Home
Contact usLog inSign up

llama-3.3-70b-versatile

Groq's deployment of Meta's Llama 3.3 70B, optimized for fast inference with strong multilingual reasoning, coding, and tool-use capabilities at 128k context.

Start buildingGET Available Models

about

MiniMax's Lightning Attention mechanism enables a 205K-token context window while routing each token through 8 of its experts, keeping 10B of 230B total parameters active per forward pass. It scores 80.2% on SWE-Bench Verified and completes benchmark runs 37% faster than the prior M2.1 release, at $0.30/$1.20 per million tokens, making it one of the cheapest models at that performance tier.

Licensellama3.3
Context window(in thousands)131,072

Use cases for llama-3.3-70b-versatile

  1. Real-time coding assistants: Groq's LPU serves this model at 276 tokens per second, enabling interactive code generation and refactoring with sub-second response times.
  2. Multilingual enterprise chat: Supporting 8 languages with 92.1 on IFEval for instruction-following, it handles complex multi-turn conversations across regional teams without quality degradation.
  3. Cost-effective 405B replacement: Matching Llama 3.1 405B quality on most benchmarks at a fraction of the compute, it runs production workloads that previously required 405B-class infrastructure.

Quality

Arena Elo1318
MMLU66.6
MT BenchN/A

Llama 3.3 70B scores 86.0% on MMLU (0-shot CoT) and 92.1 on IFEval, matching or exceeding Llama 3.1 70B Instruct (86.0% MMLU) on the same sheet while surpassing GPT-4o (84.6 IFEval) on instruction-following. At 70B parameters it delivers benchmark results previously requiring the 405B variant, making the larger model largely redundant for most workloads.

llama-4-17b-128e-instruct

1327

gpt-4-turbo-preview

1324

llama-3.3-70b-versatile

1318

Llama-3.3-70B-Instruct

1318

GPT-4 Omni

1316

pricing

The cost of running Llama 3.3 70B with Telnyx Inference is $0.0006 per 1,000 tokens. Analyzing 1,000,000 customer chats at 1,000 tokens each would cost $600, delivering 405B-class quality at the 70B price tier.

What's Twitter saying?

  • Developers praise Llama 3.3 70B's superior instruction-following, with Christopher Penn noting it outperforms Llama 3.1 405B in tests, scoring 99 vs. 87-88.
  • Reviewers highlight its efficiency and cost-effectiveness, achieving 276 tokens/sec inference speed (25% faster than Llama 3.1 70B) at $0.10/million input tokens.
  • A YouTube reviewer calls it a "pretty good" junior engineer-like model that follows directions well but requires precise prompting to avoid processing issues.

Explore Our LLM Library

Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project.

Organizationdeepseek-ai
Model NameDeepSeek-R1-Distill-Qwen-14B
Taskstext generation
Languages SupportedEnglish
Context Length43,000
Parameters14.8B
Model Tiermedium
Licensedeepseek

TRY IT OUT

Chat with an LLM

Powered by our own GPU infrastructure, select a large language model, add a prompt, and chat away. For unlimited chats, sign up for a free account on our Mission Control Portal here.

HOW IT WORKS

Selecting LLMs for Voice AI

GET Available Models
RESOURCES

Get started

Check out our helpful tools to help get you started.

  • Icon Resources ebook

    Test in the portal

    Easily browse and select your preferred model in the AI Playground.

    Test today
  • Icon Resources Docs

    Explore the docs

    Don’t wait to scale, start today with our public API endpoints.

    Get started
  • Icon Resources Article

    Stay up to date

    Keep an eye on our AI changelog so you don't miss a beat.

    See updates

Sign up and start building

Sign upContact sales

faqs

Is the Llama 3.3 70B good?

Llama 3.3 70B delivers performance competitive with much larger models on reasoning, coding, and multilingual tasks. It is one of the strongest open-weight models in its class, frequently matching or exceeding proprietary alternatives on standard benchmarks.

What GPU is needed for Llama 3.3 70B?

Running Llama 3.3 70B at full precision requires approximately 140GB of VRAM, typically achieved with two A100 80GB GPUs. With 4-bit quantization, it can run on a single GPU with 48GB+ VRAM, or through hosted inference platforms that handle GPU provisioning.

What is the Llama 3.3 70B used for?

Llama 3.3 70B excels at conversational AI, code generation, document analysis, and complex reasoning tasks. Its instruction-tuned variant supports a 128K context window, making it well suited for long-document processing and multi-turn dialogue.

Is Llama 3 70B better than DeepSeek 70B?

Llama 3.3 70B and DeepSeek models trade wins across different benchmarks. Llama 3.3 generally leads on multilingual tasks and instruction following, while DeepSeek models are competitive on math and coding. The choice often depends on deployment infrastructure and specific task requirements.

Is Llama 3.3 70B good at coding?

Llama 3.3 70B performs well on coding benchmarks, approaching the performance of Llama 3.1 405B on many tasks. It handles code generation, debugging, and explanation effectively, making it a practical choice for developer-facing applications at lower compute cost than larger models.

What is the context window for Llama 3.3 70B?

Llama 3.3 70B supports a 128K token context window, matching the Llama 3.1 series for long-document processing. This enables tasks like full-codebase analysis and lengthy conversation history without truncation.

Can I use Llama 3.3 70B for free?

Llama 3.3 70B is released under Meta's Llama Community License, which is free for most commercial use. Weights are available on Hugging Face, and hosted inference is available through various providers.

Llama 3.3 70B Versatile: Advanced Reasoning Model for Complex Problem-Solving