For most models, translation into the slowest language takes over three times as long as into the median language.
With an MT engine, the slowest languages differ from those of the LLMs. With Yandex Cloud Translate v2, the slowest five are Ukrainian, Kyrgyz, Azerbaijani, Tajik and Tatar, and translation into any language takes at most twice the median time.
With Translated Lara 2, the slowest languages are those of the LLMs, at a fraction of their time. Translation into Sinhala, Lao and Odia takes 7.7 to 9.4 seconds per 1,000 words with Translated Lara 2, against 93 to 144 seconds per 1,000 words with xAI Grok 4.20 (reasoning).
Chart data
| Language | Seconds per 1,000 words |
|---|---|
| Tatar | 8.14 |
| Tajik | 8.01 |
| Azerbaijani | 7.1 |
| Kyrgyz | 6.97 |
| Ukrainian | 6.83 |
| Serbian | 6.8 |
| Armenian | 6.49 |
| Gujarati | 6.17 |
| Hungarian | 5.94 |
| Tamil | 5.78 |
| Malayalam | 5.77 |
| Japanese | 5.72 |
| Belarusian | 5.64 |
| Khmer | 5.5 |
| Croatian | 5.46 |
| German | 5.25 |
| Punjabi | 5.24 |
| Lao | 5.15 |
| Dutch | 5.03 |
| Slovak | 4.86 |
| Marathi | 4.8 |
| French | 4.79 |
| Bulgarian | 4.74 |
| Welsh | 4.72 |
| Basque | 4.7 |
| Mongolian | 4.6 |
| Norwegian | 4.58 |
| Finnish | 4.57 |
| Swedish | 4.47 |
| Burmese | 4.42 |
| Indonesian | 4.4 |
| Russian | 4.35 |
| Turkish | 4.35 |
| Yiddish | 4.3 |
| Swahili | 4.27 |
| Kannada | 4.22 |
| Catalan | 4.19 |
| Czech | 4.17 |
| Greek | 4.16 |
| Telugu | 4.15 |
| Spanish (European) | 4.12 |
| Afrikaans | 4.11 |
| Portuguese (European) | 4.11 |
| Icelandic | 4.08 |
| Bengali | 4.05 |
| Lithuanian | 3.98 |
| Sinhala | 3.96 |
| Malagasy | 3.93 |
| Galician | 3.82 |
| Zulu | 3.82 |
| Sundanese | 3.81 |
| Amharic | 3.79 |
| Irish | 3.76 |
| Maltese | 3.75 |
| Cebuano | 3.7 |
| Italian | 3.69 |
| Albanian | 3.67 |
| Nepali | 3.64 |
| Hindi | 3.53 |
| Latvian | 3.5 |
| Urdu | 3.5 |
| Korean | 3.49 |
| Estonian | 3.46 |
| Xhosa | 3.46 |
| Slovenian | 3.45 |
| Macedonian | 3.42 |
| Haitian Creole | 3.4 |
| Danish | 3.36 |
| Romanian | 3.32 |
| Hebrew | 3.31 |
| Farsi | 3.26 |
| Uzbek | 3.23 |
| Tagalog | 3.15 |
| Chinese Simplified | 3.1 |
| Bosnian | 3.09 |
| Georgian | 3.09 |
| Thai | 3.01 |
| Scottish Gaelic | 2.99 |
| Vietnamese | 2.91 |
| Arabic (MSA) | 2.9 |
| Malay | 2.87 |
| Polish | 2.77 |
| Javanese | 2.59 |
Chart data
| Language | Seconds per 1,000 words |
