Llama 3.2 3b Instruct
meta/llama-3-2-3b-instruct出品方： Meta · 系列: llama · 发布 2024-09-18 · 知识截止: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清单
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.
Coding10
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability8/10
Agents13
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability8/10
JSON / structured output20
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume20/20
Cost efficiency100
- Headline price (log-scaled)95/95
- Has prompt-cache pricing5/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Production-readiness94
- Number of independent providers40/40
- 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 | $0.06 < $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. |
定价详情
推荐定价来自 chutes · unsloth/Llama-3.2-3B-Instruct
最便宜的渠道： nvidia · Unknown 输入 + Unknown 输出
在 8 家渠道可用
| 服务商 | 服务商模型 ID | 输入 / 1M | 输出 / 1M | 上下文 | 发布日期 |
|---|---|---|---|---|---|
| NanoGPT nano-gpt | meta-llama/llama-3.2-3b-instruct | $0.031 | $0.049 | 131K | 2024-09-25 |
| NovitaAI novita-ai | meta-llama/llama-3.2-3b-instruct | $0.030 | $0.050 | 33K | 2024-09-18 |
| Chutes chutes | unsloth/Llama-3.2-3B-Instruct | $0.010 | $0.014 | 16K | 2025-02-12 |
| Kilo Gateway kilo | meta-llama/llama-3.2-3b-instruct | $0.051 | $0.340 | 80K | 2024-09-18 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3.2-3b-instruct | $0.051 | $0.340 | 128K | 2025-04-03 |
| Nvidia nvidia | meta/llama-3.2-3b-instruct | Unknown | Unknown | 33K | 2024-09-18 |
| Inference inference | meta/llama-3.2-3b-instruct | $0.020 | $0.020 | 16K | 2025-01-01 |
| LLM Gateway llmgateway | llama-3.2-3b-instruct | $0.030 | $0.050 | 33K | 2024-09-18 |
各渠道数据存在不一致
- context_window varies: 128000, 131072, 16000, 16384, 32768, 80000
- release_date varies (span 197d): 2024-09-18, 2024-09-25, 2025-01-01, 2025-02-12, 2025-04-03
- modalities varies across offerings
各服务商对此模型的报告值存在差异。上方「核心数据」使用代表性服务商的值；逐项请以下表为准。
Frequently asked questions
How much does Llama 3.2 3b Instruct cost?
Llama 3.2 3b Instruct costs $0.010 per 1M input tokens and $0.014 per 1M output tokens, sourced from chutes. 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 3b Instruct?
Llama 3.2 3b Instruct has a context window of 131K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 3.2 3b Instruct support tool calling?
No. Llama 3.2 3b 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 3b Instruct support structured output / JSON mode?
No. Llama 3.2 3b 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 3b Instruct accept image input?
No. Llama 3.2 3b Instruct only accepts text, pdf as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama 3.2 3b Instruct open-weight?
No. Llama 3.2 3b Instruct is a proprietary model — only Meta (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Llama 3.2 3b Instruct?
If Llama 3.2 3b 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.
Explore more
More Meta models
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- Llama-3.3-70B-Instruct$0.05 in / $0.23 out
- 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
最近更新：
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.