Llama 3.2 3B Instruct
meta/llama-3-2-3b-instructVon Meta · Familie: llama · veröffentlicht 2024-09-18 · Wissensstand: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Fähigkeiten
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.
Coding9
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability9/10
Agents24
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit15/15
- Provider availability9/10
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-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. |
Preis-Details
Empfohlene Preise von inference · meta/llama-3.2-3b-instruct
Günstigster Anbieter: nvidia · Unknown Eingabe + Unknown Ausgabe
Bei 9 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| OpenRouter openrouter | meta-llama/llama-3.2-3b-instruct | $0.051 | $0.335 | 80K | 2024-09-25 |
| NovitaAI novita-ai | meta-llama/llama-3.2-3b-instruct | $0.030 | $0.050 | 33K | 2024-09-18 |
| Cloudflare Workers AI cloudflare-workers-ai | @cf/meta/llama-3.2-3b-instruct | $0.051 | $0.335 | 80K | 2024-09-25 |
| LLM Gateway llmgateway | llama-3.2-3b-instruct | $0.030 | $0.050 | 33K | 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 |
| Kilo Gateway kilo | meta-llama/llama-3.2-3b-instruct | $0.051 | $0.340 | 80K | 2024-09-18 |
| NanoGPT nano-gpt | meta-llama/llama-3.2-3b-instruct | $0.031 | $0.049 | 131K | 2024-09-25 |
Datenunterschiede zwischen Anbietern
- context_window varies: 128000, 131072, 16000, 32768, 80000
- release_date varies (span 197d): 2024-09-18, 2024-09-25, 2025-01-01, 2025-04-03
- modalities varies across offerings
Anbieter melden unterschiedliche Werte für dieses Modell. Die Schnellinfos oben nutzen den repräsentativen Anbieter; pro Anbieter siehe Tabelle.
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 80K tokens, with a max output of 80K 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 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, Meta-Llama-3.1-8B-Instruct, Llama 4 Scout 17B 16E Instruct. 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
Zuletzt aktualisiert:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.