Llama 3.2 11B Vision Instruct
meta/llama-3-2-11b-vision-instructPar Meta · famille: llama · sorti 2024-09-25 · fin de connaissance: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacités
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding6
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability4/10
Agents19
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit15/15
- Provider availability4/10
JSON / structured output30
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency86
- Headline price (log-scaled)86/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Vision81
- Accepts image input50/50
- Context window (10K → 1M)17/30
- Has published price10/10
- Provider availability4/10
Production-readiness74
- Number of independent providers20/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers4/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.30 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $0.60 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.14 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $0.49 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $0.69 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Détail des tarifs
Tarif recommandé de inference · meta/llama-3.2-11b-vision-instruct
Fournisseur le moins cher : nvidia · Unknown entrée + Unknown sortie
Disponible chez 4 fournisseurs
| Fournisseur | ID modèle fournisseur | Entrée / 1M | Sortie / 1M | Contexte | Publié le |
|---|---|---|---|---|---|
| Cloudflare Workers AI cloudflare-workers-ai | @cf/meta/llama-3.2-11b-vision-instruct | $0.049 | $0.676 | 128K | 2024-09-25 |
| Nvidia nvidia | meta/llama-3.2-11b-vision-instruct | Unknown | Unknown | 128K | 2024-09-18 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3.2-11b-vision-instruct | $0.049 | $0.680 | 128K | 2025-04-03 |
| Inference inference | meta/llama-3.2-11b-vision-instruct | $0.055 | $0.055 | 16K | 2025-01-01 |
Incohérences de données entre fournisseurs
- context_window varies: 128000, 16000
- release_date varies (span 197d): 2024-09-18, 2024-09-25, 2025-01-01, 2025-04-03
- modalities varies across offerings
Les fournisseurs rapportent des valeurs différentes pour ce modèle. Les infos clés ci-dessus utilisent un fournisseur représentatif ; voir le tableau pour le détail par fournisseur.
Frequently asked questions
How much does Llama 3.2 11B Vision Instruct cost?
Llama 3.2 11B Vision Instruct costs $0.055 per 1M input tokens and $0.055 per 1M output tokens, sourced from inference. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Llama 3.2 11B Vision Instruct?
Llama 3.2 11B Vision Instruct has a context window of 128K tokens, with a max output of 128K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 3.2 11B Vision Instruct support tool calling?
No. Llama 3.2 11B Vision Instruct does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Llama 3.2 11B Vision Instruct support structured output / JSON mode?
No. Llama 3.2 11B Vision Instruct does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.
Can Llama 3.2 11B Vision Instruct accept image input?
Yes. Llama 3.2 11B Vision Instruct accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Meta's docs for the exact rule.
Is Llama 3.2 11B Vision Instruct open-weight?
Yes. Llama 3.2 11B Vision Instruct's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Llama 3.2 11B Vision Instruct?
If Llama 3.2 11B Vision Instruct doesn't fit, consider Llama-3.3-70B-Instruct, Llama 3.1 8B Instruct, Llama 4 Maverick 17B 128E Instruct FP8. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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Capability lists this model is in
Dernière mise à jour :
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.