🦙 Meta Llama 4: The Open-Source Challenger That’s Changing the AI Landscape

cnai_admin
Wed, 08 Apr 2026 15:07:45 +0000
Tech

TL;DR

On April 7, 2026, Meta officially launched Llama 4 — a new open-source LLM family featuring a Mixture-of-Experts (MoE) architecture. The Ultra version scores 89.7% on mainstream benchmarks, outperforming GPT-4 (88.5%) with 30% faster inference. With total parameters reaching 1.2 trillion, Llama 4 is set to reshape the open-source ecosystem and intensify competition with Chinese models like DeepSeek and Qwen.

What Is Llama 4?

Llama 4 is Meta’s latest open-source large language model family, unveiled on April 7, 2026. Available in Base and Ultra editions, the series is designed for developers and enterprises worldwide, reinforcing Meta’s commitment to open-source AI.

Key Technical Specifications

Feature | Llama 4 Ultra
Architecture | Mixture-of-Experts (MoE)
Total Parameters | 1.2 trillion
Inference Speed | 30% faster than previous-gen SOTA
Training & Deployment Cost | Significantly reduced

Benchmark Performance: Outperforming GPT-4

Llama 4 Ultra achieves an average score of 89.7% across mainstream benchmarks including MMLU, HumanEval, and GSM8K — surpassing GPT-4 (88.5%) and representing a major leap in open‑source AI capabilities.

Multi-Model Comparison (Key Benchmarks)

Model | MMLU | HumanEval | GSM8K | Average
Llama 4 Ultra | 90.2% | 88.7% | 90.1% | 89.7%
GPT-4 | 88.5% | 85.0% | 87.0% | 86.8%
DeepSeek V3.2 | 89.1% | 89.2% | 89.5% | 89.3%
Claude 3.5 Sonnet | 88.7% | 86.2% | 88.3% | 87.7%

Note: DeepSeek V3.2 benchmarks are estimates based on public performance metrics.

Beyond raw numbers, Llama 4 excels in multi‑turn conversations, logical reasoning, and code generation , with enhanced support for long‑context understanding and complex instruction‑following.

Why the MoE Architecture Matters

The Mixture-of-Experts (MoE) architecture is now standard across top‑tier LLMs, including DeepSeek‑R1, GPT‑5, Qwen‑MoE, and Meta’s Llama 4.

How MoE works:

Large total parameter count but sparse activation per token

Only a subset of “experts” is activated during inference

Dramatically reduces compute and memory costs (60‑80% lower)

For Llama 4 Ultra:
Total parameters: 1.2 trillion | Activated parameters per token: ~17 billion

This means it delivers massive model capacity while maintaining efficient inference — crucial for developers looking to self‑host or deploy at scale.

Open‑Source Impact: Closing the Gap with Proprietary Models

Meta’s commitment to open‑source AI is reshaping the industry. As noted by dev.to (March 2026), the performance gap between open‑source and closed models once measured in years, but is now measured in months.

For Chinese developers and enterprises, Llama 4 offers a powerful open‑source alternative for self‑hosting and custom deployment — particularly for applications requiring full model control and data privacy.

Llama 4 vs. Chinese Models: A New Competitive Dynamic

With Chinese models like DeepSeek and Qwen already dominating global API call volumes (6 of the top 7 spots as of April 2026), Llama 4 introduces a new variable:

Dimension | Llama 4 Ultra | DeepSeek V3.2 | Qwen3.6 Plus
Architecture | MoE | MoE | MoE (235B/22B)
Total Parameters | 1.2T | 685B | 235B
Inference Cost | Lower (MoE sparse activation) | Ultra-low ($0.28/M input) | Low ($2/M input)
License | Open-source (Meta) | MIT (most permissive) | Apache 2.0
Primary Strength | Multimodal + reasoning | Coding + math + cost | Chinese understanding + multilingual

Llama 4’s release raises key questions:

Can Meta’s open‑source push compete with Chinese models’ cost leadership?

Will developers choose Llama 4 for its multimodal capabilities, or stick with DeepSeek/Qwen for their extreme cost‑efficiency and permissive licenses?

Discussion Points (Join the Conversation)

1. Open‑source vs. cost efficiency : Llama 4 offers cutting‑edge open‑source performance, but DeepSeek’s MIT license and ultra‑low pricing remain unmatched. Which matters more for your projects?

2. MoE as the new standard : With MoE now adopted across Meta, DeepSeek, and Qwen, how will this architecture shape the next generation of AI applications?

3. The China‑US open‑source race : How will Llama 4 impact adoption of Chinese models among global developers? Will it accelerate or fragment the open‑source ecosystem?

Resources

Original announcement: 4月8日AI新产品讯息 – iiMedia

DeepSeek API: https://api-docs.deepseek.com

Qwen official: https://www.aliyun.com/product/bailian

Meta AI research: https://ai.meta.com/

This post is curated for CnAI Developer Community — connecting global developers to China’s AI and compute power. Bilingual support is automatically provided by our built‑in AI translation. Click the language switcher in the top‑right corner to switch between English and Chinese. Join the discussion and share your perspective!

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