AI Translation API Comparison 2025: GPT-4 vs Claude vs DeepL vs Google

What I Learned After Six Months of Testing Translation APIs

Last year, I spent way too many hours integrating five different translation APIs into our localization pipeline. What started as a simple "just pick one and ship it" task turned into a rabbit hole of tradeoffs, edge cases, and some genuinely surprising results.

This is what I wish someone had told me before I started.

The Quick Answer (If You're in a Hurry)

But honestly, the real answer is "it depends," and I'll explain why.

What I Actually Found Using Each One

OpenAI GPT-4 / GPT-4o

This is what we use most. Not because it's perfect, but because it handles the weird edge cases that kept breaking other solutions.

Current Pricing:

The trick is getting the system prompt right. You need to tell it to preserve placeholders like {name} and {{count}}, 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:

What caught me off guard:

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:

What's less great:

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:

What nobody mentions:

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:

The honest downsides:

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:

Amazon Translate:

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:

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:

Go with Claude if:

Go with DeepL if:

Go with Google if:

Go with Azure/Amazon if:

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

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 2025 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 2025?

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.