-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 under Apache-2.0; commercial use allowed; redistribution allowed; derivatives allowed; 4 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

    Yes

  • Redistribution

    Yes

  • Derivatives

    Yes

Licence: Apache License 2.0 (permissive · SPDX Apache-2.0 · stated as “apache-2.0”)

dimensions marked null are unknown, not false

Key facts

Release date

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 11 h agomedium

Status

Source:Artificial AnalysisT2observed 14 h agomedium

Openrouter id

Source:OpenRouter public model & pricing listingT2observed 16 h agomedium

Architecture

Architecture

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium

Model type

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium

Parameters

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 16 h agomedium

Tokenizer

Source:OpenRouter public model & pricing listingT2observed 16 h agomedium

Weights dtype

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium

File size

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium

Library name

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 15 h agomedium

Pipeline tag

Source:Hugging Face Hub (public pages, model cards, papers)T2observed 16 h agomedium

Hugging Face repo

Source:OpenRouter public model & pricing listingT2observed 16 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 13 h agomedium

Max output

Source:OpenRouter public model & pricing listingT2observed 16 h agomedium

Tokenizer

Source:OpenRouter public model & pricing listingT2observed 16 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

Benchmark results grouped by comparability group
Benchmark · groupBest scoreTrustConfigurationResultsvs leaderEvaluatedSource
Terminal-Benchagentic · accuracy · variant=hard · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisvarianthardreasoningonconditions differ across rows → partially comparable4−62.1 ptvs gpt-5.6-solobs. 12 Sept 2026artificialanalysis.aiT2
τ²-benchagentic · pass^1 · variant=Telecom · evaluator=Artificial AnalysisIndependentevaluatorArtificial AnalysisvariantTelecomreasoningoffconditions differ across rows → partially comparable2non-primary groupobs. 12 Sept 2026artificialanalysis.aiT2
τ²-benchagentic · pass^1 · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysis2−69.9 ptvs Z.ai GLM 5.2obs. 11 Sept 2026artificialanalysis.aiT2
Artificial Analysis Intelligence Indexcomposite · indexIndependentreasoningonversion4.3conditions differ across rows → partially comparable4−45.2vs Claude Fable 5.1obs. 12 Sept 2026artificialanalysis.aiT2
IFBenchinstruction-following · accuracy · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisreasoningonconditions differ across rows → partially comparable4−43.5 ptvs Grok 4.3obs. 12 Sept 2026artificialanalysis.aiT2
Humanity's Last Examknowledge · accuracy · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisreasoningonconditions differ across rows → partially comparable4−55.3 ptvs Claude Fable 5.1obs. 12 Sept 2026artificialanalysis.aiT2
MMMU-Promultimodal · accuracy · evaluator=Artificial AnalysisIndependentevaluatorArtificial Analysisreasoningonconditions differ across rows → partially comparable4−30.2 ptvs gpt-6-astraobs. 12 Sept 2026artificialanalysis.aiT2
GPQA Diamondreasoning · accuracy · variant=Diamond · evaluator=Artificial AnalysisIndependentevaluatorArtificial AnalysisvariantDiamondreasoningonconditions differ across rows → partially comparable2non-primary groupobs. 12 Sept 2026artificialanalysis.aiT2
GPQA Diamondreasoning · accuracy · variant=GPQA Diamond · evaluator=Artificial AnalysisIndependentevaluatorArtificial AnalysisvariantGPQA Diamondreasoningonconditions differ across rows → partially comparable2−38.4 ptvs gpt-6-astraobs. 11 Sept 2026artificialanalysis.aiT2

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 →
Provider deployments, cheapest output first
ProviderContextInput / 1MOutput / 1MStatusObservedSource
OpenRoutercheapest outputqwen/qwen3-vl-8b-instruct131.1Kout 32.8Kactive15 min agosince 11 Sept 2026openrouter.aiT2

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

Step lines per provider; amber markers are recorded changes. Click a marker or a row for the evidence behind that price.

Output price · USD / 1M tokens 1 provider

Output price history of Qwen3 VL 8B Instruct$0$0.20$0.40$0.60Sept 26Sept 26Sept 26Sept 26Sept 26OpenRouter: first observed → $0.455 · 11 Sept 2026
  • OpenRouter
  • OpenRouterfirst observed $0.45511 Sept 2026

Input price · USD / 1M tokens 1 provider

Input price history of Qwen3 VL 8B Instruct$0$0.05$0.10$0.15Sept 26Sept 26Sept 26Sept 26Sept 26OpenRouter: first observed → $0.117 · 11 Sept 2026
  • OpenRouter
  • OpenRouterfirst observed $0.11711 Sept 2026

Hardware fit37

Estimated

37 of 37 device × quantization combinations fit.

Run locally: your machine →
Estimated hardware fit
HardwareQuantizationDevice memoryEst. memoryFits
Apple M3 Ultra4bit—5.5 GB est.Yes
Mac Studio (Apple M5 Ultra)4bit—5.5 GB est.Yes
Apple M2 Ultra4bit—5.5 GB est.Yes
Apple M1 Ultra4bit—5.5 GB est.Yes
Apple M3 Max4bit—5.5 GB est.Yes
Apple M4 Max4bit—5.5 GB est.Yes
Mac Studio (Apple M5 Max)4bit—5.5 GB est.Yes
MacBook Pro (Apple M5 Max)4bit—5.5 GB est.Yes
Apple M2 Max4bit—5.5 GB est.Yes
Apple M1 Max4bit—5.5 GB est.Yes
Apple M4 Pro4bit—5.5 GB est.Yes
Mac mini (Apple M5 Pro)4bit—5.5 GB est.Yes
MacBook Pro (Apple M5 Pro)4bit—5.5 GB est.Yes
Apple M3 Pro4bit—5.5 GB est.Yes
Apple M1 Pro4bit—5.5 GB est.Yes
Apple M2 Pro4bit—5.5 GB est.Yes
Apple M44bit—5.5 GB est.Yes
iMac (Apple M4)4bit—5.5 GB est.Yes
Mac mini (Apple M6)4bit—5.5 GB est.Yes
MacBook Air (Apple M5)4bit—5.5 GB est.Yes
MacBook Pro (Apple M5)4bit—5.5 GB est.Yes
Apple M24bit—5.5 GB est.Yes
Apple M34bit—5.5 GB est.Yes
Apple M14bit—5.5 GB est.Yes
NVIDIA GeForce RTX 30904bit24 GB5.5 GB est.Yes
NVIDIA GeForce RTX 40904bit24 GB5.5 GB est.Yes
NVIDIA GeForce RTX 50904bit32 GB5.5 GB est.Yes
NVIDIA A100 80GB4bit80 GB5.5 GB est.Yes
NVIDIA H100 SXM4bit80 GB5.5 GB est.Yes
NVIDIA H100 NVL4bit94 GB5.5 GB est.Yes
NVIDIA DGX Spark4bit128 GB5.5 GB est.Yes
NVIDIA H2004bit141 GB5.5 GB est.Yes
NVIDIA H200 NVL4bit141 GB5.5 GB est.Yes
NVIDIA B2004bit180 GB5.5 GB est.Yes
AMD Instinct MI300X4bit192 GB5.5 GB est.Yes
AMD Instinct MI325X4bit256 GB5.5 GB est.Yes
NVIDIA DGX B2004bit1,440 GB5.5 GB est.Yes
Assumptions (7)
  • Estimated, not measured: weights = parameters × bytes/param × 1.15 runtime overhead (or the observed artifact file size when one is recorded).
  • bytes/param: 4bit = 0.5, 8bit = 1.0, fp16 = 2.0 (uniform quantization, no per-layer exceptions).
  • KV cache: 2 × layers × kv_heads × head_dim × 2 bytes × context × batch when the architecture is known; otherwise 0.5 GB per 8 192 tokens (× batch), independent of architecture (GQA/MLA models need less).
  • A model 'fits' when the estimate is at most the device memory minus 2 GB reserved for the OS and framework.
  • Mixture-of-experts models are estimated on total parameters (all experts must be resident); active parameters are ignored.
  • Device memory uses the largest configuration when several are listed (e.g. Apple silicon tiers).
  • Multi-GPU: device memories are summed; interconnect bandwidth, tensor-parallel replication and pipeline bubbles are not modelled.

Lineage

Open in Graph →
Explicit derived_from / fine_tuned_from / distilled_from relations stated by sources; artifacts collapsed by kind.
DESCENDANTS 0 · ARTIFACTS1 quantizationartifacts · collapsed1 quantization — artifacts · collapsedQwen3 VL 8B Instruct8.77B params · this modelQwen3 VL 8B Instruct — 8.77B params · this model

    Versions & Artifacts1

    Version history

    Context window2 changes

    11 Sept 2026→11 Sept 2026→12 Sept 2026current

    Licensefirst observation only

    11 Sept 2026current

    Max outputfirst observation only

    11 Sept 2026current

    Opennessfirst observation only

    11 Sept 2026current

    Parametersfirst 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

    Papers4

    Timeline17

    Full timeline →

    Change history

    Temporal, append-only claims: a new observation closes the previous claim instead of overwriting it. Rewind the record with the as-of picker.
    2 claims · 1 propertiesShow all properties

    Reasoningreasoning2

    Claim history for Reasoning
    ValueValid from → toStatusSourceConfidenceExtractor
    No → currentcurrentArtificial AnalysisT2mediumdeterministic
    Yes → currentcurrentArtificial AnalysisT2mediumdeterministic

    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

    46

    Source tiers

    T246

    Freshest observation

    1 h ago

    Conflicts

    None

    Source documents 6

    Source documents
    SourceDocumentTypeTierLast observedSnapshots
    OpenRouter public model & pricing listingopenrouter.ai/api/v1/models catalogueT2· Quality secondary15 min ago12
    Hugging Face Hub (public pages, model cards, papers)huggingface.co/Qwen/Qwen3-VL-8B-Instruct/raw/main/README.md model_cardT2· Quality secondary23 min ago1
    Hugging Face Hub (public pages, model cards, papers)huggingface.co/Qwen/Qwen3-VL-8B-Instruct model_pageT2· Quality secondary1 h ago6
    Hugging Face Hub (public pages, model cards, papers)huggingface.co/amd/Qwen3-VL-8B-Instruct-w8a8-llmcompressor model_pageT2· Quality secondary2 h ago7
    Hugging Face Hub (public pages, model cards, papers)huggingface.co/models?author=Qwen&p=0&sort=downloads listingT2· Quality secondary2 h ago8
    Artificial Analysisartificialanalysis.ai/leaderboards/models leaderboardT2· Quality secondary6 h ago2

    Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.

    Data quality (72/100) measures how well AI Atlas knows this entity — completeness, primary-source ratio, freshness, conflicts — never how good the model is. Methodology →