Translate Gemma 4B vs 12B vs 27B: 6 Prompt Translation Test

Translate Gemma 4B vs 12B vs 27B is a practical comparison of three Google translation models on the same six short business prompts. The goal was not to declare a universal winner. It was to see where model size changes the result: preserving operational details, choosing natural wording, and handling short text that can affect a delivery, login, or purchase.

The three models are Translate Gemma 4B, Translate Gemma 12B, and Translate Gemma 27B. Wiro documents the family as Gemma 3-based translation models with support for 55 languages. Each exposes the same useful controls: prompt, sourceLng, targetLng, and maxNewTokens.

Translate Gemma 4B vs 12B vs 27B test setup

Every model received the same six prompts. The source and target language were selected explicitly instead of being left for the model to infer. maxNewTokens was set to 200 for every run, which matches the documented default and gives short translations enough room without inviting explanations around the answer.

This is a narrow test. It does not measure long documents, uncertain language detection, legal translation, or quality across all 55 supported languages. It does focus on a useful production question: does the model keep the meaning of dates, placeholders, prices, codes, and instructions while still sounding natural?

What the six translation outputs actually show

1. Turkish checkout note to English

Input: Sepet tutari 1.249,90 TL. Kargo ucreti 79,90 TL. Teslimat tahmini 2-3 is gunu. Siparis notu: Kapiciya birakmayin.

All three retain the amounts and delivery window. The difference is the final instruction. 4B makes it broader, while 12B and 27B identify the doorman. That makes the larger outputs safer for this exact note, though a delivery instruction should still retain its original text in a real workflow.

2. Password reset message to Turkish

The OTP placeholder survived in all three answers. 4B uses a direct translation of the expiry wording. 12B and 27B use more polished Turkish phrasing and explicitly say not to share the code with anyone. The important result is structural: none altered {OTP}. For authentication messages, that is necessary but not sufficient. Approved string catalogs and a native-language review remain the right final check.

3. German ERP instruction to English

The source asked the user to check a supplier declaration and serial number DE-77-2048 before release in an ERP system. Every output preserves the serial number. The divergence is in the action: 4B says to verify before entering data, 12B says before approving the entry, and 27B says before releasing them. The 27B wording is closest to the source intent, but this case also shows why a fluent sentence can still shift a business action.

4. Chinese meeting reschedule to English

4B loses the word “next” before Wednesday. 12B retains “next Wednesday” and asks for a reply before the end of the workday. 27B also keeps the new date and produces the shortest natural English. For scheduling, that one missing qualifier changes the appointment. This is the clearest reason not to automate calendar-facing text without an exact-date check.

5. English UI strings to Spanish

All models translate the four pipe-separated strings cleanly. 4B chooses “Proceder al pago” for checkout and keeps the amount as 50 $. The two larger models use “Finalizar compra” and make the delivery wording more idiomatic. None is obviously wrong. The better choice depends on the product glossary: a store that already uses a fixed Spanish CTA should protect that CTA instead of asking any model to reinvent it.

6. French stock notification to English

4B adds “when it’s back in stock” to the email-notification question. That is helpful context, but it is an addition rather than a literal rendering. 12B and 27B return the shorter original meaning. This is a small difference, yet it is useful evidence that larger model size does not always mean more words. In this set, the larger models often chose a tighter translation when the source was already clear.

Two fresh Wiro outputs

Two new runs used the Turkish checkout prompt with sourceLng=Turkish, targetLng=English, and maxNewTokens=200. These are raw model-output files uploaded to this blog’s media library, not temporary CDN links.

Runtime and cost per output

The elapsed times are averages from the original six-run comparison, not a service guarantee. The current Wiro documentation for these variants does not list a per-output price, and the completed fresh runs did not return a billable cost field. No cost has been inferred from model size. Queueing, hardware allocation, prompt length, and account configuration can change the observed runtime.

When to pick each model

• Pick 4B for short, low-risk UI strings and structured text when fast turnaround matters. It stayed competitive on the Spanish UI strings and preserved key values in this set.
• Pick 12B for a middle ground. It usually gave more natural phrasing than 4B while staying closer to the smaller model’s observed runtime than 27B.
• Pick 27B when a short instruction carries operational weight and the added latency is acceptable. It gave the strongest wording on the checkout note and kept the scheduling qualifier that 4B missed.

None of these outputs should be treated as a final authority for legal, safety, payment, identity, medical, or customer-service commitments. Keep variables in structured fields, test placeholders deliberately, and let a native reviewer approve text that changes an action.

Further reading

• Google’s TranslateGemma 4B model card on Hugging Face
• tgemma open-source batch translation project on GitHub
• TranslateGemma 4B: 8 Translation Prompts for Real Apps
• Translate Gemma Image: OCR Translation in 6 Screenshot Tests
• GLM-4.7-Flash: 6 Quick Tests

Try the models

Run Translate Gemma 4B, Translate Gemma 12B, or Translate Gemma 27B on Wiro with explicit language selectors. Start with the smallest model that passes your own representative test set, then move up only when the wording or error profile justifies it.