Llama 3.2 3B Instruct
meta/llama-3-2-3b-instruct提供： Meta · ファミリー: llama · リリース 2024-09-25 · 知識カットオフ: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
機能一覧
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.
Coding32
- Tool calling0/40
- Structured output20/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability10/10
Agents50
- Tool calling0/35
- Structured output25/25
- Reasoning0/15
- Output token limit15/15
- Provider availability10/10
JSON / structured output80
- Structured output / JSON mode50/50
- 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 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 | $0.11 < $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 | $0.22 < $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 | $0.05 < $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 | $0.18 < $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 | $0.25 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
料金詳細
推奨料金 (提供元): inference · meta/llama-3.2-3b-instruct
最安プロバイダー: nvidia · Unknown 入力 + Unknown 出力
10 か所で利用可能
| プロバイダー | プロバイダーモデルID | 入力 / 1M | 出力 / 1M | コンテキスト | リリース日 |
|---|---|---|---|---|---|
| OpenRouter openrouter | meta-llama/llama-3.2-3b-instruct | $0.050 | $0.330 | 131K | 2024-09-25 |
| Cloudflare Workers AI cloudflare-workers-ai | @cf/meta/llama-3.2-3b-instruct | $0.051 | $0.335 | 80K | 2024-09-25 |
| Nvidia nvidia | meta/llama-3.2-3b-instruct | Unknown | Unknown | 33K | 2024-09-18 |
| NanoGPT nano-gpt | meta-llama/llama-3.2-3b-instruct | $0.031 | $0.049 | 131K | 2024-01-01 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-3.2-3b-instruct | $0.051 | $0.340 | 128K | 2025-04-03 |
| Inference inference | meta/llama-3.2-3b-instruct | $0.020 | $0.020 | 16K | 2025-01-01 |
| NovitaAI novita-ai | meta-llama/llama-3.2-3b-instruct | $0.030 | $0.050 | 33K | 2024-09-18 |
| LLM Gateway llmgateway | llama-3.2-3b-instruct | $0.030 | $0.050 | 33K | 2024-09-18 |
| Kilo Gateway kilo | meta-llama/llama-3.2-3b-instruct | $0.050 | $0.330 | 131K | 2024-09-25 |
| Pioneer pioneer | meta-llama/Llama-3.2-3B-Instruct | $0.100 | $0.335 | 131K | 2024-08-31 |
プロバイダー間でデータに差異
- context_window varies: 128000, 131072, 16000, 32768, 80000
- release_date varies (span 458d): 2024-01-01, 2024-08-31, 2024-09-18, 2024-09-25, 2025-01-01, 2025-04-03
- modalities varies across offerings
プロバイダーごとに本モデルの値が異なります。上部の「主要数値」は代表的プロバイダーを使用しています。詳細は表をご確認ください。
Frequently asked questions
How much does Llama 3.2 3B Instruct cost?
Llama 3.2 3B Instruct costs $0.020 per 1M input tokens and $0.020 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 3B Instruct?
Llama 3.2 3B Instruct has a context window of 131K tokens, with a max output of 131K 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?
Yes. Llama 3.2 3B Instruct supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Can Llama 3.2 3B Instruct accept image input?
No. Llama 3.2 3B 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 3B Instruct open-weight?
Yes. Llama 3.2 3B 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 3B Instruct?
If Llama 3.2 3B 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.
Explore more
More Meta models
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- Llama 3.1 8B Instruct$0.02 in / $0.03 out
- Llama 4 Maverick 17B 128E Instruct FP8$0.14 in / $0.59 out
- Llama 4 Scout 17B 16E Instruct$0.10 in / $0.30 out
- Llama 3.2 1B Instruct$0.01 in / $0.01 out
Capability lists this model is in
最終更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.