---
title: Open Source vs Closed Source AI: What Actually Matters
description: The real differences between open and closed AI models. When each makes sense, what you give up, and why the distinction matters for your work.
date: February 5, 2026
author: Robert Soares
category: ai-fundamentals
---

The gap between open and closed AI models has collapsed. A year ago, open models trailed their proprietary counterparts by 17.5 percentage points on standard benchmarks. Today, [that gap is 0.3 points](https://mitsloan.mit.edu/ideas-made-to-matter/ai-open-models-have-benefits-so-why-arent-they-more-widely-used). Llama, Mistral, DeepSeek, and Qwen now match GPT-4 and Claude on most tests.

So why do closed models still capture [80% of usage and 96% of revenue](https://openrouter.ai/state-of-ai)?

That's the question worth unpacking. Not which is "better," but when each makes sense, and what you're actually trading off.

## What "Open" and "Closed" Mean (It's Messier Than You'd Think)

The terms get thrown around loosely. Here's the actual distinction.

**Closed source models** like GPT-4, Claude, and Gemini run on the provider's servers. You send text through an API, get a response back. You can't see the model weights, can't modify them, can't run them on your own hardware. The model is a black box you rent access to.

**Open source models** (or more precisely, "open weight" models) like Llama, Mistral, and DeepSeek publish their model weights. You can download them. Run them on your own machine. Fine-tune them for specific tasks. Inspect what they're doing. Deploy them wherever you want.

The distinction matters less for casual use. If you're asking Claude a question or generating a marketing email, you probably don't care whether you can see the weights. But for companies building products on AI, the difference is significant: control over data, customization, cost structure, and what happens when the provider changes something.

## The Cost Gap Is Larger Than Most People Realize

Closed models cost roughly [87% more to run](https://mitsloan.mit.edu/ideas-made-to-matter/ai-open-models-have-benefits-so-why-arent-they-more-widely-used). On average, $1.86 per million tokens versus $0.23 for open alternatives.

At low volume, this barely registers. If you're spending $50 a month on API calls, an 87% savings is $43. Nice, but not worth restructuring your stack.

At scale, the math changes entirely. MIT Sloan researchers estimate that optimal reallocation from closed to open models could save the global AI economy roughly [$25 billion annually](https://mitsloan.mit.edu/ideas-made-to-matter/ai-open-models-have-benefits-so-why-arent-they-more-widely-used).

But cost isn't just about the per-token price. Self-hosting an open model means hardware, maintenance, engineering time. A typical Llama 70B setup needs 8x A100 GPUs, [roughly $80,000 annually in cloud costs](https://hatchworks.com/blog/gen-ai/open-source-vs-closed-llms-guide/) plus the team to manage it. That breaks even against GPT-4 API costs at around 20-30 million tokens per month.

Below that threshold, paying the API premium is often cheaper than running your own infrastructure. Above it, self-hosting starts making financial sense.

As Frank Nagle, a researcher on the MIT study, [put it](https://mitsloan.mit.edu/ideas-made-to-matter/ai-open-models-have-benefits-so-why-arent-they-more-widely-used): "The difference between benchmarks is small enough that most organizations don't need to be paying six times as much just to get that little bit of performance improvement."

## The Major Players

The landscape has fragmented over the past two years. Here's where things stand.

**Closed source:**
- OpenAI (GPT-4, GPT-4o, o1, o3) remains the default for many. Strong general reasoning, fast iteration, deep integrations.
- Anthropic (Claude 3.5 Sonnet, Claude 4) has carved out a reputation for nuanced writing and safety-conscious responses. [Over 60% of programming workloads](https://openrouter.ai/state-of-ai) on OpenRouter go to Claude.
- Google (Gemini) offers massive context windows and tight integration with Google's ecosystem.

**Open source:**
- Meta's Llama family dominates the Western open source ecosystem. Llama 4, released in April 2025, includes models ranging from 17B to 288B parameters. Downloads nearly [doubled from 350 million to 650 million](https://developers.redhat.com/articles/2026/01/07/state-open-source-ai-models-2025) between July and December 2024.
- DeepSeek emerged as a major player, [leading open source token usage](https://openrouter.ai/state-of-ai) with 14.37 trillion tokens processed. Their R1 reasoning model specifically challenges OpenAI's o1.
- Mistral, the French startup, offers efficient models that punch above their weight, particularly for European enterprises concerned about data sovereignty.
- Qwen, from Alibaba, has grown rapidly, [ranking second in open source usage](https://openrouter.ai/state-of-ai) with 5.59 trillion tokens.

The competitive dynamic is shifting. [By late 2025](https://trendforce.com/news/2026/01/26/news-chinese-ai-models-reportedly-hit-15-global-share-in-nov-2025-fueled-by-deepseek-open-source-push/), Chinese models (primarily DeepSeek and Qwen) captured around 15% of global AI usage, up from roughly 1% a year earlier. No single model now exceeds 25% of open source token share.

## Privacy and Data Control

This is where the choice gets personal.

With closed models, your data goes to someone else's servers. OpenAI, Anthropic, and Google all claim not to train on API inputs (with some conditions), but you're trusting their word and their security. If you're in healthcare, finance, legal, or any industry with strict compliance requirements, that trust is a real consideration.

With open models, you can run everything locally. Data never leaves your infrastructure. You control encryption, access, retention.

