AI Translation API Comparación 2026: GPT-4 vs Claude vs DeepL vs Google

Lo que aprendí tras seis meses probando API de traducción

El año pasado pasé demasiadas horas integrando cinco API de traducción diferentes en nuestro proceso de localización. Lo que empezó como una simple tarea de "elige una y envíala" se convirtió en una madriguera de compensaciones, casos límite y algunos resultados realmente sorprendentes.

Esto es lo que me hubiera gustado que me dijeran antes de empezar.

La respuesta rápida (si tienes prisa)

| API, calidad, velocidad, precio por 1 millón de caracteres |-----|---------|-------|-------------------|----------| | GPT-4o Excelente Media ~$5 Cadenas de interfaz de usuario con mucho contexto | Claude Sonnet | Excelente | Medio | ~$6 | Manteniendo un tono consistente | | DeepL Muy bueno Rápido 25$ Idiomas europeos | Google Translate Muy bueno Muy rápido ,0 Velocidad bruta, idiomas poco comunes | Azure Translator Bueno Muy rápido 10 $ Tiendas Microsoft | Amazon Translate, bueno, muy rápido, 15 $, ya en AWS

Pero honestamente, la verdadera respuesta es "depende", y voy a explicar por qué.

Lo que realmente encontré usando cada uno

OpenAI GPT-4 / GPT-4o

Esto es lo que más utilizamos. No porque sea perfecto, sino porque maneja los casos extremos que siguen rompiendo otras soluciones.

Precios actuales:

| Modelo de Entrada (1M de tokens) Salida (1M de tokens) |-------|-------------------|-------------------| | GPT-4o $5.00 content: 5.00 | GPT-4o Mini 0,15 $ 0,60 | GPT-4 Turbo content: 0.00 $30.00

El truco está en hacer que el prompt del sistema sea correcto. Usted necesita decirle a preservar los marcadores de posición como {name} y {{cuenta}}, or it will helpfully "translate" them. I learned this the hard way when our Spanish build started showing "nombre" instead of the user's actual name.

What actually works well:

• Understands that "Save" in a button context means something different than "Save" as in "save money"
• Handles pluralization rules without me having to explain them
• The JSON mode is genuinely useful for batch operations

What caught me off guard:

• No built-in language detection, you need to handle that separately
• Response times are inconsistent. Sometimes 400ms, sometimes 2 seconds
• Mini is tempting for the price, but quality drops noticeably for complex sentences

My take: Worth it if you're translating UI text or anything where context matters. Overkill for simple strings like "OK" or "Cancel."

Anthropic Claude

I was skeptical at first because Claude isn't really marketed as a translation tool. But after testing it alongside GPT-4, I was surprised how well it handled brand-specific terminology.

Current Pricing:

Where it impressed me:

• We have a glossary of terms we never translate (product names, technical terms). Claude follows these instructions more consistently than GPT-4
• The 200K context window meant we could send our entire glossary with each request
• Tone stays remarkably consistent across long documents

What's less great:

• Slightly slower than GPT-4o on average
• Fewer model options means less flexibility on price/quality tradeoffs

My take: If you're translating marketing copy or anything where brand voice matters, Claude is worth testing. For raw UI strings, it's comparable to GPT-4.

DeepL API

DeepL has a reputation for quality, and for European languages, it's earned. But I've seen too many teams default to it without understanding where it falls short.

Current Pricing:

What's genuinely good:

• German and French translations are noticeably more natural than the LLMs
• Fast. Consistently fast. No random 2-second delays
• The glossary feature actually works (define "enterprise" as "entreprise" and it sticks)

What nobody mentions:

• Japanese and Korean translations feel robotic compared to GPT-4
• No Arabic support at all
• You can't give it context. If "reservation" could mean a hotel booking or a hesitation, DeepL just picks one

My take: If your app is primarily for European markets, DeepL is probably your best choice. For Asian languages or complex context, look elsewhere.

Google Cloud Translation

Google Translate gets a bad rap from people who remember the "All your base" era. The current API is actually quite good for what it is.

Current Pricing:

Where it makes sense:

• 100+ languages. If you need Uzbek or Swahili, this is probably your only option
• Blazing fast. 50ms response times are common
• Language detection is built in and actually reliable

The honest downsides:

• Translations feel "correct but generic." A human would never word it that way
• Struggles with informal text, slang, or anything requiring cultural adaptation
• The AutoML feature sounds great but requires significant training data to be useful

My take: Great for user-generated content where speed matters more than polish. Less suitable for your carefully crafted marketing copy.

