The go-to vision model when budget is the top constraint and good-enough accuracy is acceptable.
Budget-conscious developers who need basic vision capabilities without paying premium multimodal prices.
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
Vision quality noticeably lags behind GPT-4o, Claude Sonnet 4.6, and Gemini 3.1 Pro on complex visual reasoning tasks
See what Meta: Llama 3.2 11B Vision Instruct actually costs at your usage level
Based on Meta: Llama 3.2 11B Vision Instruct API pricing: $0.245/1M input · $0.245/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
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→0% since May 9
4 data points · tracked daily since May 9, 2026
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Flexible open-weight model for teams that want control, portability, and solid general-purpose performance.
Pricing moves, ranking shifts, and capability updates.
Meta: Llama 3.2 11B Vision Instruct output pricing changed from $0.05/1M to $0.24/1M (↑ more expensive, 400% increase).
View modelMeta: Llama 3.2 11B Vision Instruct input pricing changed from $0.05/1M to $0.24/1M (��� more expensive, 400% increase).
View modelMeta: 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.
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.
Meta: Llama 3.1 8B Instruct is the lower-cost option to compare first when you want a similar workflow fit with less token spend.
Llama 4 Maverick is the better pick when response time matters more than maximum depth or premium quality.
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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.
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
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.
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.
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.
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