MetaLanguage model

Llama-3.2-11B-Vision-Instruct

Llama-3.2-11B-Vision-Instruct has no overall index value yet because there are too few independent benchmark results.

Intelligencenot ranked yet

n/aOverall indexhigher is better

SpeedRank 101 of 101

21Tokens per secondhigher is better

Priceno API price

n/aper 1M tokenslower is better

ContextRank 98 of 103

128Ktokens at once≈ 190 A4 pages

PopularityRank 243 of 785

42out of 100same as last month

based on search, usage and attention

Summary

At 21 tokens per second it writes slower than a typical model (87).

Llama 3.2 11B Vision Instruct adds image understanding to Meta’s smaller Llama line and processes text and images in one open checkpoint. That is what distinguishes it from Llama 3.2 3B, which is limited to text and code.

The weights use the Llama 3.2 Community License. The knowledge cutoff is December 2023.

All benchmark results

Every published result with its source, test setup and comparison models.

Show all 50 published benchmark results

Measurements without matching peer values

These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.

CritPt v2: Overall (Show measurement, test conditions, and source)

Source value: 0
Score: 0
Metric: pass@1 accuracy
Unit: %
Benchmark version: v2
Category: reasoning
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: disputed
Retrieved at: 2026-11-02
Methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. The full CritPt v2 run covers 70 questions with five repeats. The official grading server reports pass@1.. Source model ID: artificial-analysis/5fb47ff6-a30e-4c2c-96f2-55e95a13390f/llama-3-2-instruct-11b-vision/llama-3-2-instruct-11b-vision. Source model name: Llama-3.2-11B-Vision-Instruct. Provider reported by source: Meta. Model snapshot: Llama 3.2 Instruct 11B (Vision). Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Python functions, symbolic expressions, and numerical answers; official grading server. Attempts: 70 questions, five repeats per question, pass@1. Reasoning: non-reasoning. Reasoning effort: none. Score display: 0.0%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-02-67d46be20a5b. Source payload hash: 67d46be20a5b412db6cfb60ad190441fcaa5513500b8fd47d033168d0e97b6b2. Parser version: artificial-analysis-rsc-models-v3. Imported at: 2026-11-02. Source cohort: artificial-analysis-critpt-v2:v2:standalone:non-reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. The full CritPt v2 run covers 70 questions with five repeats. The official grading server reports pass@1.. Tool policy: Python functions, symbolic expressions, and numerical answers; official grading server. Attempts: 70 questions, five repeats per question, pass@1. Reasoning: non-reasoning. Reasoning effort: none
Context: This AA source field contains the original CritPt score. The earlier CritPt v2 attribution was incorrect; the value and former label remain as disputed historical evidence.. The source dataset marks this result as disputed. It remains visible as documented evidence, but is not reliable comparative proof.

Source Artificial Analysis

IFBench: Overall (Show measurement, test conditions, and source)

Source value: 30.408163265306097
Score: 30.408163265306097
Metric: IFBench prompt-level loose accuracy (pass@1)
Unit: %
Benchmark version: AA test set: 294 questions
Category: general
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: superseded
Retrieved at: 2026-11-02
Methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Source model ID: artificial-analysis/5fb47ff6-a30e-4c2c-96f2-55e95a13390f/llama-3-2-instruct-11b-vision/llama-3-2-instruct-11b-vision. Source model name: Llama-3.2-11B-Vision-Instruct. Provider reported by source: Meta. Model snapshot: Llama 3.2 Instruct 11B (Vision). Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Reasoning effort: none. Score display: 30.4%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-02-67d46be20a5b. Source payload hash: 67d46be20a5b412db6cfb60ad190441fcaa5513500b8fd47d033168d0e97b6b2. Parser version: artificial-analysis-rsc-models-v3. Imported at: 2026-11-02. Source cohort: artificial-analysis-ifbench:AA test set: 294 questions:standalone:non-reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Reasoning effort: none
Context: The often cited 58 refers to verifiable out-of-domain constraint types, not the number of questions in AA's test run.. This result is retained as historical evidence and is not interchangeable with current results.

