The old compromise is gone
For years, the trade-off was simple: cloud models were dramatically better and open weights were a hobby. That gap has narrowed considerably. The current open-weight coding models we test in AI Arena — including the specific Qwen Coder, DeepSeek, Codestral and Llama model versions named in each published test — handle real refactoring, test generation and repository-level tasks that were unthinkable on a local GPU a couple of years ago. Results should not be generalised across an entire model family: changes in version, quantisation and serving setup can materially alter the outcome.
But a favourable result on a benchmark or in a focused test is not the same as being equal in your editor. Cloud models can still lead on the things that hurt most in production: multi-file agentic changes, very long context, and reliable tool-calling. The honest 2026 answer for most teams is not one or the other — it is a deliberate choice per use case.
What we measure in AI Arena — and what we do not trust
Coding benchmarks like HumanEval, SWE-bench and LiveCodeBench are useful filters, not verdicts. A model can score well on a benchmark and still butcher a real pull request. That is why our benchmark rig measures what actually matters when you run a model yourself:
- Tokens per second — how fast code comes out, not just how correct it is.
- Time-to-first-token — the perceived "thinking" delay before any output starts.
- VRAM usage — whether a model fits on your GPU with a usable context window.
- Practical context — how long a context you can keep open before speed collapses.
Live numbers for every model we test, including the precise model version, quantisation and test setup where published, are available on the AI Arena page. The rest of this article is about how to read those numbers for your own situation. They are measurements of particular runs, not universal specifications.
Cloud vs open weights: the decision table
| Factor | Open weights (local) | Cloud API |
|---|---|---|
| Upfront cost | GPU + hardware (about €550 for a 16 GB card, an illustrative assumption rather than a dated price) | €0 |
| Monthly cost | A few euros in electricity under the stated usage assumption | Consumer subscription pricing varies by named plan, country and date, or use a per-token API |
| Data control | With a correctly configured local deployment, code can remain on your machine | Data leaves your network; obligations depend on the provider terms, processing arrangement and transfer mechanism |
| Capability ceiling | Can be behind on some complex agentic work, depending on the model and setup | Often offers stronger tool-calling and larger context, depending on the model and provider |
| Context window | Illustratively 8k–32k on some consumer-GPU setups | Illustratively 128k to over 1M for some services |
| Output speed | Illustratively 5–50 tok/s depending on GPU, model, quantisation and context | Illustratively 50–150 tok/s, plus network latency, depending on provider, model and prompt |
| Offline use | Yes | No |
| Maintenance | You handle updates, quantisation, VRAM juggling and configuration | Handled by the vendor |
The context and speed ranges in this table are illustrative, not universal. Model version, provider, prompt length, quantisation, hardware, software stack and current service load can all change the result substantially.
Where cloud still wins: agents and context
Cloud coding agents — the current generation of Claude Code, Codex-style agents and Gemini's coding agents — can operate across hundreds of files and keep very large repositories in context, subject to the specific product and plan. A 16 GB GPU may not be able to do that at a useful speed. If your daily work is large refactors, PR reviews or multi-file scaffolding, a cloud plan may be worth its price, but the conclusion depends on the model version, workload and provider limits.
Where local wins: privacy, latency and lock-in
Local models can give you two things an API may not: greater data control and predictable latency. There is no upload queue or per-token bill for experiments, although local systems can still have hardware and software bottlenecks. For snippets, boilerplate, tests and some tasks involving customer data, a local 8–14B model may be good enough — and, with a correctly configured deployment, the output need not leave your desk.
What 16 GB of VRAM can actually run
Approximate memory footprints with 4-bit quantisation:
- 7–8B models: ~5 GB VRAM — comfortable on 16 GB with a large context.
- 14B models: ~9–10 GB — the sweet spot for a 16 GB card.
- 32B models: ~20 GB — needs a 24 GB card for decent speed.
- 70B models: ~40 GB — server territory.
These are rough planning figures, not fixed requirements. The KV cache for long context windows also eats VRAM, so a 14B model at 32k context uses noticeably more than the base footprint. And the giant MoE models with hundreds of billions of parameters are not a practical local option on a typical consumer GPU: a 4-bit version of a 671B-parameter model needs roughly 400 GB. Do not buy a gaming PC expecting to run that class of model.
The cost math: subscriptions vs silicon
Let's put the numbers on one page. Assumptions: a professional developer, 20 working days a month, two hours of active AI-assisted coding per day. Consumer subscription prices change by plan, country, VAT treatment and date, so no single €23–28 range is used here. Check the current EU price, including VAT, for the specific plan before making a purchase decision.
- Cloud subscription: use the current, VAT-inclusive price of the named ChatGPT Plus, Claude Pro or Gemini plan available to you on the day you subscribe. That is the figure to compare with your expected usage, rather than a generalised subscription range.
