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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.
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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. One clarification matters up front: the closed-weight flagship is API-only while a Max-class model has also been released with open weights under a custom licence, and the open-weight line is what this page describes.
A paid hosted API also exists, running on Alibaba Cloud, and for some organisations that carries a jurisdiction question. Self-hosting the open weights removes it entirely, because data governance stays in your hands rather than with any provider.
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The ceiling is worth naming plainly. On ambiguous specs and sustained multi-agent work the closed frontier models still win, so the working pattern is not Qwen instead of a frontier model but Qwen as the daily driver, with a retained frontier seat for the hardest problems. No tool is best for everyone; this one is best when you want to own the model layer.
- 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
- Local prototyping with Ollama, production with vLLM
- An actively maintained open-weight coding line
- 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. It exists for the organisations the cloud assistants cannot serve.
That deployment flexibility, rather than peak suggestion quality, is the product: regulated industries, defence-adjacent teams and strict-sovereignty environments get modern AI coding inside their own walls.
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It is enterprise-only, with no individual tier, and current suggestion quality deserves a direct test as part of any evaluation.
- 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
- Regulated teams the cloud assistants cannot serve
- Sovereignty requirements with modern assistance inside
- Cost
- Paid only
- 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.
Pricing
- Qwen
Free
Prices as of August 2026.
- Free
- Free
- Tabnine
$39/user·$59/user
Prices as of August 2026.
- Tabnine Code Assistant
- $39per user, per monthbilled annually; usage billed at API rates
- Tabnine Agentic Platform
- $59per user, per monthbilled annually; usage billed at API rates
By area
Where each one pulls ahead, area by area.
| Area | Qwen | Tabnine |
|---|---|---|
| By job | ||
| Software development | Qwen powers an internal coding assistant behind the firewall from weights the team holds, and is built to carry the everyday volume rather than the exceptional case | Tabnine arrives through enterprise procurement with no individual tier, so it is adopted by an organisation rather than tried by a developer |
| By task | ||
| Coding & software development | Qwen anchors a coding stack you own end to end, from the weights upward rather than from a vendor's deployment options downward | Tabnine satisfies the security review itself, arriving with the retention guarantees that decide whether a coding assistant is permitted at all |
| Private, local & self-hosted | Qwen leaves the model choice open, keeping DeepSeek available as a second open-weight option on the same stack so the deployment never locks you to one vendor's models | Tabnine serves the regulated teams that cloud assistants exclude, which is a procurement problem before it is a modelling one |
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 arrives closed-weight and API-only, so that part of the range cannot be self-hosted, and a Max-class model has also been released with open weights under a custom licence. 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 August 2026