llama-3-2-1b-instruct
Llama 3.2 1B is a 1-billion-parameter model from Meta for efficient language tasks such as summarization, dialogue and multilingual text analysis; its small size suits low-resource deployments.
Precio: Entrada: 6.48 créditos / 1 M de tokens (≈ $0.03); salida: 48.24 créditos / 1 M de tokens (≈ $0.24).
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Llama 3.2 1B Instruct API on ModelFold
Llama 3.2 1B is a 1-billion-parameter model from Meta for efficient language tasks such as summarization, dialogue and multilingual text analysis; its small size suits low-resource deployments. Try Llama 3.2 1B Instruct in the ModelFold Playground, then call it from your own code through an OpenAI-compatible chat completions API.
What Is the Llama 3.2 1B Instruct API
Llama 3.2 1B Instruct at a glance
Llama 3.2 1B Instruct is a chat model from Meta. It accepts text as input and returns text, with a context window of up to 60K tokens.
Use it without hosting anything
On ModelFold you call Llama 3.2 1B Instruct with a single API key — no model weights to download and no inference servers to run. The same key also works for the other chat, image, video and audio models on ModelFold.
Core Capabilities of Llama 3.2 1B Instruct
Up to 60K tokens of context
Keep long conversations, large documents and sizeable code in a single request, so the model can reason over the full material instead of fragments.
How to Use Llama 3.2 1B Instruct on ModelFold
- 1
Step 1: Try it in the Playground
Open the Playground at the top of this page, type a prompt and see how Llama 3.2 1B Instruct responds before writing any code.
- 2
Step 2: Create an API key
Sign in to ModelFold and create an API key in the console. Keep it secret and send it as a Bearer token.
- 3
Step 3: Send a request
Call POST https://modelfold.ai/v1/chat/completions with "model": "llama-3-2-1b-instruct" and your messages. Existing OpenAI-compatible SDKs work by changing the base URL and key.
- ✓
Step 4: Ship and monitor
Integrate the response into your product and check token usage and spend for every call in the console logs.
What You Can Build with Llama 3.2 1B Instruct
High-volume pipelines
Run classification, extraction, rewriting and batch jobs at scale where cost per request matters.
Low-latency experiences
Power chat interfaces, autocomplete and interactive tools where users expect fast responses.
Multilingual products
Serve users in many languages with translation, localized support replies and cross-language search.
Frequently Asked Questions
What is Llama 3.2 1B Instruct?⌄
Llama 3.2 1B is a 1-billion-parameter model from Meta for efficient language tasks such as summarization, dialogue and multilingual text analysis; its small size suits low-resource deployments.
Which inputs does Llama 3.2 1B Instruct support?⌄
Llama 3.2 1B Instruct accepts text and returns text. Through ModelFold you send text messages; documents attached in the Playground are converted to text.
How long is the context window of Llama 3.2 1B Instruct?⌄
Up to 60K tokens, shared between your input and the model’s output.
Does Llama 3.2 1B Instruct support function calling?⌄
Function calling is not listed for Llama 3.2 1B Instruct; requests that include tools will be rejected. Use plain prompts or JSON output instead.
How is Llama 3.2 1B Instruct billed on ModelFold?⌄
Pay as you go by tokens: input and output tokens are charged at the per-million-token prices shown on this page. There is no monthly fee, and failed requests are not charged.
How do I call Llama 3.2 1B Instruct through the API?⌄
Send a POST request to https://modelfold.ai/v1/chat/completions with "model": "llama-3-2-1b-instruct", using your ModelFold API key as the Bearer token.
| Entrada | 6.48 créditos 1M tokens≈ $0.03 |
| Salida | 48.24 créditos 1M tokens≈ $0.24 |
Se cobra por token, con tarifas separadas para entrada y salida. Los tokens de razonamiento cuentan como salida. El entorno de pruebas aplica exactamente el mismo cálculo.
Leer la documentación de la API →AI Agents · Claude Code / Cursor / Trae →curl https://modelfold.ai/v1/chat/completions \
-H "Authorization: Bearer $MODELFOLD_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"llama-3-2-1b-instruct","messages":[{"role":"user","content":"Hello"}]}'