Official API Model ID (SDK String): meta-llama/Llama-3.2-11B-Vision
Supported SDK & API Protocol: OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Lifecycle & Deprecation Status: Legacy Stable (Upgrade path available in Meta lineup)
Production Rate Limits (RPM & TPM): Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Pricing Tier (>128K / >200K): Free across full context window (only KV-cache VRAM scales)
Prompt Cache Write & TTL Policy: Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use & Browser GUI Agent: API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool Calling: Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Code Sandbox: Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Fine-Tuning & Distillation Support: Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Tokenization Billing: Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Weights Precision & Quantization: BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
Minimum GPU VRAM Requirement: 7 GB VRAM (4-bit Quantized) · 24 GB VRAM (Native BF16)
Supported Inference Engines: vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
System Card & Safety Framework: Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (SOC2 / HIPAA / ZDR): 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control

Engineering & Pricing Workbench

Llama 3.2 11B Vision Cost, Context, Hardware & API Integration

Daily API Requests:500 / day

Avg Input Tokens per Request:4,000 tok

Avg Output Tokens per Request:1,000 tok

Conversation Depth (History Re-billing):1 Turn (Single-Shot)

Prompt Cache Hit Rate:($0/1M cached)50%

Estimated Monthly API Cost (30 Days)

Free ($0)

Daily Cost:: $0.00
Cost per Request:: $0.00000
Effective Input / 1M:: $0.000
Effective Output / 1M:: $0.000

Subscription ($20/mo) vs API

Open-weights or free-tier model: $0 API license cost when self-hosted.

Same Monthly Workload Cost Across Popular Reference Models

Llama 3.2 11B Vision (Active)

$0.00

/ month

Gemini 2.5 Flash

$18.00

/ month

DeepSeek V3

$32.70

/ month

GPT-4o

$300.00

/ month

Claude 3.7 Sonnet

$405.00

/ month

Live Prompt Paste Tokenizer & Cost Counter

Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.

Chars / Words

175 / 24

OpenAI o200k

~46 tok

Claude BPE

~49 tok

Llama / DeepSeek

~51 tok

Hybrid Prompt Routing Cascade Simulator

Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Llama 3.2 11B Vision:

Cascaded Monthly Bill:$13.50/mo

This model is already priced at or below the fast router baseline.

Benchmark Scores & 6-Axis Capability Radar

Benchmark Encyclopedia

Llama 3.2 11B Vision2026 Frontier Median

MMLU-Pro: 68%
HumanEval: 74%
Chatbot Arena Elo: 1285pts

Strengths

premier accessible ouvert modèle de vision de Meta
solide document et graphique compréhension
128K fenêtre de contexte

Limitations & Trade-offs

vision précision inférieur que fermé de pointe modèles like GPT-4o
Does pas process vidéo ou audio

Editorial Assessment

un vital open source jalon bringing natif visuel compréhension au Llama écosystème.

Best for: local document OCR et extraction, image captioning, et ouvert visuel recherche

Compare Llama 3.2 11B Vision

Llama 3.2 11B Vision vs Llama 3.1 8B Llama 3.2 11B Vision vs OLMo 2 13B Llama 3.2 11B Vision vs Llama 3.2 1B Llama 3.2 11B Vision vs Gemini 4 Argon Llama 3.2 11B Vision vs GPT-6 Astra Llama 3.2 11B Vision vs Claude Opus 5.5 Llama 3.2 11B Vision vs GPT-6.1 Sol

Frequently Asked Questions about Llama 3.2 11B Vision

How much does the Llama 3.2 11B Vision API cost per 1M tokens?+

Llama 3.2 11B Vision by Meta is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.

Should I use a $20/month subscription or the Llama 3.2 11B Vision pay-as-you-go API?+

A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.

How many words and pages fit in Llama 3.2 11B Vision's context window?+

Llama 3.2 11B Vision features a 128K tokens context window, which equals approximately 96,000 English words or ~256 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 4,096 tokens.

Can I run Llama 3.2 11B Vision locally on my own GPU (Open Weights)?+

Yes, Llama 3.2 11B Vision (11B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.

Does Meta train on data sent to the Llama 3.2 11B Vision API, and is commercial use allowed?+

100% Isolated when self-hosted on private GPUs or VPC. License tier: Llama Community License (Commercial Permitted). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.

How does Llama 3.2 11B Vision perform on coding and reasoning benchmarks?+

Llama 3.2 11B Vision achieves a composite Intelligence Index of 66/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, 68% on MMLU-Pro, and 1285 pts on Chatbot Arena Elo.

What is Llama 3.2 11B Vision best used for?+

Llama 3.2 11B Vision is best suited for: local document OCR et extraction, image captioning, et ouvert visuel recherche. Assessment: un vital open source jalon bringing natif visuel compréhension au Llama écosystème.

Meta Family Lineage, Routing Tiers & Upgrade Path

Step down to a lighter sibling for high-volume routing or step up to Meta's flagship reasoning tier:

Higher-Intelligence Upgrade2026-05

AI Glossary & Architecture Concepts

View full glossary →

Key machine learning concepts and benchmarks relevant to Llama 3.2 11B Vision:

Jeton (Token)

L'unité textuelle de base traitée par un modèle linguistique, représentant généralement 3 à 4 caractères ou environ 0,75 mot. Par exemple, une phrase courante est découpée en plusieurs jetons.

Benchmark (Banc d’essai)

Une suite de tests standardisés permettant d'évaluer et de comparer objectivement les performances des modèles d'IA sur des tâches ciblées (code avec SWE-bench, mathématiques avec AIME, etc.).

Fenêtre de contexte

Le nombre maximal de jetons qu'un modèle peut traiter simultanément (entrée et sortie comprises). Une fenêtre de 1 000 000 jetons permet d'analyser environ 750 000 mots en une seule requête.

Appel d'outils (Tool Calling)

La capacité d'un LLM à déterminer quand une action externe est requise et à générer des arguments structurés pour invoquer des API, calculatrices ou bases de données avant de poursuivre.

Protocole de contexte de modèle (MCP)

Un standard ouvert créé par Anthropic permettant aux agents d'IA de se connecter de façon uniforme aux systèmes de fichiers locaux, aux environnements de développement et aux API tierces.

Modèle vision-langage (VLM)

Un réseau neuronal multimodal conçu pour analyser, raisonner et générer conjointement des données visuelles (images, schémas, interfaces) et du langage naturel au sein d'une même structure.

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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.

Sources: Meta AI Llama 3.2 Official Release↗

Llama 3.2 11B Vision

Technical Specifications

Data Privacy, Licensing & Deployment Governance

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