Llama 3.3 70B Instruct APIOPEN WEIGHTS131K context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Llama 3.3 70B Instruct?

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...

Llama 3.3 70B Instruct on the release timeline

Llama 3.3 70B Instruct pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Llama 3.3 70B Instruct is cheaper than 84% of the 309 models in the catalog. It is served by 12 providers; the best combined rate at our last snapshot was ≈ $0.10 / $0.32 per 1M via DeepInfra, with upstream quantizations bf16, fp16, fp8.

What Llama 3.3 70B Instruct costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

Llama 3.3 70B Instruct vs alternatives

Specs

How to call Llama 3.3 70B Instruct

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="meta-llama/llama-3.3-70b-instruct",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"meta-llama/llama-3.3-70b-instruct","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "meta-llama/llama-3.3-70b-instruct",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Llama 3.3 70B Instruct and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Llama 3.3 70B Instruct

• Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Llama 3.3 70B Instruct follows concrete instructions better than abstract ones.
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let Llama 3.3 70B Instruct emit tool calls, execute them, and feed results back for the next turn.
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.
• Cheap enough to batch. Great for high-volume classification, extraction and drafting — template the prompt, batch requests, and validate outputs programmatically.

Tips are derived from Llama 3.3 70B Instruct's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Llama 3.3 70B Instruct

Popular open-source projects for running and building with Llama 3.3 70B Instruct — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to Llama 3.3 70B Instruct

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → meta-llama/llama-3.3-70b-instruct. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Llama 3.3 70B Instruct ships open weights (meta-llama/Llama-3.3-70B-Instruct, ~761.5K downloads and 2.9K likes in the last 30 days), with community quantizations (bf16, fp16, fp8) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 12 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff; because Llama 3.3 70B Instruct is served by 12 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions