Llama 3.2 1B Instruct
meta/llama-3-2-1b-instructPor Meta · família: llama · lançado 2024-09-18 · data de conhecimento: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
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)0/20
- Provider availability5/10
Agents10
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability5/10
JSON / structured output30
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency95
- Headline price (log-scaled)95/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness74
- Number of independent providers25/40
- Has published per-token price20/20
- Context window ≥ 8K8/15
- 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 | $0.06 < $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 | $0.11 < $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 | $0.03 < $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 | $0.09 < $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 | $0.13 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalhes de preço
Preço recomendado de inference · meta/llama-3.2-1b-instruct
Provedor mais barato: nvidia · Unknown entrada + Unknown saída
Disponível em 5 provedores
| Provedor | ID do modelo do provedor | Entrada / 1M | Saída / 1M | Contexto | Lançado |
|---|---|---|---|---|---|
| Chutes chutes | unsloth/Llama-3.2-1B-Instruct | $0.010 | $0.011 | 16K | 2026-01-27 |
| Kilo Gateway kilo | meta-llama/llama-3.2-1b-instruct | $0.027 | $0.200 | 60K | 2024-09-18 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3.2-1b-instruct | $0.027 | $0.200 | 128K | 2025-04-03 |
| Nvidia nvidia | meta/llama-3.2-1b-instruct | Unknown | Unknown | 128K | 2024-09-18 |
| Inference inference | meta/llama-3.2-1b-instruct | $0.010 | $0.010 | 16K | 2025-01-01 |
Inconsistências de dados entre provedores
- context_window varies: 128000, 16000, 16384, 60000
- release_date varies (span 496d): 2024-09-18, 2025-01-01, 2025-04-03, 2026-01-27
Os provedores reportam valores diferentes para este modelo. Os dados rápidos acima usam um provedor representativo; consulte a tabela para detalhes por provedor.
Frequently asked questions
How much does Llama 3.2 1B Instruct cost?
Llama 3.2 1B Instruct costs $0.010 per 1M input tokens and $0.010 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 1B Instruct?
Llama 3.2 1B Instruct has a context window of 16K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 3.2 1B Instruct support tool calling?
No. Llama 3.2 1B 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 1B Instruct support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Llama 3.2 1B Instruct in our data source. Verify with Meta's official documentation before relying on it in production.
Can Llama 3.2 1B Instruct accept image input?
No. Llama 3.2 1B Instruct only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama 3.2 1B Instruct open-weight?
Yes. Llama 3.2 1B 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 1B Instruct?
If Llama 3.2 1B Instruct 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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- Llama 4 Maverick 17B 128E Instruct FP8$0.14 in / $0.59 out
- Llama 4 Scout 17B 16E Instruct$0.08 in / $0.30 out
- Meta-Llama-3.1-70B-Instruct$0.40 in / $0.40 out
Capability lists this model is in
Última atualização:
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.