OpenAI vs Meta vs DeepSeek: Free & Customizable AI Models You Can Use Today

Why This Matters

On August 5, 2025, OpenAI dropped a bombshell: a new family of free, fully customizable large language models (LLMs) under the codename “OpenFlex”. The move is a direct response to Meta’s wildly popular Llama 3.1 405B and the Chinese underdog DeepSeek-Coder-V2, both of which already offer permissive licenses and zero-cost weights.

For startups, indie developers, and enterprises alike, the playing field just leveled—again. We break down the specs, the catches, and the quickest path to deployment so you can pick the best model for your next project.

Head-to-Head Comparison

Key takeaway: OpenFlex is the smallest and fastest to fine-tune, Llama 3.1 is the largest and most capable, and DeepSeek-Coder-V2 offers the most permissive license.

How to Get & Run Each Model

OpenAI OpenFlex

1. Sign up at platform.openai.com.
2. Download the openai-cli: pip install openai-cli
3. Pull weights: openai model pull openflex-8b
4. Run locally: openai serve openflex-8b --gpu auto

Meta Llama 3.1 405B

1. Accept the license on llama.meta.com.
2. Install Hugging Face CLI: pip install huggingface_hub
3. Download: huggingface-cli download meta-llama/Llama-3.1-405B-Instruct
4. Run with vLLM: vllm serve meta-llama/Llama-3.1-405B-Instruct --tensor-parallel-size 8

DeepSeek-Coder-V2

1. Clone from Hugging Face (no registration): git lfs install && git clone https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct
2. Install DeepSeek-FT: pip install deepseek-ft
3. Run: deepseek-ft serve ./DeepSeek-Coder-V2-Instruct --port 8000

Fine-Tuning in Under 10 Minutes

All three models support LoRA (Low-Rank Adaptation), cutting GPU VRAM by up to 70%. Here’s a universal snippet:

pip install peft transformers datasets
python -m peft.lora \
  --model_name_or_path ./openflex-8b \
  --dataset_name squad \
  --output_dir ./openflex-squad-lora \
  --per_device_train_batch_size 4 \
  --num_train_epochs 1 \
  --learning_rate 2e-4

Swap the model_name_or_path for Llama or DeepSeek and you’re done. Expect 5 minutes on a single A100.

Real-World Use Cases

• Customer Support Bot: Fine-tune OpenFlex-8B on your ticket history for sub-second response times.
• Code Generation: DeepSeek-Coder-V2 dominates on HumanEval and MultiPL-E benchmarks; ideal for IDEs.
• Research Summaries: Llama 3.1 405B’s 405B parameters excel at long-document comprehension.