One [Hacker News commenter](https://news.ycombinator.com/item?id=42768072) captured the calculation this way: "Spending ~$3,000+ on a laptop to run local models is only economically sensible if you are VERY paranoid." That's Simon Willison, a well-known developer in the AI space. He's not wrong that local hosting adds cost. But for some organizations, "very paranoid" is just called compliance.

The privacy picture gets complicated with Chinese open source models. Italy [banned DeepSeek-R1 in April 2025](https://brlikhon.engineer/blog/deepseek-r1-vs-gpt-5-vs-claude-4-the-real-llm-cost-performance-battle) for GDPR violations. Researchers have documented cases where DeepSeek's internal reasoning shows one analysis of sensitive political topics but outputs a different answer. You can run these models locally, but their training and alignment carry a particular context.

## Performance: It Depends What You're Doing

The blanket "which is better" question misses the point. Different models excel at different things.

Closed models still lead on the most demanding tasks. Complex reasoning, nuanced writing, certain coding benchmarks. Claude in particular has become the go-to for developers working on difficult programming problems.

Open models have caught up for most practical applications. And for specific use cases, they can be fine-tuned to outperform general-purpose closed models on narrow tasks.

As one [Hacker News user](https://news.ycombinator.com/item?id=41999151) put it: "Deepseek is my favourite model to use for coding tasks...it has outstanding task adhesion, code quality is consistently top notch & it is never lazy."

The pattern showing up in usage data: closed models capture high-value tasks, open models capture high-volume, lower-value tasks. [Per OpenRouter's analysis](https://openrouter.ai/state-of-ai): "a simple heuristic: closed source models capture high value tasks, while open source models capture high volume lower value tasks."

That heuristic is useful but not universal. Plenty of high-value production systems run on open models. The trade-offs are real, but so is the capability.

## What Open Source Can Do That Closed Can't

There are things you simply cannot do with a closed model.

**Fine-tuning on proprietary data.** You can sort of do this with closed model APIs, but you're limited by what the provider allows. With open models, you have full control. Train on your industry's jargon, your company's documentation, your specific domain.

**Running air-gapped.** Some environments can't connect to external APIs. Defense, certain healthcare systems, secure enterprise networks. Open models are the only option.

**Customizing behavior at the model level.** Not just prompting differently, but actually modifying how the model processes and responds.

**Avoiding vendor lock-in.** When your entire product depends on an API, you're dependent on that provider's pricing, availability, and policy decisions. In January 2025, when DeepSeek released R1 and the AI stock market had a brief panic, companies running on closed APIs were reminded how much they depend on someone else's roadmap.

## What Closed Source Can Do That Open (Mostly) Can't

The trade-offs run both ways.

**Frontier performance.** The absolute best models on the hardest benchmarks are still closed. If you need maximum capability and can afford it, Claude Opus or GPT-4 remains the answer.

**Simplicity.** No infrastructure to manage. No GPU costs. No model updates to handle. Just an API key and a billing relationship. For small teams or rapid prototyping, that simplicity has value.

**Enterprise features.** SOC 2 compliance, enterprise SLAs, admin dashboards, audit logs. Anthropic and OpenAI have built the infrastructure large organizations expect.

**Continuous improvement.** Closed model providers update their models regularly. Sometimes this breaks things (ask anyone who relied on specific GPT-4 behaviors that changed), but mostly it means you're getting better performance over time without lifting a finger.

## The Real Choice Framework

Forget the tribalism. Here's when each approach makes sense.

**Open source fits when:**
- You're processing massive volume (millions of tokens monthly)
- Data can't leave your infrastructure for compliance or security reasons
- You need to fine-tune on specialized domain data
- You want to avoid API dependency for a core business function
- You have (or can hire) the engineering capacity to run and maintain models

**Closed source fits when:**
- You need maximum capability, cost secondary
- Volume is moderate enough that API costs don't dominate
- You want to move fast without infrastructure overhead
- You're prototyping or validating before committing to a stack
- Your team is focused on product, not model ops

Many organizations end up using both. Closed models for complex tasks where quality matters most. Open models for high-volume, cost-sensitive applications. The smart play is often not choosing sides but knowing when each tool fits.

## The Convergence Ahead

Open models now achieve [89.6% of closed-model performance at release](https://mitsloan.mit.edu/ideas-made-to-matter/ai-open-models-have-benefits-so-why-arent-they-more-widely-used), and typically match them within 13 weeks. A year ago, that catch-up period was 27 weeks.

The performance gap keeps shrinking. The cost gap isn't. If anything, open models are getting cheaper while closed model pricing has stayed relatively stable.

This doesn't mean closed models are doomed. They'll likely keep the frontier, at least for the hardest problems. And the simplicity of "just use the API" isn't going away. But the economic case for open source keeps getting stronger, and the capability excuse for avoiding it keeps getting weaker.

What remains unclear is whether the current ecosystem can sustain itself. Meta spends billions developing Llama and releases it for free. DeepSeek's efficiency gains came from a Chinese lab with access to cheap compute. Neither business model makes obvious sense unless you squint at second-order effects (Meta wants AI everywhere to power engagement; DeepSeek is a hedge fund that wants better AI for trading). The question of who pays for open AI development, and how that shapes what gets built, is still unresolved.