|---|---|
| Tamil | 30.13 |
| Yiddish | 10.23 |
| Odia (Oriya) | 9.35 |
| Lao | 7.85 |
| Sinhala | 7.67 |
| Khmer | 6.47 |
| Amharic | 5.85 |
| Uyghur | 4.78 |
| Bengali | 4.54 |
| Turkmen | 4.19 |
| Burmese | 4.05 |
| Punjabi | 3.73 |
| Malayalam | 3.34 |
| Assamese | 3.25 |
| Yoruba | 3.22 |
| Gujarati | 3.15 |
| Telugu | 2.88 |
| Kannada | 2.87 |
| Kyrgyz | 2.82 |
| Marathi | 2.65 |
| Tajik | 2.64 |
| Igbo | 2.54 |
| Nepali | 2.48 |
| Kinyarwanda | 2.48 |
| Scottish Gaelic | 2.44 |
| Mongolian | 2.41 |
| Hindi | 2.4 |
| Malagasy | 2.4 |
| Thai | 2.39 |
| Tatar | 2.39 |
| Sesotho | 2.38 |
| Georgian | 2.37 |
| Hausa | 2.25 |
| Kazakh | 2.25 |
| Shona | 2.21 |
| Somali | 2.17 |
| Maltese | 2.13 |
| Wolof | 2.12 |
| Greek | 2.11 |
| Belarusian | 2.08 |
| Xhosa | 2.08 |
| Albanian | 2.06 |
| Uzbek | 2.05 |
| Armenian | 2.03 |
| Latvian | 2.02 |
| Welsh | 2.01 |
| Urdu | 2.01 |
| Cebuano | 2 |
| Irish | 2 |
| Pashto | 2 |
| Sundanese | 1.96 |
| Zulu | 1.96 |
| Icelandic | 1.92 |
| Basque | 1.9 |
| Hebrew | 1.84 |
| Lithuanian | 1.83 |
| Swahili | 1.81 |
| Ukrainian | 1.81 |
| Afrikaans | 1.79 |
| Bulgarian | 1.79 |
| Hungarian | 1.78 |
| Azerbaijani | 1.76 |
| Farsi | 1.76 |
| Japanese | 1.76 |
| Estonian | 1.75 |
| Tagalog | 1.75 |
| Macedonian | 1.72 |
| Polish | 1.7 |
| Javanese | 1.69 |
| Finnish | 1.68 |
| Chinese Simplified | 1.67 |
| Czech | 1.66 |
| Haitian Creole | 1.66 |
| Bosnian | 1.65 |
| Slovak | 1.65 |
| Chinese Traditional | 1.65 |
| Catalan | 1.64 |
| Arabic (MSA) | 1.62 |
| Russian | 1.62 |
| Turkish | 1.62 |
| French | 1.61 |
| Romanian | 1.61 |
| Slovenian | 1.61 |
| Korean | 1.6 |
| Galician | 1.57 |
| Croatian | 1.57 |
| Swedish | 1.57 |
| Serbian | 1.56 |
| Malay | 1.55 |
| Danish | 1.52 |
| Portuguese (European) | 1.51 |
| Vietnamese | 1.45 |
| Italian | 1.44 |
| Dutch | 1.43 |
| German | 1.38 |
| Indonesian | 1.38 |
| Spanish (European) | 1.29 |
Chart data
| Language | Seconds per 1,000 words |
|---|---|
| Yoruba | 231.32 |
| Odia (Oriya) | 143.61 |
| Lao | 141.4 |
| Amharic | 134.49 |
| Yiddish | 123.81 |
| Malayalam | 118.05 |
| Turkmen | 111.11 |
| Kyrgyz | 108.07 |
| Somali | 107.94 |
| Tatar | 103.43 |
| Khmer | 101.52 |
| Burmese | 100.84 |
| Mongolian | 98.73 |
| Scottish Gaelic | 98.1 |
| Gujarati | 94.77 |
| Kannada | 94.74 |
| Pashto | 94.11 |
| Sinhala | 93.16 |
