Free GPT-4 alternatives — open-source models via the OpenAI API

GPT-4 and GPT-4o are excellent models — and at $2.50-$10/M tokens they add up fast. For developers building applications, testing ideas, or running high-volume workloads, there are genuinely capable open-source alternatives that cost $0. All of these run on NVIDIA's DGX Cloud infrastructure (NVIDIA NIM), callable with the exact same code as OpenAI — just change two values. --- ### Drop-in replacement: one base URL change ```python from openai import OpenAI # Before (OpenAI) # client = OpenAI(api_key="sk-...") # After (free open-source models, no code changes) client = OpenAI( base_url="https://api.inferall.ai/v1", api_key="ifu_your_key_here", # get one at inferall.ai/keys — no card required ) # Your existing code works unchanged response = client.chat.completions.create( model="meta/llama-3.3-70b-instruct", # swap in any free model messages=[{"role": "user", "content": "Summarize the history of the internet."}], max_tokens=500, ) print(response.choices[0].message.content) ``` --- ### The best free alternatives to GPT-4 **For general tasks (closest to GPT-4o):** ```python # Llama 3.3 70B — Meta's most refined 70B model, strong instruction following model="meta/llama-3.3-70b-instruct" # NVIDIA Nemotron 120B — NVIDIA's largest free model, excellent reasoning model="nvidia/nemotron-3-super-120b-a12b" # Llama 4 Maverick — Meta's newest (MoE architecture, 17B active / 128 experts) model="meta/llama-4-maverick-17b-128e-instruct" ``` **For coding tasks (alternative to GPT-4o for code):** ```python # Qwen3 Coder 480B — Alibaba's massive coding model (480B total, 35B active) model="qwen/qwen3-coder-480b-a35b-instruct" # Codestral 22B — Mistral's code-specialized model, fast and accurate model="mistralai/codestral-22b-instruct-v0.1" ``` **For lightweight/fast tasks:** ```python # Llama 3.1 8B — extremely fast, good for simple tasks model="meta/llama-3.1-8b-instruct" # Gemma 4 31B — Google's latest open model model="google/gemma-4-31b-it" ``` --- ### TypeScript / Node.js ```typescript import OpenAI from "openai"; const client = new OpenAI({ baseURL: "https://api.inferall.ai/v1", apiKey: process.env.INFERALL_API_KEY, }); // Replace any OpenAI model with a free alternative const response = await client.chat.completions.create({ model: "meta/llama-3.3-70b-instruct", messages: [{ role: "user", content: "Explain quantum entanglement simply." }], }); ``` --- ### Honest tradeoffs These models are genuinely impressive but not identical to GPT-4o: | Task | Llama 3.3 / Nemotron | GPT-4o | |---|---|---| | General conversation | ✅ Excellent | ✅ Excellent | | Summarization | ✅ Excellent | ✅ Excellent | | Code generation | ✅ Strong | ✅ Strong | | Complex multi-step reasoning | ⚠️ Good | ✅ Better | | Instruction following | ✅ Strong | ✅ Strong | | Context window | ✅ 128k (Llama 3.3) | ✅ 128k | | **Cost** | **$0** | **$2.50-$10/M tokens** | For most development, prototyping, and many production workloads, the free models are sufficient. Use GPT-4o when you specifically need its reasoning depth — InferAll routes to both from the same key. See [InferAll's AI inference API](/solutions/ai-inference-api) for full provider and endpoint documentation. --- ### Switching between models easily The real advantage of InferAll: you can switch models without changing your integration. Test which model works best for your use case: ```python models_to_test = [ "meta/llama-3.3-70b-instruct", "nvidia/nemotron-3-super-120b-a12b", "meta/llama-4-maverick-17b-128e-instruct", "anthropic/claude-sonnet-4-6", # paid — add a card for this ] prompt = "Write a Python decorator that adds retry logic." for model in models_to_test: resp = client.chat.completions.create( model=model, messages=[{"role": "user", "content": prompt}], max_tokens=300, ) print(f"\n=== {model.split('/')[-1]} ===") print(resp.choices[0].message.content[:500]) ``` --- ### Get started [inferall.ai/keys](https://inferall.ai/keys) — no credit card required. 200 free requests to evaluate any model, then add a card to unlock the full free allowance ($0 for NVIDIA models) and paid providers (OpenAI, Anthropic, Google) at the published per-token rate with zero markup.