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ModelCap

Qwen3 VL 30B A3B Instruct

Official publisher releaseOpen weights6.9d #177 → #170

Qwen·#170 of 352 on the current board·cheaper than #163 Command A

Qwen3 VL 30B A3B Instruct is a language model from Qwen. It ranks #170 of 352 on ModelCap's current board, with an Index score of 39.1, modeled from the publisher's launch results against measured models. Its score range of 19.4–58.9 spans positions #89–#289. It is listed at $0.15 per million input tokens and $0.60 per million output tokens across 3 providers. It reads up to 262K tokens of context, and its weights are openly downloadable.

Figures as of 6 Nov 2026, 20:32 UTC · download the public dataset · methodology

ModelCap Index · Current board
39.1#170
Modeled from the publisher's launch results against measured models · range 19.4–58.9
Market Gravity
35.4
Secondary market signal
Input
$0.15
per 1M tokens
Output
$0.60
per 1M tokens
Context
262K
tokens
Providers
3
endpoints

Providers

Uptime measured over the last 30 minutes
ProviderInputOutputContextQuantUptime
DeepInfrafp8Cheapest$0.15$0.60262Kfp8100.0%
Novitabf16$0.20$0.70131Kbf16100.0%
SiliconFlowfp8$0.29$1.00262Kfp8—

Hugging Face Inference Providers

Exact repository identity

Provider economics and telemetry published by the Hugging Face Router. These are operational signals, not benchmark results.

ProviderInputOutputContextTTFTThroughputFeatures
deepinfra
live
$0.15$0.60262K409 ms30.9 tok/stools · structured
novita
live
$0.20$0.70131K832 ms45.4 tok/stools · structured
featherless-ai
live
——————

Reported evaluation leads (6)

Not independent measurement

Structured rows reported in this exact model repository at the pinned revision below. They are the publisher talking about itself: never measured evidence, never a score input on their own. Rows marked as used were laddered against independently measured peers inside the launch-card channel of this model's modeled placement, with the replay-measured optimism haircut applied; the others played no part.

Dataset / taskMetricValueRevisionProvenance
MBZUAI/longshot-bench
overall
—30.81
Unpinned
not published
Reported
Not used in launch estimate
MBZUAI/longshot-bench
core_perception
—27.18
Unpinned
not published
Reported
Not used in launch estimate
MBZUAI/longshot-bench
reasoning
—33.52
Unpinned
not published
Reported
Not used in launch estimate
MBZUAI/longshot-bench
information
—29.29
Unpinned
not published
Reported
Not used in launch estimate
MBZUAI/longshot-bench
multimodal
—33.23
Unpinned
not published
Reported
Not used in launch estimate
VCLab-PolyU/OmniCompass
OmniCompass-VL
—23.6
Unpinned
not published
Reported
Not used in launch estimate

ModelCap Index · Current board

Methodology
39.1
Modeled from the publisher's launch results against measured models
Current board
#170 of 352
Score range
19.4–58.9
Rank range
#89–#289
Technical details
Public rank basis
Modeled · peer benchmarks (architecture)
Evidence label
Modeled · peer benchmarks · 4 reported rows against measured corpus ladders (58%); publisher-corpus-prior over 210 held-out anchors (42%); shrunk 8.5 up toward the measured corpus; held just below qwen/qwen3-vl-32b-instruct, whose own published results lead on 5 of 7 benchmarks both report
Index support
20.1% · limited
Identity binding
Exact catalogue product · qwen/qwen/qwen3-vl-30b-a3b-instruct · aggregates disclosed benchmark configurations · endpoint configuration not claimed · artifact metadata Qwen/Qwen3-VL-30B-A3B-Instruct@9c4b90e1e4ba969fd3b5378b57d966d725f1b86c (not the evaluation revision)
Index observations used
0
Qualified Index evidence point
39.1
How this position was produced · fused launch placement

No general-family board lists this model yet. Its point estimate is the precision-weighted mean of every channel that spoke for it; each channel’s share is the weight it carried. Independent measurements replace this placement as boards list the model.

Fused estimate
39.2 ± 15.4
Independent share
0%
Shrink toward corpus
+8.5
Optimism haircut
None applied
Method
precision-weighted-v1
Channels fused into this model’s launch placement
ChannelEstimateShare
Corpus ladder · not independent
4 reported rows against measured corpus ladders
30.6 ± 20.258%
Publisher corpus prior · not independent
publisher-corpus-prior over 210 held-out anchors
51.0 ± 23.842%
Bayesian Evidence Score · measured evidence input only

BES normalizes admitted public benchmark evidence for measured Index rows. Its legacy score and observables prior do not define the public language rank.

Observed capability
—
Evidence status
No admitted measured evidence
General preference
—
Coding
—
Agents & tools
—
Reasoning
—
Evidence breadth
0%
Evidence coverage
0%
Market and catalogue signals · secondary, never the language Index rank
Usage (OpenRouter popularity)
14.7
Liquidity (providers × uptime)
65.7
Open reach (HF downloads)
65.0
Surface (context / tools / modalities)
65.3
Freshness
22.1
  • · OpenRouter popularity #144
  • · 3 live providers
  • · HF 30d downloads 444454
  • · Catalogue: context 262144, tools, modalities Text/Image
  • · First seen 2025-10-06

These adoption and deployment observations remain context only. They do not change this model's ModelCap Index score or rank.

The bounded 0–100 capability score blends each source’s competitive placement with its published achievement, then combines capability families. Evidence breadth adds a modest uncertainty adjustment; price and popularity are not part of benchmark-led rank.