| Cebuano | 92.82 |
| Uyghur | 92.58 |
| Shona | 91.8 |
| Irish | 88.18 |
| Tamil | 86.12 |
| Punjabi | 85.66 |
| Javanese | 84.83 |
| Telugu | 82.86 |
| Maltese | 80.56 |
| Kinyarwanda | 79.95 |
| Malagasy | 79.22 |
| Frisian | 78.33 |
| Wolof | 77.23 |
| Georgian | 76.77 |
| Assamese | 76.58 |
| Tajik | 75.99 |
| Welsh | 75.15 |
| Bengali | 74.12 |
| Armenian | 73.21 |
| Sundanese | 72.05 |
| Sesotho | 71.94 |
| Nepali | 71.93 |
| Haitian Creole | 69.92 |
| Igbo | 69.36 |
| Marathi | 68.93 |
| Hausa | 68.08 |
| Albanian | 66.88 |
| Belarusian | 66.85 |
| Uzbek | 66.69 |
| Finnish | 65.78 |
| Estonian | 65.66 |
| Azerbaijani | 65.23 |
| Arabic (MSA) | 64.8 |
| Greek | 64.51 |
| Kazakh | 64.13 |
| Hebrew | 63.82 |
| Bosnian | 63.14 |
| Kurdish (Kurmanji) | 62.59 |
| French | 61.76 |
| Icelandic | 61.7 |
| Tagalog | 61.02 |
| Catalan | 60.98 |
| Romanian | 60.51 |
| Latvian | 59.19 |
| Slovenian | 58.41 |
| Hindi | 58.28 |
| Swahili | 58 |
| Urdu | 57.69 |
| Afrikaans | 57.53 |
| Bulgarian | 57.38 |
| Hungarian | 57.37 |
| Lithuanian | 56.29 |
| Japanese | 55.99 |
| German | 55.27 |
| Basque | 54.97 |
| Slovak | 53.82 |
| Macedonian | 53.71 |
| Danish | 53.11 |
| Zulu | 52.36 |
| Swedish | 52.24 |
| Xhosa | 51.28 |
| Russian | 50.6 |
| Croatian | 50.43 |
| Spanish (European) | 49.79 |
| Malay | 49.72 |
| Norwegian | 49.72 |
| Thai | 49.38 |
| Portuguese (European) | 49.07 |
| Ukrainian | 48.81 |
| Farsi | 48.41 |
| Italian | 47.87 |
| Dutch | 47.36 |
| Chinese Traditional | 47.35 |
| Galician | 46.22 |
| Serbian | 44.5 |
| Czech | 43.7 |
| Chinese Simplified | 43.61 |
| Polish | 42.49 |
| Vietnamese | 39.47 |
| Korean | 39.33 |
| Turkish | 37.02 |
| Indonesian | 32.07 |
Translation time in the median and slowest languages, by model
| Model | Type | Median language, s per 1,000 words | Slowest language, s per 1,000 words | Slowest language | Slowest against median | Languages |
|---|---|---|---|---|---|---|
| Translated Lara 2 | LLM-based MT engine | 2 | 30.1 | Tamil | 15.1× | 97 |
| OpenAI gpt-oss-120b | open-weight LLM | 2.73 | 9.1 | Lao | 3.3× | 100 |
| Yandex Cloud Translate v2 | MT engine | 4.11 | 8.1 | Tatar | 2.0× | 83 |
| OpenAI GPT-5.6 Luna | LLM | 15.97 | 49 | Lao | 3.1× | 100 |
| xAI Grok 4.20 (non-reasoning) | LLM | 20.35 | 5,029.7 | Hausa | 247.1× | 100 |
| OpenAI GPT-4.1 | LLM | 28.09 | 137.4 | Sinhala | 4.9× | 100 |
