Meta • LLM
Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and...
Context Window
131K tokens
Input Price/1M
$0.24
Output Price/1M
$0.24
Parameters
—
Speed
77 tok/s
Latency (TTFT)
481ms
Max Output
16K tokens
Llama 3.2 11B Vision Instruct results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 1.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| SciCode | 11.0 | 100.0 | — |
| LiveCodeBench | 11.0 | 100.0 | — |
| AA Coding Index | 4.2 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 46.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 12.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AIME 2025 | 2.0 | 100.0 | — |
| AA Math Index | 1.7 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 8.7 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| IFBench | 30.0 | 100.0 | — |
| GPQA Diamond | 22.0 | 100.0 | — |
| HLE | 5.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 15.0 | 100.0 | — |
Llama 3.2 11B Vision Instruct is an AI model developed by Meta, classified as a large language model (LLM). It is a multimodal model, capable of processing text, images, and potentially other media types. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 131K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Llama 3.2 11B Vision Instruct is usage-based, priced at $0.245/1M input tokens and $0.245/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Llama 3.2 11B Vision Instruct was evaluated on 13 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Llama 3.2 11B Vision Instruct is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), image and visual document analysis (OCR, diagrams, screenshots), multimodal processing combining text and images, high-volume chatbots and automated support, text generation, summarization, translation, and general assistance.
In the 2026 AI model ecosystem, Llama 3.2 11B Vision Instruct competes directly with similarly capable models. As an open source model, it competes with Qwen (Alibaba), Mistral, and DeepSeek, as well as proprietary models like GPT, Claude, and Gemini. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and...
Llama 3.2 11B Vision Instruct costs $0.245/1M input tokens and $0.245/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, Llama 3.2 11B Vision Instruct scored: Terminal-Bench Hard: 1/100, SciCode: 11/100, LiveCodeBench: 11/100. See the full table above for a detailed comparison.
Yes, Llama 3.2 11B Vision Instruct is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.
Llama 3.2 11B Vision Instruct excels at multimodal tasks including text and vision. With its large context window, it handles long documents, codebases, and extended conversations.
Last updated: June 01, 2026 • View methodology →