Experimental capability estimate

Estimate policy
33.2
19.1–47.4 uncertainty interval
Excluded from rank
Confidence
78%
Training support
221 models · 129 lineages
Out-of-domain check
in domain
Feature coverage
100%
Lineage-held-out error
8.3 MAE

Experimental estimate. This layer predicts from admitted evidence and safe metadata only. It never enters ModelCap Score or rank.

Deployment readiness

Metadata index
74.2
partial · 76% metadata coverage
Not capability
Artifact reproducibility
85.0
Access & legal clarity
100.0
Deployability
95.6
Evaluation provenance
0.0

Not yet published: the base model it derives from, admitted benchmark results, results across several capability areas, results from more than one source, a confident match to its benchmark entries, pinned evaluation dataset versions.

A metadata completeness and deployability index—not a safety certification, quality grade, or production-readiness claim.

Market activity

Venue coverage
Market Gravity
35.4
OpenRouter weekly popularity
#144 of 339

Market Gravity uses OpenRouter’s full-catalogue popularity order. Sparse Vercel top-ten observations appear only when published and do not affect the score.

Arena preference

No matched Arena rating

This model is visible because it is available in the live catalogue, but it has no trustworthy community result yet. ModelCap does not infer a quality score from price, features, or market activity.

Market Gravity breakdown

Usage55%
14.7

OpenRouter weekly popularity across the full model catalogue.

Liquidity25%
65.7

Independent providers versus the model's open or closed cohort, weighted by uptime.

Open reach15%
65.0

Hugging Face 30-day downloads for open models; neutral for closed models.

Freshness5%
22.1

Time since first public availability, on a six-month half-life.

Specification

Model ID
qwen/qwen3-vl-30b-a3b-instruct
Context window
262K tokens
Declared output cap
16K tokens

OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. OpenRouter metadata

Inputs
Text, Image
Outputs
Text
Tokenizer
Qwen3
Cached input tokens
Not offered
First seen on OpenRouter
6 Oct 2025
Knowledge cutoff
2025-03-31

Capabilities

  • Not supported: Reasoning
  • Supported: Tool use
  • Supported: Structured output
  • Supported: Response format
  • Not supported: Moderated

Weights & access

Open weights. Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI.

Access
Open weights
Licence
apache-2.0
Pinned revision
9c4b90e1e4ba
Parameters
31.1B
Architecture
Qwen3VLMoeForConditionalGeneration
Model type
qwen3_vl_moe
Hugging Face downloads (30d)
444K
Downloads (all time)
18.5M
Likes
627
Repository updated
26 Nov 2025, 13:18 UTC

Alternatives to Qwen3 VL 30B A3B Instruct, by the numbers

Ranked neighbours

Models within 3 places of #170 on the ModelCap Index.

Cheaper at a similar or higher Index

Index score within 3 points of, or above, this model's, with a lower listed blended price (3:1 input:output).

Other ranked Qwen models

The same publisher's other current models with a public Index position.

Qwen3 VL 30B A3B Instruct: common questions

How does Qwen3 VL 30B A3B Instruct rank among AI models?

Qwen3 VL 30B A3B Instruct holds ModelCap Index position #170 of 352 ranked language models as of 6 November 2026, with an Index score of 39.1 (interval 19.4–58.9); evidence: modeled · peer benchmarks. The Index combines public benchmark boards with published uncertainty; the methodology page explains the weighting.

How much does Qwen3 VL 30B A3B Instruct cost per 1M tokens?

Qwen3 VL 30B A3B Instruct is listed at $0.15 per 1M input tokens and $0.60 per 1M output tokens as of 6 November 2026 across 3 providers. Prices come from the OpenRouter catalogue and re-observe on every refresh. For 1,000 requests using 2,000 input and 500 output tokens each, the listed rates imply $0.60 total (2M input + 0.5M output). This excludes caching, batch discounts, prompt-length tiers, tool charges and retries; verify the chosen endpoint's rate and limits before budgeting.

What is the context window of Qwen3 VL 30B A3B Instruct?

Qwen3 VL 30B A3B Instruct has a published context window of 262K tokens (262,144). The snapshot lists a 16,384 token catalogue-declared route output cap. OpenRouter catalogue or provider-endpoint declaration. Limits can vary by provider and prompt length; a model-wide maximum has not been independently verified. The providers table lists each endpoint's context limit.

Is Qwen3 VL 30B A3B Instruct open-weight?

Open weights (apache-2.0). Downloadable weights are published under an OSI or free-culture license in the current repository metadata. This describes weight availability, not whether the full training stack qualifies as Open Source AI. The repository is Qwen/Qwen3-VL-30B-A3B-Instruct on Hugging Face.

Which API providers serve Qwen3 VL 30B A3B Instruct?

3 providers serve Qwen3 VL 30B A3B Instruct through OpenRouter as of 6 November 2026: DeepInfra, Novita and SiliconFlow. Each provider's price, context limit, quantization and measured uptime are in the providers table above.

Which benchmarks has Qwen3 VL 30B A3B Instruct been evaluated on?

Qwen3 VL 30B A3B Instruct's Index position rests on the evidence listed on this page; it has no result on the public boards ModelCap tracks as separate leaderboards as of 6 November 2026. The evidence panel names each source and its publication date.

When was Qwen3 VL 30B A3B Instruct released?

Qwen3 VL 30B A3B Instruct first appeared in the catalogue on 6 Oct 2025, with a published knowledge cutoff of 2025-03-31. It is the current version in its family. Rank and price movements since then are recorded on the site's changes feed.