Translation quality, examined.

Measure translation quality.

Find the best LLM for translation in your languages using the latest benchmarks. Audit translations against your own quality scorecard.

Backed by Y Combinator

Trusted by international teams managing content at scale.

  • Mistral AI
  • Solana
  • Cal.com
  • SoSafe
  • Laurel
  • Scribe
  • Papermark
  • Truely
  • freenet
  • Maze
  • Formbricks
  • Opus
  • Tulip
  • ImagenAI

01 / LOOK CLOSELY

Measure translation quality

Audit human and LLM translations against your quality scorecard. Track quality by language and translation provider.

01

Find changes in meaning.

Check for omitted information, added claims, and wording that changes the source meaning.

  • SOURCEThe original passage
  • TRANSLATIONThe same passage,translated
  • CHANGED MEANINGWording shifts the claim
  • OMITTED INFORMATIONThe translation drops a detail
  • ADDED CLAIMThe source never says this
  • SOURCEThe original passage
  • TRANSLATIONThe same passage,translated
  • CHANGED MEANINGWording shifts the claim
  • OMITTED INFORMATIONThe translation drops a detail
  • ADDED CLAIMThe source never says this
Exploded drawing of a source passage above its translation. One red projection line runs for each finding. One source clause has no counterpart: its line ends at an empty slot in the translation. One translated clause has changed wording. One translated claim rises to an empty slot in the source, because nothing there matches it.

EXISTING CONTENT

Lingo.dev reads translations where your teams keep them.

01 / Code repositories

The strings your developers ship.

Examine translated strings in each commit and pull request before a release reaches your users.

  • LOCALE FILESSource and translations
  • COMMITSEach change, in order
  • PULL REQUESTSChecked before merge
  • LOCALE FILESSource and translations
  • COMMITSEach change, in order
  • PULL REQUESTSChecked before merge
A short commit history runs along one line. A pull request branches off, adds one commit and merges back. That commit is lifted open to show its locale files: a green source file above two translated files. One string in a translated file is marked red as a finding.

Lingo.dev in numbers

300M+

Words processed

Languages
100+
Enterprise uptime SLA
99.99%
Model authors
80

02 / CHOOSE DELIBERATELY

Decide what to improve next

Compare quality, reviewer edits, and costs. Decide which providers to use and which problems to address first.

01

Choose the next improvement to test.

Use recurring findings to decide whether to investigate terminology, missing context, or translation instructions.

  • TERMINOLOGYGlossary and product names
  • MISSING CONTEXTWhat translators did not know
  • TRANSLATION INSTRUCTIONSGuidance sent with the work
  • YOUR NEXT TESTYour team picks one cause to test
  • RECURRING FINDINGSFindings that repeat, grouped by possible cause
  • TERMINOLOGYGlossary and product names
  • MISSING CONTEXTWhat translators did not know
  • TRANSLATION INSTRUCTIONSGuidance sent with the work
  • YOUR NEXT TESTYour team picks one cause to test
  • RECURRING FINDINGSFindings that repeat, grouped by possible cause
Three trays of recurring findings stand in registered slots: terminology, missing context and translation instructions. Your team has lifted the missing context tray out of its slot to investigate next. Its dashed footprint stays on the floor. Each cause leaves its own pattern on the pages: a repeated term, missing details, or work that departs from its instructions. A red mark on every page edge shows the finding recurs.

From customer stories

Customers describe working with Lingo.dev.

SoSafe
“The knowledge stays with us – what quality looks like, what needs translating, for what audience.”

Annika Palm

Localization Engineer, SoSafe

Read the SoSafe story
Cal.com
“When we needed adjustments, they delivered fixes faster than I’ve seen from any vendor.”

Keith Williams

Head of Engineering, Cal.com

Read the Cal.com story
Papermark
“It was clear they had thought through all the complexities of localization, especially for MDX files which were a particular pain point for us.”

Iuliia Shnai

Founder, Papermark

Read the Papermark story
Laurel
“We’re in a shared Slack channel with the engineers who actually build the platform.”

Nick Bazley

Staff Product Manager, Laurel

Read the Laurel story
Truely
“Their co-founder didn’t just understand the problem – she shipped a solution in a couple of days.”

Sebastiaan van Leeuwen

Product Manager, Truely

Read the Truely story
SoSafe
“And because of how their team is set up, we talk directly to the engineers building the infrastructure. Edge cases get resolved the same day.”

Max Höffner

Director of Product Engineering, SoSafe

Read the SoSafe story
Scribe
“Lingo.dev gave us a localization engine we run from an API, an MCP, and a CLI, right inside our own stack.”

Clément Champau

CEO and Co-Founder, Scribe

Read the Scribe story
SoSafe
“I would recommend revamping the process so the humans employed can actually focus on what matters.”

Sheree Foltin

Content Engineering Teamlead, SoSafe

Read the SoSafe story
Jarvi
“We used to constantly worry about keeping translations in sync. Now we just build features, and localization happens automatically.”

Quentin Decré

Co-founder, Jarvi

Read the Jarvi story

03 / EXPLORE THE RESEARCH

Compare LLMs for your languages

Access Lingo.dev's translation benchmarks. Choose the models worth testing on your content.

01

Compare model rankings by language pair.

Find the strongest benchmark results for the source and target languages you translate.

EN → JAEN → DEFR → ENEN → PT
  • 1 · MODEL CStrongest result for this pair
  • 2 · MODEL A
  • 3 · MODEL D
  • EN → JA
  • EN → DE
  • FR → EN
  • EN → PT
  • YOUR LANGUAGE PAIRThe study ranks models for this pair
  • STUDY CRITERIASet by the published study
  • PUBLISHED STUDYOne ranking per language pair.Each ranking applies only to its pair.
  • 1 · MODEL CStrongest result for this pair
  • 2 · MODEL A
  • 3 · MODEL D
  • YOUR LANGUAGE PAIRThe study ranks models for this pair
  • STUDY CRITERIASet by the published study
  • PUBLISHED STUDYOne ranking per language pair.Each ranking applies only to its pair.
A published study is a base carrying its criteria, with one tile per language pair. The EN to JA tile is selected, and three model layers ranked for that pair float above it. The other pairs keep their own results unopened.

Ongoing research

We study how models translate.

  • OpenAIOpenAI
  • AnthropicAnthropic
  • Google DeepMindGoogle DeepMind
  • MicrosoftMicrosoft
  • Meta
  • Amazon
  • Mistral AIMistral AI
  • DeepSeekDeepSeek
  • Alibaba
  • NVIDIANVIDIA
  • xAI / SpaceXSIxAI / SpaceXSI
  • CohereCohere

Make your localization data-driven.

Start with translations your team has already reviewed.

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