| OpenAI GPT-5.6 Terra | LLM | 39.49 | 123.1 | Odia (Oriya) | 3.1× | 100 |
| Anthropic Claude Haiku 4.5 | LLM | 45.33 | 109.6 | Khmer | 2.4× | 100 |
| Swiss AI Apertus-8B-2509 | open-weight LLM | 50.82 | 258.1 | Igbo | 5.1× | 100 |
| Mistral Medium 3.1 | LLM | 53.48 | 1,756.1 | Amharic | 32.8× | 100 |
| MiniMax M2.5 | LLM | 58.39 | 708.7 | Lao | 12.1× | 100 |
| Cohere Command A Translate | LLM | 63.59 | 167.2 | Amharic | 2.6× | 100 |
| AI21 Jamba Large 1.7 | LLM | 64.03 | 149.7 | Sinhala | 2.3× | 100 |
| xAI Grok 4.20 (reasoning) | LLM | 65.02 | 231.3 | Yoruba | 3.6× | 100 |
| DeepSeek V3.2 | LLM | 65.27 | 143.5 | Welsh | 2.2× | 100 |
| Google Gemini 3.5 Flash | LLM | 67.63 | 101.4 | Odia (Oriya) | 1.5× | 100 |
| Mistral Large 3 | LLM | 78.68 | 528.9 | Odia (Oriya) | 6.7× | 100 |
| Anthropic Claude Sonnet 5 | LLM | 78.77 | 266.3 | Khmer | 3.4× | 100 |
| Anthropic Claude Fable 5 | LLM | 87.56 | 335.9 | Lao | 3.8× | 100 |
| Anthropic Claude Sonnet 4.6 | LLM | 89.72 | 178.5 | Odia (Oriya) | 2.0× | 100 |
| OpenAI GPT-5.5 | LLM | 90.39 | 360.8 | Odia (Oriya) | 4.0× | 100 |
| Anthropic Claude Opus 4.8 | LLM | 96.27 | 367.7 | Malayalam | 3.8× | 100 |
| Google Gemini 3.1 Pro Preview | LLM | 99.24 | 420.3 | Nepali | 4.2× | 100 |
| OpenAI GPT-5.6 Sol | LLM | 99.38 | 419.9 | Burmese | 4.2× | 100 |
| Google Gemma 4 | open-weight LLM | 224.41 | 449.1 | Tatar | 2.0× | 100 |
| z.ai GLM-5 | LLM | 237.07 | 5,929.6 | Odia (Oriya) | 25.0× | 100 |
| MoonshotAI Kimi K2.5 | LLM | 323.15 | 658.6 | Lao | 2.0× | 100 |
Translated without context in the request, each text timed separately.
Languages among the ten slowest and the ten fastest of each model
| Language | Time against the median language of the model | Models with it among the ten slowest | Models with it among the ten fastest | Models |
|---|---|---|---|---|
| Odia (Oriya) | 2.7 | 23 | 0 | 26 |
| Lao | 2.43 | 22 | 0 | 27 |
| Sinhala | 2.33 | 17 | 0 | 27 |
| Amharic | 2.28 | 19 | 0 | 27 |
| Khmer | 1.86 | 16 | 0 | 27 |
| Uyghur | 1.84 | 16 | 0 | 26 |
| Yiddish | 1.75 | 13 | 1 | 27 |
| Burmese | 1.69 | 14 | 0 | 27 |
| Malayalam | 1.64 | 11 | 0 | 27 |
| Kannada | 1.52 | 6 | 0 | 27 |
| Gujarati | 1.5 | 8 | 0 | 27 |
| Armenian | 1.49 | 7 | 0 | 27 |
| Punjabi | 1.49 | 10 | 0 | 27 |
| Telugu | 1.44 | 4 | 0 | 27 |
| Tamil | 1.4 | 4 | 0 | 27 |
