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Model details

Meta Llama 3.3 70B Instruct

Llama 3.3 70B Instruct is Meta's December 2024 instruction-tuned refresh of the Llama 3.1 70B line, positioned as a workhorse model for complex instruction following rather than a full-scale flagship. Meta describes it as a targeted upgrade that strengthens tool calling, multilingual text handling, mathematical reasoning, and code generation, while keeping the parameter count modest so it can run efficiently on widely available infrastructure. The headline value proposition is that a 70B-parameter model can deliver reasoning and instruction-following quality close to the much larger Llama 3.1 405B, but at a fraction of the serving cost and with noticeably faster response times, which makes it attractive for production assistants that previously would have needed the bigger sibling to feel competitive.

In practical terms, this is a multilingual chat and reasoning model best suited to developer-facing applications such as retrieval-augmented agents, code helpers, and structured-data workflows that lean on its improved tool-calling behavior. Because it is resold through more than twenty cloud providers, teams can choose between fully managed APIs, on-demand deployments on dedicated GPUs, or fine-tuning with low-rank adaptation to specialize the model on domain-specific data. The combination of strong math and coding scores, broad language coverage, and a context window comfortably above one hundred thousand tokens makes it a balanced generalist for enterprise assistants, while its smaller footprint compared with 405B-class alternatives helps keep latency and per-token spend in check for high-traffic services.

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Provider
Helicone
Model key
llama-3.3-70b-instruct
Release date
Dec 6, 2024
Last updated
Dec 6, 2024
Knowledge cutoff
2024-12
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.13
Output token cost
$0.39

Limits

Output tokens
16,400 tokens
Context window
128,000 tokens

Transparent token rates

Compare Meta Llama 3.3 70B Instruct pricing

Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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