Llama 4 Beats GPT-5 on Coding and Math. Open-Source Just Won.

Meta's open-weights model outperforms OpenAI's flagship on HumanEval and MATH benchmarks. Anyone can run it locally.

The Open-Source Milestone

For the first time, an open-weights model has definitively beaten a proprietary frontier model on major benchmarks. Llama 4's victory isn't marginal—it's decisive.

BenchmarkLlama 4GPT-5Claude Opus 4 HumanEval92.4%89.1%91.2% MATH78.3%74.6%76.8% MMLU89.7%91.2%90.4% GSM8K96.2%94.8%95.1% Coding (SWE-Bench)58.4%64.3%72.1% Llama 4 leads on coding and math; GPT-5 leads on general knowledge; Claude leads on agentic tasks.

---

Model Specifications

Llama 4 Family

VariantParametersContextVRAM Required Llama 4 Scout8B128K16GB Llama 470B128K140GB Llama 4 Maverick405B (MoE)256K320GB Llama 4 Behemoth2T (MoE)512KAPI only

Key Improvements Over Llama 3

- 3x training compute (estimated $500M+ training cost) - Mixture of Experts architecture for larger models - Native tool use built into base model - Improved instruction following without fine-tuning

---

Why This Matters

1. Anyone Can Run It

Unlike GPT-5 or Claude, you can download Llama 4 and run it on your own hardware. No API calls, no rate limits, no usage tracking.

2. Fine-Tuning Freedom

Organizations can customize Llama 4 for their specific needs: - Train on proprietary data - Remove or add safety measures - Optimize for specific tasks

3. Cost Structure

ApproachCost per 1M Tokens GPT-5 Turbo API$15-30 Claude Opus 4 API$15-75 Llama 4 (self-hosted)$0.50-2 Llama 4 (cloud inference)$1-5 Self-hosting amortizes hardware costs over time.

---

How to Run Llama 4 Locally

Requirements for Llama 4 70B

- GPU: 2x RTX 4090 or 1x A100 80GB - RAM: 64GB+ - Storage: 150GB SSD

Quick Start

```bash

Using Ollama (easiest)

ollama run llama4

Using llama.cpp (most efficient)

./main -m llama-4-70b-Q4.gguf -p 'Your prompt here'

Using vLLM (best for serving)

python -m vllm.entrypoints.openai.api_server \\ --model meta-llama/Llama-4-70B ```

---

Community Response

'This is the iPhone moment for open-source AI. The proprietary advantage just evaporated.' — Andrej Karpathy

'We're switching our production workloads to Llama 4. The cost savings are too significant to ignore.' — CTO at a Fortune 500

'Meta just made AI a commodity. Everyone else is now competing on distribution and features, not model quality.' — VC Partner

---

Meta's Strategy

Why give away a model that cost $500M+ to train?

1. Commoditize AI - If AI is free, Meta's distribution advantage matters more 2. Ecosystem lock-in - Developers who build on Llama stay in Meta's orbit 3. Recruiting - Best AI researchers want to publish and share work 4. Regulation defense - Hard to regulate what everyone can access

---

Limitations

- Agentic tasks: Still behind Claude on autonomous workflows - Multimodal: Vision capabilities lag Gemini 2 - Safety: More jailbreakable than proprietary alternatives - Support: No enterprise SLA or support structure

---

What's Next

Meta announced Llama 5 development is 'well underway' with expected release in late 2026. If the current trajectory holds, open-source will continue closing the gap—or maintaining the lead.

---