- Local hardware: an RTX 5060 Ti 16 GB at €550 is an illustrative hardware-cost assumption, not a dated market quote. Electricity at the EU average near €0.25/kWh, with a 250 W GPU load for two hours a day, 20 days a month: 10 kWh, or about €2.50 a month for the GPU’s draw alone.
The hardware pays for itself in roughly two years only as a simplified comparison with a subscription of a similar value, and only if the local model actually covers your workload. The €2.50 electricity figure excludes the rest of the computer, idle time, other system consumption and hardware depreciation. If a local 14B model makes you 15% slower on complex refactors, the subscription may be the cheaper option in billable time. This is why we run both in production.
For API users, the comparison is per token. Order of magnitude: premium cloud APIs charge roughly $2.50–3 / €2.50–3 per million input tokens and $8–15 / €8–14 per million output tokens; lighter models cost a fraction of that. These figures vary by provider, model version and billing date. A local model also has hardware, electricity and maintenance costs, while the quality difference shows up in how much you have to fix afterward. "Free tokens" can still be expensive in human hours.
The European angle: GDPR, AI Act and where your code sleeps
For EU companies, the local-vs-cloud decision is also a data protection decision. Code can contain personal data — emails, IP addresses, test dumps, database samples — but not every code transfer involves personal data or has the same legal consequence. Sending relevant data to a cloud provider requires an assessment of the controller’s instructions, processor terms, retention, security measures and any international transfer mechanism, such as standard contractual clauses. Local processing can reduce transfer and provider exposure, but it is not automatically compliant. A legal or privacy review is appropriate for regulated or sensitive workloads.
The EU AI Act adds a second layer. It targets providers of general-purpose AI models, not the developer who merely uses a model. But the moment you fine-tune an open-weight model and offer it as a service, you can become a provider with transparency and copyright obligations. For most in-house coding assistants, the practical consequence is simple: document which models you use, where data is processed, what is retained, and why, then confirm the position with the relevant legal or privacy team.
European options exist. Mistral runs models on EU-hosted infrastructure, and Mistral's Codestral 2 is positioned for coding use. Licensing and commercial-use terms depend on the exact model release, so check the applicable licence rather than generalising from the model family; the original Codestral had different restrictions. And "open" does not automatically mean "GDPR-friendly": Italy's data protection authority blocked DeepSeek in early 2025, and other EU authorities voiced similar concerns. Availability in the EU is not the same as regulatory safety.
The practical decision flow
- Audit your code. Determine whether it contains personal data, what your controller instructions and provider terms allow, where processing occurs, how long data is retained and which safeguards apply. A repo leaving your network does not automatically create a breach notification, but it may require a documented privacy and security assessment.
- Split your tasks. Autocomplete, snippets, tests and boilerplate can go local. Large refactors, repo-wide agents and architectural questions can go to the cloud where the provider terms, transfer mechanism, residency and retention arrangements are acceptable.
- Check your hardware. 16 GB VRAM runs up to ~14B models well in some configurations. If you have less, start with 7–8B. Use the AI Arena numbers for the exact model version, quantisation and setup to pick a size you can actually serve.
- Test local first. Ollama, Continue and Aider make this a one-hour experiment. Compare against free cloud tiers where available to build a baseline before paying.
- Try cloud trials. Run the same tasks in ChatGPT, Claude or Gemini and measure time-to-done, not just time-to-first-token.
- Go hybrid. Route by sensitivity, compliance requirements and task. This is what we do in production, and it can be cheaper than subscribing everyone to everything.
FAQ
Is a 16 GB GPU really enough for useful coding models in 2026?
Yes, with limits. A 14B model at 4-bit quantisation may fit with a reasonable context window and produce useful code for autocomplete, tests and focused edits. Exact speed and quality depend on the model version, quantisation, context and software setup. It will not necessarily replace a cloud agent for multi-file refactors.
Can I paste proprietary code into GPT, Claude or Gemini?
That depends on the product, plan, contract and your organisation’s data-protection assessment. Enterprise APIs may offer zero-retention and EU data-residency options, but "zero retention" does not necessarily mean "zero transfer". Check the controller instructions, processor terms, transfer mechanism, residency, retention and safeguards, and obtain a legal or privacy review for regulated customers. Local processing may be appropriate, but it is not the only possible compliant arrangement.
Do I need a coding agent, or is autocomplete enough?
Autocomplete predicts the next few lines; agents plan across files, run tests and fix fallout. If your work is greenfield features and big refactors, an agent may save hours. If you mostly write glue code, a local 14B model in an IDE is likely enough.
Bottom line: in 2026, choosing between open weights and cloud is not a religion — it is a routing decision. Keep sensitive code on your GPU when that fits your requirements, send heavy reasoning to the cloud only where the provider, transfer and retention arrangements are acceptable, and use measurements from the exact model and setup to decide where each task goes.
