The go-to vision model when budget is the top constraint and good-enough accuracy is acceptable.
52
Coding
60
Writing
55
Research
65
Images
92
Value
60
Long Context
Use this when
Budget-conscious developers who need basic vision capabilities without paying premium multimodal prices.
Skip this if
You need precise OCR, complex chart interpretation, or visual reasoning that requires flagship-level accuracy — the quality gap versus GPT-4o is significant on hard vision benchmarks.
Pricing
$0.34/1M in
$0.34/1M out
↑41%since May 2026
Context
131k tokens
Speed
Fast
Available via multiple inference providers including Together AI, Fireworks, and OpenRouter. As an open-weight model, it can also be self-hosted for even lower marginal costs at scale. Part of Meta's Llama 3.2 family which also includes a 90B vision variant for heavier workloads.
Exceptional price at $0.049/1M tokens for both input and output — roughly 40x cheaper than GPT-4o for vision tasks
Open-weight model allows self-hosting and fine-tuning for custom applications
Solid image understanding for a model of its size, handling charts, diagrams, and photos competently
128K context window is generous for a budget-tier model
Weaknesses
Vision quality noticeably lags behind GPT-4o, Claude Sonnet 4.6, and Gemini 3.1 Pro on complex visual reasoning tasks
11B parameter count limits nuanced reasoning, multi-step logic, and sophisticated code generation compared to flagship models
Struggles with dense text extraction from images and fine-grained visual detail recognition
Real-world use cases
What people actually use Llama 3.2 11B Vision Instruct for.
Batch-processing thousands of product images to generate alt-text or category labels at minimal cost
Extracting structured data from simple forms or receipts in a high-volume document pipeline
Prototyping a vision-enabled chatbot before committing to a more expensive frontier model
How Llama 3.2 11B Vision Instruct compares
The nearest models people weigh against it, and what actually separates them.
vs Llama 3.1 70B Instruct — Against Llama 3.1 70B Instruct (Meta), Llama 3.2 11B Vision Instruct runs about 14% cheaper per token. Take Llama 3.2 11B Vision Instruct unless you specifically need what Llama 3.1 70B Instruct does better.
vs Llama 4 Maverick — Against Llama 4 Maverick (Meta), Llama 3.2 11B Vision Instruct runs about 69% cheaper per token and gives up 2x on context. Take Llama 3.2 11B Vision Instruct unless you specifically need what Llama 4 Maverick does better.
vs Mistral Medium 3.1 — Against Mistral Medium 3.1 (Mistral), Llama 3.2 11B Vision Instruct runs about 71% cheaper per token. Take Llama 3.2 11B Vision Instruct unless you specifically need what Mistral Medium 3.1 does better.
Price History
Llama 3.2 11B Vision Instruct pricing over time
↑41% since May 31
90 data points · tracked daily since May 31, 2026
Ready to try it?
Start using Llama 3.2 11B Vision Instruct
Budget-conscious developers who need basic vision capabilities without paying premium multimodal prices.. Start free — no card required.
Meta's Llama 3.1 70B Instruct is a open-weight large language model with 70 billion parameters, fine-tuned for instruction following across coding, reasoning, and general-purpose tasks. It offers a strong balance of capability and cost at $0.40/1M tokens for both input and output.
Verdict
The go-to budget open-weight model for teams who need solid LLM capability without frontier model pricing.
Quality score
65%
Pricing
$0.40/1M in
$0.40/1M out
Speed
Fast
4/5 speed
Context
131k tokens
Pricing shown is via third-party API providers (e.g., OpenRouter, Together AI) — costs may vary. Meta releases Llama 3.1 weights publicly, enabling self-hosting at even lower cost. Not available directly from Meta as a hosted API.
Mistral Medium 3.1 is a multimodal mid-tier model from Mistral that supersedes Mistral Large 2, offering vision capabilities alongside strong text performance at a significantly reduced price point. It targets the sweet spot between budget models and expensive flagships, with a 128K context window and competitive multilingual support.
Verdict
The best Mistral model for budget-conscious builders who still need multimodal capability and solid multilingual output.
Quality score
70%
Pricing
$0.40/1M in
$2.00/1M out
Speed
Fast
4/5 speed
Context
131k tokens
Officially supersedes Mistral Large 2, representing a generational shift in Mistral's lineup toward multimodal capability at lower cost tiers. Available via Mistral API and select cloud providers. No function calling limitations noted at this tier.
BudgetMultimodalMultilingualMid-tierVision
Best for
Cost-sensitive teams needing solid coding, instruction-following, and basic vision tasks without paying flagship prices.
Meta: Llama 3.2 11B Vision Instruct — added to UseRightAI
Meta: Llama 3.2 11B Vision Instruct (Meta) is now indexed. The go-to vision model when budget is the top constraint and good-enough accuracy is acceptable.
Llama 3.2 11B Vision Instruct costs $0.345 per million input tokens and $0.345 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $4.14 at list price, before any batch or caching discounts.
What is Llama 3.2 11B Vision Instruct best for?
Llama 3.2 11B Vision Instruct is best for budget-conscious developers who need basic vision capabilities without paying premium multimodal prices.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and fast speed.
When should I avoid Llama 3.2 11B Vision Instruct?
You need precise OCR, complex chart interpretation, or visual reasoning that requires flagship-level accuracy — the quality gap versus GPT-4o is significant on hard vision benchmarks.
What is a cheaper alternative to Llama 3.2 11B Vision Instruct?
Mistral Small 3.1 (Mistral) at $0.10/1M/1M input against Llama 3.2 11B Vision Instruct's $0.34/1M/1M — roughly 42% less per token all in. Ultra-cheap multimodal model for massive-volume, low-complexity pipelines. Compare it first if Llama 3.2 11B Vision Instruct's pricing is the thing stopping you.
What is a faster alternative to Llama 3.2 11B Vision Instruct?
Llama 3.1 70B Instruct — fast against Llama 3.2 11B Vision Instruct's fast, with 131k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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