Source Artificial Analysis

IFBench: Overall (Show measurement, test conditions, and source)

Source value: 30.408163265306097
Score: 30.408163265306097
Metric: IFBench prompt-level loose accuracy (pass@1)
Unit: %
Benchmark version: AA test set: 294 questions
Category: general
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: active
Retrieved at: 2026-11-07
Methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). This source capture does not provide an average reasoning-token count. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Source model ID: artificial-analysis/5fb47ff6-a30e-4c2c-96f2-55e95a13390f/llama-3-2-instruct-11b-vision/llama-3-2-instruct-11b-vision. Source model name: Llama-3.2-11B-Vision-Instruct. Provider reported by source: Meta. Model snapshot: Llama 3.2 Instruct 11B (Vision). Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Reasoning effort: none. Score display: 30.4%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-07-b96e3ffc8c5b. Source payload hash: b96e3ffc8c5b052aa56f96d05e89d02b8cc234a75085820fca0ca2a39e498274. Parser version: artificial-analysis-rsc-models-v4. Imported at: 2026-11-07. Source cohort: artificial-analysis-ifbench:AA test set: 294 questions:standalone:non-reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). This source capture does not provide an average reasoning-token count. Artificial Analysis runs 294 questions from the IFBench test set with five repeats per question. The official evaluator scores prompt-level accuracy in loose mode as pass@1.. Tool policy: Single-turn answer evaluated with the official IFBench source code. Attempts: pass@1, 5 repeats per question. Reasoning: non-reasoning. Reasoning effort: none
Context: The often cited 58 refers to verifiable out-of-domain constraint types, not the number of questions in AA's test run.

Source Artificial Analysis

MMMU-Pro: Overall (Show measurement, test conditions, and source)

Source value: 29.3063583815029
Score: 29.3063583815029
Metric: pass@1 accuracy
Unit: %
Benchmark version: AA cohort: 1,730 questions
Category: multimodal
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: superseded
Retrieved at: 2026-11-02
Methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. The AA run covers 1,730 questions with ten answer options. Answers are extracted by regex and scored pass@1.. Source model ID: artificial-analysis/5fb47ff6-a30e-4c2c-96f2-55e95a13390f/llama-3-2-instruct-11b-vision/llama-3-2-instruct-11b-vision. Source model name: Llama-3.2-11B-Vision-Instruct. Provider reported by source: Meta. Model snapshot: Llama 3.2 Instruct 11B (Vision). Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Ten-option multiple choice; regex answer extraction. Attempts: pass@1, one run per question. Reasoning: non-reasoning. Reasoning effort: none. Score display: 29.3%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-02-67d46be20a5b. Source payload hash: 67d46be20a5b412db6cfb60ad190441fcaa5513500b8fd47d033168d0e97b6b2. Parser version: artificial-analysis-rsc-models-v3. Imported at: 2026-11-02. Source cohort: artificial-analysis-mmmu-pro:AA cohort: 1,730 questions:standalone:non-reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). AA marks this model variant as non-reasoning and reports 0 reasoning tokens. The AA run covers 1,730 questions with ten answer options. Answers are extracted by regex and scored pass@1.. Tool policy: Ten-option multiple choice; regex answer extraction. Attempts: pass@1, one run per question. Reasoning: non-reasoning. Reasoning effort: none
Context: The benchmark page describes 3,460 questions in the full MMMU-Pro dataset; AA's methodology specifies a 1,730-question run.. This result is retained as historical evidence and is not interchangeable with current results.

Source Artificial Analysis

MMMU-Pro: Overall (Show measurement, test conditions, and source)

