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Devin CLI supports several model families, but that does not mean every model runs on your computer. The CLI runs in your terminal and works with local files; model inference may still be hosted. Devin’s current listing includes Anthropic Claude, OpenAI GPT, Google Gemini, Cognition models, and open-weight models including Kimi, GLM, and DeepSeek. If your goal is genuine on-device inference, you need a local runtime and compatible model—and the available evidence does not establish that Devin CLI provides that path for every model it lists.
What “local” means in Devin CLI
Devin describes its CLI as a terminal tool that accesses your local repository, shell, and credentials. That describes where the agent interface operates, not necessarily where the language model computes. Devin Cloud is a separate product that runs in a virtual machine and can receive a session handoff. Devin’s documentation explicitly says the CLI and Devin are separate tools designed for different workflows.
So there are two distinct questions: which models the CLI supports, and where those models run. The current product overview answers the first with a list of model families; it does not establish that all those models—or any particular selection—run on your own hardware. The CLI overview displayed version v2026.9.2 and listed macOS, Linux, and Windows support when accessed on October 7, 2026. Product listings and capabilities can change.
Which model families does Devin CLI list?
Devin’s current overview lists these families:
- Anthropic Claude
- OpenAI GPT
- Google Gemini
- Cognition models, including Devin Fusion
- Open-weight models including Kimi, GLM, and DeepSeek
That is meaningful breadth, but “every AI model” overstates it: the listing names selected families, not every model available across providers. Devin also describes Fusion as combining a frontier lead model for decisions and important edits with a lower-cost sidekick for exploration, file reads, and test runs. That is Devin’s description of its system, not an independently verified performance result. The available overview does not say that Fusion runs locally.
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In an active session, the product overview says the /model command switches models. Check Devin’s current model listing and the command’s available options rather than assuming a particular model, provider, or local runtime is supported: Devin CLI model and product overview.
Does Devin CLI replace a paid cloud model with a local LLM?
Not on the evidence available. Running the CLI locally gives the agent access to your local working environment; it does not, by itself, move inference onto your machine. Devin’s official overview lists hosted-provider families alongside open-weight models, but does not establish a built-in local-inference route for every supported model. A model being open-weight also does not prove that a particular product runs it locally.
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For on-device inference, separate runtimes such as Ollama and LM Studio provide local-model options. Ollama distinguishes its local models from hosted cloud models. These runtimes are separate from Devin CLI; the cited Devin material does not document an integration that turns every Devin-supported model into local inference. See Ollama’s platform and model information and LM Studio’s system requirements.
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Local feasibility depends on the model, its context requirements, and your hardware. LM Studio recommends at least 16GB of RAM on Apple Silicon Macs; it says an 8GB Mac may work with smaller models and modest context. For Windows, it recommends at least 16GB of RAM and 4GB of dedicated VRAM. These are broad recommendations, not guarantees of acceptable speed or coding quality.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
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Ollama likewise cautions that large models can be slow on computers without a strong GPU. Before choosing a machine—whether you are considering an Apple Silicon Mac with 16GB of RAM or another system—check the specific model’s memory needs and try your intended coding workload. The cited guidance does not establish that any specific computer will deliver a particular model capacity, response time, or quality.
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Devin’s product page reports two benchmark comparisons from Artificial Analysis Coding Agent Index 1.5: Devin Fusion with Fable 5.1 cost $7.90 per run versus $12.36 for Claude Code with Fable 5.1; Devin Fusion with Astra 6 cost $4.54 versus $7.47 for Codex with Astra 6. Those are figures Devin attributes to that named index. They are not a measurement of local inference, a monthly bill, or your personal savings.
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A fair comparison between hosted use and local inference needs your own usage volume, task mix, model quality, and hardware costs. Local operation may avoid a hosted inference charge for a locally run model, but the full cost picture also includes hardware acquisition, electricity, setup, and maintenance. Hosted and local models may differ in capability and speed, so a lower inference bill alone does not show that one is cheaper overall.
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- Hosted model: Consider recurring usage or subscription charges, network access, and the provider’s data-handling terms.
- Local model: Consider the upfront hardware cost, power use, model setup, and whether your system can handle the model and context you need.
- Either approach: Compare results on your actual coding tasks rather than assuming that a model name or a benchmark price predicts your experience.
Workflow trade-offs beyond inference
Devin CLI’s local-terminal workflow is not identical to Devin Cloud. At the time the documentation was accessed, it said the CLI did not yet support account Knowledge, Playbooks, or Secrets available in Devin Cloud. These feature limits are product details that may change; consult the Devin CLI documentation for the current distinction.
Local inference and local file access are also separate decisions. A CLI can work against files on your computer while relying on hosted model inference; conversely, a separate local runtime can perform inference on-device without providing Devin Cloud’s workflow features. Assess model access, data handling, latency, maintenance, and required agent features independently.
Is OpenDevin an alternative?
OpenDevin is a separate open-source project, not Devin by Cognition. Its README describes configuration for multiple LLM backends, including a local Ollama path. The same README labels the project alpha and warns that it may be unstable, can issue many prompts, and that most configured LLMs cost money. Its ability to connect to a local backend does not guarantee that every workflow is local or free. Review its current README and project status before adopting it.
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