Llama Guard 4 12B
meta/llama-guard-4-12bPar Meta · famille: llama · sorti 2025-04-05
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.
Coding5
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability3/10
Agents3
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit0/15
- Provider availability3/10
JSON / structured output29
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency73
- Headline price (log-scaled)73/95
- Has prompt-cache pricing0/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Vision80
- Accepts image input50/50
- Context window (10K → 1M)17/30
- Has published price10/10
- Provider availability3/10
Production-readiness71
- Number of independent providers15/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers6/10
- 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 | $0.99 < $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 | $1.98 < $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 | $0.45 < $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 | $1.62 < $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 | $2.27 < $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 kilo · meta-llama/llama-guard-4-12b
Fournisseur le moins cher : nvidia · Unknown entrée + Unknown sortie
Disponible chez 3 fournisseurs
| Fournisseur | ID modèle fournisseur | Entrée / 1M | Sortie / 1M | Contexte | Publié le |
|---|---|---|---|---|---|
| Groq groq | meta-llama/llama-guard-4-12b | $0.200 | $0.200 | 131K | 2025-04-05 |
| Kilo Gateway kilo | meta-llama/llama-guard-4-12b | $0.180 | $0.180 | 164K | 2025-04-05 |
| Nvidia nvidia | meta/llama-guard-4-12b | Unknown | Unknown | 128K | 2025-04-05 |
Incohérences de données entre fournisseurs
- context_window varies: 128000, 131072, 163840
- 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 Guard 4 12B cost?
Llama Guard 4 12B costs $0.180 per 1M input tokens and $0.180 per 1M output tokens, sourced from kilo. 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 Guard 4 12B?
Llama Guard 4 12B has a context window of 131K tokens, with a max output of 1K tokens per reply. This is the total combined size of prompt + completion.
Does Llama Guard 4 12B support tool calling?
No. Llama Guard 4 12B does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Llama Guard 4 12B support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Llama Guard 4 12B in our data source. Verify with Meta's official documentation before relying on it in production.
Can Llama Guard 4 12B accept image input?
Yes. Llama Guard 4 12B 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 Guard 4 12B open-weight?
Yes. Llama Guard 4 12B'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 Guard 4 12B?
If Llama Guard 4 12B doesn't fit, consider Meta-Llama-3.1-8B-Instruct, Llama-3.3-70B-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 come from the public models.dev API and are normalised into a single canonical model record. 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 :
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.