Why Open Source?

Get better value for money

Closed-source model providers make healthy margins on their API pricing, and they need to pay back the high training costs. For the same capability level, open models are 5–10x cheaper.

Sources: Doubleword — comparison pricing page · Artificial Analysis — model comparison

Get frontier performance

Open-source models are now highly competitive. For example, as of 22nd June 2026, GLM 5.2 is reported to be beating GPT-5.5 on design benchmarks.

Sources: Doubleword — frontier open-source LLMs · MindStudio — what is GLM 5.2

Control depreciation / upgrade cycles

OpenAI / Anthropic regularly deprecate models that are no longer their “frontier” models. E.g. GPT-5 is scheduled to be deprecated on December 11, 2026.

Source: OpenAI — model deprecations

Don’t lose access

US-based access to frontier models can be removed within 90 minutes, as evidenced by the June 10th Fable 5 event.

Source: Anthropic — Fable Mythos access

Don’t give money to a potential future competitor

Frontier model providers may build businesses in the future that compete with yours. As an example, if you were running a consulting business, frontier-model joint ventures are now a competitor. Or if you are Figma, Anthropic is now a competitor.

Sources: TechCrunch — Anthropic CPO leaves Figma’s board · Forbes — OpenAI starts selling what your consulting business sells

Don’t discriminate against employees based on nationality

US-based frontier models may eventually discriminate access based on user nationality, forcing you to have tiered access within your organisation.

Source: Anthropic — Fable Mythos access

Avoid a single nation dominating the intelligence business

All of the competitive frontier model labs are US-based, and the US government has already demonstrated an appetite to use their businesses and industries as leverage in geopolitical conversations. The US government has also demonstrated that it is happy to restrict access to models on a nationality basis.

Source: Anthropic — Fable Mythos access

Control your latency, throughput, and uptime

Proprietary models have inconsistent latency / throughput / uptime metrics that can’t be tuned for your use case. Open models can be deployed in a way that suits your use case by choosing a provider that optimises its stack in line with your requirements.

Get consistent model performance

Proprietary model providers have been known to nerf model capabilities without warning — sometimes for ‘safety reasons’, sometimes accidentally, and sometimes for suspected capacity reasons. Open models don’t change their capabilities over time, because the weights don’t change. If you are building an application on top of a model, you know the model’s capabilities will not change over time.

Improve performance on your use case

You can further fine-tune / post-train open models with your proprietary data to make them better at your use case. Proprietary model providers have a built-in capability ceiling that you can’t cross.

Don’t get locked into a single provider

Open-model inference providers have to compete aggressively on cost, reliability, data policies, locality, latency, and more. They compete aggressively to ensure they have the best serving stack and developer experience, because they can’t differentiate as well on the underlying model. Also, by building with open models you will adopt harnesses and tools that are model-agnostic by design — whereas proprietary model providers attempt to lock you in with a full harness ecosystem (e.g. Claude Code).

Don’t go through identity checks to access it

As of June 2026, Claude now requires identity verification for some use cases.

Source: Identity verification on Claude

Benefit from a fast-moving, vibrant community

Open models get a whole ecosystem on top of them: quantizations, fine-tunes, and serving tooling (vLLM, SGLang, Dynamo, OpenCode), built and improved by thousands of people, often within hours of release.

Host it in an airgapped environment

If you need to deploy in an airgapped or highly secure environment, only open models will allow you to do this (unless you’re the US government). You can take an open model and deploy it wherever you have access to compute, without any need for a connection to the internet.

Run on the edge / on-device

Open weights can be run directly on laptops, phones, robots, or embedded hardware when use cases require.

Control your carbon footprint

Open models allow you to control where you deploy and what energy source powers that data centre.

Achieve data sovereignty

Open models can be deployed wherever you need them to be, and can ensure that data does not leave your required data region. Proprietary models are only deployed in a selection of data regions. For example, if I am a highly regulated Mexican company or government, I...