Source value: 29.3063583815029
Score: 29.3063583815029
Metric: pass@1 accuracy
Unit: %
Benchmark version: AA cohort: 1,730 questions
Category: multimodal
Task: overall
Task label: Overall
Direction: Higher is better
Source type: independent-evaluator
Evaluator: Artificial Analysis
Status: active
Retrieved at: 2026-11-07
Methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). This source capture does not provide an average reasoning-token count. The AA run covers 1,730 questions with ten answer options. Answers are extracted by regex and scored pass@1.. Source model ID: artificial-analysis/5fb47ff6-a30e-4c2c-96f2-55e95a13390f/llama-3-2-instruct-11b-vision/llama-3-2-instruct-11b-vision. Source model name: Llama-3.2-11B-Vision-Instruct. Provider reported by source: Meta. Model snapshot: Llama 3.2 Instruct 11B (Vision). Evaluator: Artificial Analysis. Dataset type: artificial-analysis-evaluation. Tool policy: Ten-option multiple choice; regex answer extraction. Attempts: pass@1, one run per question. Reasoning: non-reasoning. Reasoning effort: none. Score display: 29.3%. Source ID: artificial-analysis. Evidence capture ID: aa-model-page-2026-11-07-b96e3ffc8c5b. Source payload hash: b96e3ffc8c5b052aa56f96d05e89d02b8cc234a75085820fca0ca2a39e498274. Parser version: artificial-analysis-rsc-models-v4. Imported at: 2026-11-07. Source cohort: artificial-analysis-mmmu-pro:AA cohort: 1,730 questions:standalone:non-reasoning:effort=unspecified:suffix=unspecified
Comparison methodology: Exact AA configuration: Llama 3.2 Instruct 11B (Vision) (release llama-3-2-instruct-11b-vision, row llama-3-2-instruct-11b-vision, UUID 5fb47ff6-a30e-4c2c-96f2-55e95a13390f, reasoning=false, effort=not specified, suffix=not specified). This source capture does not provide an average reasoning-token count. The AA run covers 1,730 questions with ten answer options. Answers are extracted by regex and scored pass@1.. Tool policy: Ten-option multiple choice; regex answer extraction. Attempts: pass@1, one run per question. Reasoning: non-reasoning. Reasoning effort: none
Context: The benchmark page describes 3,460 questions in the full MMMU-Pro dataset; AA's methodology specifies a 1,730-question run.

Source Artificial Analysis

VQAv2 (test) (Show measurement, test conditions, and source)

Source value: 75.2
Score: 75.2
Metric: Accuracy
Unit: %
Benchmark version: test
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.2 Vision model card (commit e2e67cd)
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Llama 3.2 11B Instruct column; 0-shot; Accuracy.
Context: The card does not publish per-row sample counts or confidence intervals.

Source Meta Llama 3.2 Vision model card (commit e2e67cd) (unavailable on November 2, 2026) https://raw.githubusercontent.com/meta-llama/llama-models/e2e67cd8b0feca839666b7082dcc1c4fc0ae11e2/models/llama3_2_vision/MODEL_CARD.md

MMLU (CoT) (Show measurement, test conditions, and source)

Source value: 73
Score: 73
Metric: Macro_avg/acc
Unit: %
Benchmark version: 0-shot CoT
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.2 Vision model card (commit e2e67cd)
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Llama 3.2 11B Instruct column; 0-shot; Macro_avg/acc.
Context: The card does not publish per-row sample counts or confidence intervals.

MATH (CoT) (Show measurement, test conditions, and source)

Source value: 51.9
Score: 51.9
Metric: final_em
Unit: %
Benchmark version: 0-shot CoT
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.2 Vision model card (commit e2e67cd)
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Llama 3.2 11B Instruct column; 0-shot; final_em.
Context: The card does not publish per-row sample counts or confidence intervals.

GPQA (Show measurement, test conditions, and source)

Source value: 32.8
Score: 32.8
Metric: Accuracy
Unit: %
Benchmark version: 0-shot
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.2 Vision model card (commit e2e67cd)
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Llama 3.2 11B Instruct column; 0-shot; Accuracy.
Context: The card does not publish per-row sample counts or confidence intervals.

MGSM (CoT) (Show measurement, test conditions, and source)

Source value: 68.9
Score: 68.9
Metric: em
Unit: %
Benchmark version: 0-shot CoT
Direction: Higher is better
Source type: vendor-reported
Evaluator: Meta Llama 3.2 Vision model card (commit e2e67cd)
Status: active
Retrieved at: 2026-10-17
Methodology: Vendor-reported result. Exact Llama 3.2 11B Instruct column; 0-shot; em.
Context: The card does not publish per-row sample counts or confidence intervals.

VRC-Bench final-answer accuracy (original 11B Vision Instruct baseline) (Show measurement, test conditions, and source)

Source value: 48.4
Score: 48.4
Metric: final-answer accuracy
Unit: %
Category: multimodal
Direction: Higher is better
Source type: independent-evaluator
Evaluator: LlamaV-o1: Rethinking Step-by-step Chain-of-Thought Reasoning in Vision-Language Models
Status: active
Retrieved at: 2026-10-17
Methodology: Independent evaluation. Table 1; original Llama-3.2-11B-Vision-Instruct baseline on VRC-Bench; final-answer accuracy.

