Llama 3.3 70B

Llama 3.3 70B is a large language model from Meta, first released on 6 December 2024. It is open-source (open-weights) and sits in the frontier, meta, open weights, enterprise, and code categories of our catalog. Open-weights 70B that matches GPT-4o on most benchmarks. This page covers Llama 3.3 70B pricing, benchmarks, API limits, speed, modalities, best use cases, and how it compares with similar models — so you can decide whether it belongs in your stack in 2026.

As a language model, Llama 3.3 70B is evaluated on reasoning quality, coding ability, latency, context window size, and dollars-per-million-tokens. The context window is 128k tokens (about 96k words), which determines how much prompt, document, and conversation history you can send in one request. At a typical 3:1 input-to-output mix, the blended API price is about $0.27 per 1M tokens. On our intelligence index it ranks #14 of 22 language models we track with a score of 66/100 (strong production-tier).

Teams usually shortlist Llama 3.3 70B when they need a dependable Meta option for production chat, agents, retrieval-augmented generation, or coding copilots. Common fits include self-hosting, eu data residency, and cost-sensitive workloads. Reviewers consistently call out open weights, fast on groq / cerebras, and cheap as standout strengths. Trade-offs to weigh include no vision and smaller context than peers. The sections below break down pricing tables, benchmark charts, token limits, input/output modalities, and head-to-head comparisons so long-tail queries — from “Llama 3.3 70B API pricing” to “Llama 3.3 70B vs DeepSeek R1” — are answered on this page.

If you are migrating from an older Meta model or switching labs entirely, treat this page as a decision brief: skim the overview stats, confirm API pricing fits your volume, check whether the context window covers your longest documents, then validate quality on a golden set of prompts. Benchmarks and charts help shortlist; your own evals decide. We refresh catalog numbers periodically (last update 2026-06) so figures stay useful through the year.

Public benchmark scores help compare Llama 3.3 70B with other LLMs on knowledge, graduate-level science, competition math, and coding. Reported figures in our catalog include MMLU 86, MMLU Pro 68.9, GPQA 50.5, MATH 77, and HumanEval 88.4. These are not a substitute for evals on your own prompts, but they are useful for shortlisting.

Our intelligence index (66/100) normalizes those benchmarks into a single strong production-tier score so you can scan the leaderboard quickly. The performance chart below shows each benchmark against the current catalog leader.

Llama 3.3 70B offers a 128k-token context window — roughly about 96k words of English text. Everything in a single API call counts against that budget: system instructions, chat history, retrieved documents, tool schemas, and the model’s reply. Exceeding the window truncates or errors depending on the provider.

Large windows help with long PDFs, multi-file code reviews, and multi-hour agent traces, but bigger contexts also cost more tokens and can add latency. Prefer retrieval that stuffs only relevant chunks, summarize old turns, and reserve headroom for up to 4k output tokens.

Token limits define how much Llama 3.3 70B can read and write per request. Total context is capped at 128k tokens. Maximum completion length is 4k tokens — even if context remains, the model stops generating beyond that ceiling unless you continue in a follow-up call. Providers may also enforce organization-level rate limits (RPM/TPM) separate from these per-request caps.

Practical tip: set max_tokens intentionally. Leaving it unbounded wastes budget on verbose answers; setting it too low truncates JSON or code. For structured outputs, prefer schemas/tool calls and keep completions tight.

The charts on this page visualize Llama 3.3 70B against catalog peers. Benchmark bars show academic scores versus the current leader; the similar-models comparison table plots intelligence, speed, and blended price so you can see trade-offs at a glance. Use them to answer “is Llama 3.3 70B fast enough?” and “is the quality jump worth the premium?” without opening a spreadsheet.

Best use cases for Llama 3.3 70B follow from its strengths, price point, and modality support. Match the model to the job: frontier reasoning for hard planning, fast/cheap tiers for classification, image/video/speech specialists for media pipelines.

Based on catalog notes, Llama 3.3 70B is a particularly strong fit for self-hosting, eu data residency, and cost-sensitive workloads. Validate with a short bake-off on your real prompts before a full cutover.

Anti-patterns: do not use a frontier-priced model like a generic classifier if a smaller model scores within a point on your eval; do not stuff entire corpora into context when retrieval would be cheaper; and do not skip structured outputs if you plan to parse Llama 3.3 70B responses in code.