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Llama 3.3 70B Instruct

Up to 30%

Llama 3.3 70B Instruct brings Meta conversational intelligence to LLM.API so teams can ship assistants and agents without juggling multiple vendor SDKs.

Start Using API

What is Llama 3.3 70B Instruct?

Llama 3.3 70B Instruct is a Meta chat model exposed on LLM.API under id `llama-3.3-70b-instruct`. llama-3.3-70b-instruct provided by nebius, novita. It is wired for API access through LLM.API with OpenAI-compatible patterns. Teams use it when they need reliable text generation with text inputs and text outputs over an OpenAI-compatible API.

Providers

LLM.API routes Llama 3.3 70B Instruct to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput
nebius$0.13 in
$0.4 out
novita$0.135 in
$0.4 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
nebius30% offin $130; out $400 per 1M tokens131K tokenstools, streaming, JSON, structured
novita30% offin $135; out $400 per 1M tokens131K tokenstools, streaming

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test Llama 3.3 70B Instruct right here — free to start.

Llama 3.3 70B Instruct
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call Llama 3.3 70B Instruct through the OpenAI-compatible API — POST /v1/chat/completions.

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="llama-3.3-70b-instruct",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)
{
  "model": "llama-3.3-70b-instruct",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

5 Core Capabilities

  • Multi-step reasoning

    Breaks down complex problems into intermediate steps before answering. Reflects Meta positioning for this endpoint.

  • Tool-ready dialogue

    Works well in agent loops that call functions, browsers, or retrieval APIs.

  • Instruction following

    Follows detailed system and user instructions with strong adherence to format and tone. Tuned to how teams typically call Llama 3.3 70B Instruct.

  • Analytical writing

    Produces clear analyses, comparisons, and decision memos from messy source material. Relevant for `llama-3.3-70b-instruct` workloads on LLM.API.

  • Code assistance

    Helps write, explain, refactor, and debug application code across common languages. Tuned to how teams typically call Llama 3.3 70B Instruct.

6 Most Valuable Use Cases

  • Meeting-note cleanup and action-item generation with Llama 3.3 70B Instruct
  • Data extraction into JSON for downstream systems
  • Customer support copilots that draft accurate, on-brand replies with Llama 3.3 70B Instruct
  • Internal knowledge assistants grounded with your retrieval layer
  • Product analytics narration and anomaly explanations with Llama 3.3 70B Instruct
  • Research synthesis across long documents and tickets

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Llama 3.3 70B Instruct on LLM.API?

  • Unified AI Routing

    Reach Llama 3.3 70B Instruct and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Llama 3.3 70B Instruct.

  • Reliability Layer

    Production: Retry and route across configured providers when a single upstream blips.

  • Observability

    Practical: Trace prompts, tokens, and errors for Llama 3.3 70B Instruct alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap Llama 3.3 70B Instruct for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You need a general-purpose text model for assistants, agents, or content workflows (Llama 3.3 70B Instruct)
  • You want OpenAI-compatible chat completions through a single LLM.API key (Llama 3.3 70B Instruct)
  • You need provider failover options exposed for this model id (Llama 3.3 70B Instruct)
  • Your prompts benefit from the model's family strengths (reasoning, speed, or cost) (Llama 3.3 70B Instruct)

Avoid if...

  • You need guaranteed real-time hard latency SLAs without benchmarking the provider
  • You need pure embedding, OCR, or media generation instead of chat
  • Your use case depends on unpublished proprietary benchmarks not listed here
  • You require on-prem only deployment with no cloud inference

COMMUNITY

What developers say about Llama models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Llama 3.3 70B Instruct.

  • Independent testers were underwhelmed by Llama 4 coding performance relative to the launch messaging around its very long context window.
  • The architecture shift to mixture-of-experts and native multimodality is seen as the real story for teams self-hosting open weights.
  • Most reviewers position Llama as an integration and deployment choice rather than a benchmark leader.

SOURCES

Frequently Asked Questions

  • What is the context length for Llama 3.3 70B Instruct?

    Reported context for Llama 3.3 70B Instruct is 131K tokens. Always verify the active provider row if multiple providers are listed.

  • What modalities does Llama 3.3 70B Instruct support?

    Llama 3.3 70B Instruct accepts text and produces text according to its architecture metadata on LLM.API.

  • What are limitations of Llama 3.3 70B Instruct?

    Like other API models, Llama 3.3 70B Instruct can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • Where is the canonical page for Llama 3.3 70B Instruct?

    https://llmapi.ai/models/meta-llama-llama-3-3-70b-instruct/

  • When should I choose Llama 3.3 70B Instruct?

    You want OpenAI-compatible chat completions through a single LLM.API key — especially when you specifically need Llama 3.3 70B Instruct.

  • Can I use tools or structured outputs with Llama 3.3 70B Instruct?

    Tool and/or structured-output flags appear on one or more providers for this model—confirm in the providers table.

  • How do I call Llama 3.3 70B Instruct via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "llama-3.3-70b-instruct" and your LLM.API key. See the code snippet on this page.

  • Is Llama 3.3 70B Instruct a chat model?

    Yes—Llama 3.3 70B Instruct is exposed as a chat/completions-style endpoint on LLM.API.

  • Does Llama 3.3 70B Instruct support streaming?

    Yes—at least one listed provider advertises streaming.

  • What is Llama 3.3 70B Instruct?

    llama-3.3-70b-instruct provided by nebius, novita. It is wired for API access through LLM.API with OpenAI-compatible patterns. On LLM.API it is addressed as `llama-3.3-70b-instruct`.

  • Which providers serve Llama 3.3 70B Instruct?

    LLM.API currently lists: nebius, novita. Availability can vary by region and account.

  • How is Llama 3.3 70B Instruct priced on LLM.API?

    Listed pricing metadata shows: In $0.13 / 1M tokens · Out $0.4 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

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