Source LlamaV-o1: Rethinking Step-by-step Chain-of-Thought Reasoning in Vision-Language Models

VRC-Bench reasoning-step score (original 11B Vision Instruct baseline) (Show measurement, test conditions, and source)

Source value: 58.37
Score: 58.37
Metric: reasoning-step performance
Unit: %
Category: multimodal
Direction: Higher is better
Source type: independent-evaluator
Evaluator: LlamaV-o1: Rethinking Step-by-step Chain-of-Thought Reasoning in Vision-Language Models
Status: active
Retrieved at: 2026-10-17
Methodology: Independent evaluation. Table 1; original Llama-3.2-11B-Vision-Instruct baseline on VRC-Bench; reasoning-step score.

Source LlamaV-o1: Rethinking Step-by-step Chain-of-Thought Reasoning in Vision-Language Models

Page 1 of 5

Popularity compared

How often the model is searched for, used and mentioned, compared with every model of its category.

Popularity index

The 100 most popular models of this category, 0 to 100, higher is more popular

20 of 101 models

GPT-6 Astra83

Claude Haiku 5.577

GPT-6 Sol73

GPT-573

DeepSeek V4.1 Flash 101769

Kimi K367

MiniCPM-V 4.7 35B-A3B63

GPT-6.1 Sol63

Claude Opus 563

Qwen 3.8 Flash62

Llama-3.2-11B-Vision-Instruct42

DeepSeek V4.1 Flash 1017

MiniCPM-V 4.7 35B-A3B

GPT-6.1 Sol

Claude Opus 5

Qwen 3.8 Flash

Llama-3.2-11B-Vision-Inst…

As of November 2026 · Source: gradually.ai popularity index

Over time

Weekly values from October 18, 2026 to November 7, 2026

Llama-3.2-11B-Vision-InstructK2 Horizon 375B-A23B 42Command R 42DeepSeek V4 Flash 0731 42GLM-4.5V 42

Method changes in the timeline, a jump at these points is no market event:

1November 1, 2026: Value only with search or usage data, 30-day attention

What is gaining popularity right now, across models, vendors, harnesses and AI subscriptions, is collected in All AI trends.

Similar models

More models of the same kind as Llama-3.2-11B-Vision-Instruct, chosen by model line, developer, size, price and release date.

All specifications

Architecture

Parameters: 11 billion; Source: Parameters
Knowledge cutoff: December 2023; Source: Knowledge cutoff

Input and output

Context window: 128,000 tokens; Source: Context window
Maximum output: 4,096 tokens; Source: Maximum output
Inputs: Text, Image; Source: Inputs
Outputs: Text; Source: Outputs
Tool calling: Yes; Source: Tool calling

Watermarking and provenance

AI watermarking refers to invisible signals embedded in the output. Visible marks and provenance metadata are listed separately. Unknown means there is insufficient evidence for that particular field.

AI watermarking: Unknown; no source
Watermarking technique: Not known; no source

Licence and access

Weights: Open weights; Source: Weights
Access: API; Source: Access
License: Llama 3.2 Community; Source: License

Frequently asked questions about Llama-3.2-11B-Vision-Instruct

What is the knowledge cutoff of Llama-3.2-11B-Vision-Instruct?

December 2023, according to Meta. That is 9 months before release.

Stated by Meta

DecFebAprJunAugKnowledge cutoffDecember 2023ReleasedSep 25, 2024

How large is the context window of Llama-3.2-11B-Vision-Instruct?

128,000 tokens. That is how much text Llama-3.2-11B-Vision-Instruct reads at once, about 190 A4 pages.

Page count as a rule of thumb from Artificial Analysis

Context windowTokens the model reads at once · higher is better

GLM-5.1200K

DeepSeek-V3 0324164K

GLM-4.5131K

GPT OSS 120B131K

Llama-3.2-11B-Vision-InstructRank 98 of 103128K

DeepSeek-V3.2128K

DeepSeek-R1128K

How fast is Llama-3.2-11B-Vision-Instruct?

21 tokens per second. That is slower than a typical model at 87.

Measured by Artificial Analysis on Nov 6, 2026. AA 3.0.0, including time to first token, averaged across available workloads and five days.

