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Qwen vs Tabnine
A plain-English comparison to help you choose between them.
Build the private stack or buy the private product. Pick Qwen when your team wants to own the model layer: Apache-2.0 open weights served on infrastructure you govern, starting locally with Ollama and graduating to vLLM, wired into your own agentic coding workflows. Pick Tabnine when private coding AI must arrive finished: air-gapped or on-premises deployment, zero code retention, custom models trained on your codebase and a compliance posture built for audits. Chosen backwards it hurts both ways: Qwen without platform engineers stalls, and Tabnine where open weights would have done buys governance you did not need.
Side by side
- Summary
Qwen is Alibaba's model line, and the part this guide recommends is the open-weight family: the models you download and serve on your own hardware.
- Best for
- The current default for new self-hosted coding work
- Apache-2.0 open weights, free to download and run
- Coding agents on infrastructure you control
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Runs privately (self-hostable)
- Data
- Qwen's hosted API runs on Alibaba Cloud, which carries a jurisdiction question for some organisations. Self-hosting the open weights removes it: the model runs on your own infrastructure, so data governance stays entirely in your hands.
- Summary
Tabnine is the coding assistant for environments where code must not leave: fully air-gapped and on-premises deployment, zero code retention, a compliance posture built for audits, and custom models trained on your private codebase.
- Best for
- Fully air-gapped and on-premises AI coding
- Zero code retention with an audit-ready posture
- Custom models trained on your private codebase
- Cost
- Freemium (Free tier + paid plans)
- Ease
- Openness
- Hosted service
- Data
- The privacy architecture is the product: self-hosted and air-gapped options keep code entirely inside your perimeter, with licensed-data provenance documented.
Common questions
What does each route really cost?
Qwen's weights cost nothing to download, but the spend shifts to GPUs and the engineering time to serve them, and that commitment runs as long as the deployment does: treat it as buying infrastructure, not software. Tabnine is enterprise-only, bought through procurement at the premium end of the assistant market. The honest comparison is Tabnine's licence against your hardware plus a platform team's time.
Which is more capable at coding?
Qwen's open-weight line is the current default for new self-hosted coding work and is actively maintained, though its flagship 3.7 models are closed-weight and API-only, so the very top of the range cannot be self-hosted. Tabnine runs proprietary models, so test current suggestion quality directly as part of any evaluation. On ambiguous work, closed frontier models still lead both.
Who should honestly choose which?
A regulated organisation without platform engineers should choose Tabnine: the guarantee arrives managed, with documentation an auditor can read, and nobody has to run a serving stack. An engineering organisation that already treats tooling as infrastructure should choose Qwen and own the model layer end to end. If neither constraint applies, the mainstream cloud assistants beat both on convenience.
Related comparisons
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Tool facts last checked July 2026