Azure and Amazon (Quick Takes)

I'll be honest: if you're already deep in Azure or AWS, the integration convenience might outweigh the quality differences. Both are fine, neither is exceptional.

Azure Translator:

• $10/1M chars is the cheapest paid option
• Free tier (2M chars/month) is generous
• Quality is... okay. Comparable to Google

Amazon Translate:

• $15/1M chars
• Batch processing is well-designed
• IAM setup is its own adventure

Quality Numbers (With Caveats)

We ran 1,000 UI strings through each API for five language pairs. Human translators scored them blind.

A few notes:

• DeepL doesn't support Arabic
• These are UI strings, not literary prose. Results would differ for other content types
• The difference between 88% and 95% is more noticeable than the numbers suggest

Speed in Practice

Average response time for translating about 100 words:

If you're doing real-time translation (chat, live content), Google or Azure are your only realistic options. For batch processing, speed matters less than you'd think.

What It Actually Costs

Let's say you're translating 100,000 strings (averaging 50 characters each) into 10 languages. That's 50 million characters.

The LLM pricing model (tokens vs characters) means they're actually cheaper than traditional MT services for most text lengths. I didn't expect that.

How to Actually Decide

After all this testing, here's my mental framework:

Go with GPT-4o if:

• Your strings have placeholders, variables, or technical content
• You need JSON output for automation
• Context matters (same word meaning different things in different places)

Go with Claude if:

• You've got a brand style guide that needs to be followed
• You're translating longer marketing or documentation content
• Consistency across thousands of strings is critical

Go with DeepL if:

• Most of your users are in Europe
• You're translating formal business content
• You want the best French/German/Dutch quality available

Go with Google if:

• You need languages that others don't support
• Real-time speed is non-negotiable
• You're translating user-generated content where "good enough" is acceptable

Go with Azure/Amazon if:

• You're already locked into that ecosystem
• Compliance requirements point you there

The Hybrid Approach That Actually Works

In production, we ended up using multiple APIs. Marketing copy goes through Claude. UI strings use GPT-4o. User comments use Google. It's more complex to set up, but the quality/cost balance is better than any single solution.

You can set up a simple routing function: critical content gets the expensive API, bulk content gets the cheap one, real-time content gets the fast one. Once it's built, you stop thinking about it.

A Few Hard-Won Lessons

1. Always send context. "Book" translates differently for a library app vs a hotel app. Include your app category or domain in every request.

2. Test with edge cases first. Before committing to an API, try it with your weirdest strings. Placeholders, emoji, HTML snippets, RTL text. The differences show up there.

3. Build in fallbacks. APIs go down. Rate limits hit. Have a backup, even if it's just caching previously translated strings.

4. Human review is still worth it for some content. Error messages, legal text, anything that could embarrass you if wrong. AI translation is good, but not perfect.

5. Translation memory saves money. If you're translating "Save changes" a hundred times across different projects, you should only be paying for it once.

Where to Go From Here

If you're just starting out with translation APIs, my honest advice is to pick GPT-4o Mini and see how far it gets you. It's cheap, the quality is reasonable, and you can always upgrade later.

If you're at the point where you need multiple engines, glossary enforcement, translation memory, and human review workflows, you probably want a proper TMS rather than building it yourself. We built IntlPull to handle exactly that use case. You can use the CLI to push strings and translate with different engines based on content type.

Whatever you choose, the good news is that machine translation in 2026 is genuinely good enough for production use. The question isn't whether to use it, but how to use it well.

Common Questions

Which API gives the best translations in 2026?

For UI and app content, GPT-4o and Claude Sonnet are essentially tied. For European languages specifically, DeepL is still the benchmark. There's no single winner.

What's the most cost-effective option?

GPT-4o Mini gives you surprisingly good quality at $0.15 per million input tokens. If you need free, Azure offers 2 million characters per month.

Can I skip human review entirely?

For most UI strings and help text, yes. For anything legal, medical, or where mistakes could cause real harm, I'd still recommend human review. The 90%+ accuracy sounds great until you remember that 10% means one in ten strings might be wrong.

What happens when an API is down?

This happened to us twice in six months. Build fallbacks. Cache translations. Have a default language that works if everything fails.