SpeedTokens per second · higher is better

MiMo V2.6 Flash35

GLM-4.733

Kimi K3.1 Pro33

DeepSeek-V3.233

MiMo-V2.5-Pro33

GLM-4.632

Llama-3.2-11B-Vision-InstructRank 101 of 10121

Can I run Llama-3.2-11B-Vision-Instruct locally?

Yes, the weights are open.

Stated by Meta

Open weights: yes
License: Llama 3.2 Community
Parameters: 11B
active per token: n/a

Sources and data date

Show all 23 sources

Benchmark sources at a glance

Which independent evaluators tested this model. This is coverage, not a quality ranking.

Evaluator	Test series	Recorded active measurements
Artificial Analysis	2	2
LMArena	0	1
Scale AI Evals	0	1

Without vendor claims and historical or disputed results; those stay labelled in the single values. 2 measurements are not yet assigned to a test series.

Type	Evidence and data date
Speed	Median across serving providers, Non-reasoning. AA 3.0.0, including time to first token, averaged across available workloads and five days. Retrieved November 6, 2026. Source: Artificial Analysis · Speed (Artificial Analysis)Speed measurement methodology
Input	Documented modalities. Retrieved October 27, 2026. Source: models.dev catalog · models.dev catalog (retrieved October 27, 2026) · Meta model catalog snapshot · Editorial description reviewed October 2, 2026
Output	Documented modalities. Retrieved October 27, 2026. Source: models.dev catalog · models.dev catalog (retrieved October 27, 2026) · Meta model catalog snapshot · Editorial description reviewed October 2, 2026
Research status	Research date October 15, 2026. 11 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Not published	score-units-and-evaluation-library Note Meta's card omits explicit score units, per-task decoding settings, and exact evaluation library version.
Not published	per-row-sample-count Note The card does not publish per-row sample counts or confidence intervals.
Not published	confidence interval Note No confidence interval is reported for the model's table scores.
Not published	serving route and checkpoint revision Note The paper names the checkpoint but no serving route or weight revision.
Not published	evaluated N Original wording The leaderboard reports preference-vote totals but no fixed task-case N; Votes are not copied to sampleCount.
Unresolved	confidence interval Original wording The Score cell displays ±, but the official policy does not define that notation as a confidence interval; no CI field is populated.
Unresolved	score direction Original wording The captured source does not explicitly state higher-is-better or lower-is-better; higherIsBetter remains null.
Not published	per-model prompts and generation settings Original wording No exact prompt, decoding settings, or serving configuration is published in the captured row.
Additional source	Hugging Face (retrieved October 15, 2026) · Hugging Face, Llama 3.2 11B Vision Instruct · Editorial description reviewed October 2, 2026
Additional source	Artificial Analysis (retrieved November 2, 2026)
Additional source	Artificial Analysis (retrieved November 2, 2026; November 7, 2026)1 2
Additional source	Meta Llama 3.2 Vision model card (commit e2e67cd) (retrieved October 17, 2026) (unavailable on November 2, 2026) https://raw.githubusercontent.com/meta-llama/llama-models/e2e67cd8b0feca839666b7082dcc1c4fc0ae11e2/models/llama3_2_vision/MODEL_CARD.md
Additional source	LlamaV-o1: Rethinking Step-by-step Chain-of-Thought Reasoning in Vision-Language Models (retrieved October 17, 2026)
Additional source	Chat-TS: Enhancing Multi-Modal Reasoning Over Time-Series and Natural Language Data (retrieved October 17, 2026)
Additional source	ACL Anthology, AbjadNLP 2026, KazakhOCR (retrieved October 18, 2026)
Additional source	Arena leaderboard (retrieved October 18, 2026)
Additional source	ACL Anthology paper 2025.acl-long.689 (retrieved October 19, 2026)
Additional source	Scale Labs VISTA leaderboard (retrieved October 23, 2026)
Additional source	Multimodal RewardBench leaderboard (retrieved October 23, 2026)
Additional source	Model metadata (retrieved October 17, 2026)
Additional source	Model metadata (retrieved October 18, 2026)
Additional source	OpenRouter: Popularity index (derived), CC BY 4.0
Additional source	Vercel AI Gateway: Popularity index (derived), CC BY 4.0
Additional source	Ollama: cumulative downloads for the popularity index
Additional source	Replicate: cumulative runs for the popularity index
Additional source	Google Trending Now: limited feed selection for Germany and the US
Additional source	Creative Commons Attribution 4.0