| Pashto | 1.39 | 1 | 1 | 26 |
| Tatar | 1.39 | 7 | 0 | 27 |
| Yoruba | 1.36 | 6 | 0 | 26 |
| Georgian | 1.35 | 7 | 1 | 27 |
| Kyrgyz | 1.32 | 2 | 0 | 27 |
| Assamese | 1.3 | 1 | 1 | 26 |
| Irish | 1.24 | 4 | 1 | 27 |
| Scottish Gaelic | 1.22 | 0 | 1 | 27 |
| Kinyarwanda | 1.21 | 1 | 0 | 26 |
| Tajik | 1.21 | 2 | 0 | 27 |
| Turkmen | 1.2 | 3 | 0 | 26 |
| Mongolian | 1.19 | 1 | 0 | 27 |
| Zulu | 1.19 | 2 | 0 | 27 |
| Sesotho | 1.18 | 3 | 1 | 26 |
| Somali | 1.18 | 1 | 0 | 26 |
| Wolof | 1.17 | 4 | 1 | 26 |
| Malagasy | 1.16 | 2 | 0 | 27 |
| Maltese | 1.16 | 2 | 1 | 27 |
| Shona | 1.16 | 1 | 0 | 26 |
| Xhosa | 1.16 | 1 | 0 | 27 |
| Igbo | 1.15 | 1 | 0 | 26 |
| Greek | 1.14 | 1 | 0 | 27 |
| Kazakh | 1.14 | 1 | 0 | 26 |
| Nepali | 1.13 | 2 | 0 | 27 |
| Belarusian | 1.12 | 2 | 0 | 27 |
| Kurdish (Kurmanji) | 1.12 | 3 | 0 | 25 |
| Hausa | 1.11 | 2 | 0 | 26 |
| Marathi | 1.11 | 1 | 0 | 27 |
| Welsh | 1.09 | 3 | 0 | 27 |
| Icelandic | 1.02 | 1 | 0 | 27 |
| Thai | 1.02 | 2 | 2 | 27 |
| Uzbek | 1.02 | 0 | 0 | 27 |
| Basque | 1.01 | 0 | 0 | 27 |
| Bengali | 1.01 | 1 | 1 | 27 |
| Urdu | 1.01 | 0 | 1 | 27 |
| Albanian | 1 | 0 | 1 | 27 |
| Azerbaijani | 1 | 1 | 0 | 27 |
| Frisian | 0.99 | 0 | 0 | 25 |
| Hebrew | 0.98 | 1 | 1 | 27 |
| Latvian | 0.98 | 0 | 0 | 27 |
| Macedonian | 0.96 | 0 | 0 | 27 |
| Cebuano | 0.94 | 0 | 1 | 27 |
| Estonian | 0.94 | 1 | 0 | 27 |
| Swahili | 0.94 | 0 | 1 | 27 |
| Sundanese | 0.93 | 1 | 0 | 27 |
| Finnish | 0.92 | 0 | 0 | 27 |
| Lithuanian | 0.92 | 0 | 0 | 27 |
| Haitian Creole | 0.91 | 0 | 0 | 27 |
| Hindi | 0.91 | 2 | 2 | 27 |
| Hungarian | 0.91 | 1 | 0 | 27 |
| Bulgarian | 0.88 | 0 | 1 | 27 |
| Farsi | 0.86 | 0 | 4 | 27 |
| Slovenian | 0.86 | 0 | 0 | 27 |
| Tagalog | 0.85 | 0 | 1 | 27 |
| Slovak | 0.83 | 0 | 2 | 27 |
| Javanese | 0.82 | 0 | 4 | 27 |
| Arabic (MSA) | 0.81 | 0 | 2 | 27 |
| Turkish | 0.81 | 0 | 2 | 27 |
| Croatian | 0.8 | 0 | 1 | 27 |
| Ukrainian | 0.8 | 1 | 3 | 27 |
| Afrikaans | 0.79 | 0 | 1 | 27 |
| Polish | 0.78 | 0 | 2 | 27 |
| Romanian | 0.78 | 0 | 1 | 27 |
| Serbian | 0.78 | 1 | 5 | 27 |
| Bosnian | 0.77 | 0 | 1 | 27 |
| Danish | 0.77 | 0 | 2 | 27 |
| Czech | 0.74 | 0 | 2 | 27 |
| Japanese | 0.74 | 0 | 6 | 27 |
| Swedish | 0.74 | 0 | 6 | 27 |
| Vietnamese | 0.73 | 0 | 11 | 27 |
| Catalan | 0.72 | 0 | 3 | 27 |
| Korean | 0.71 | 0 | 8 | 27 |
| Norwegian | 0.71 | 0 | 4 | 26 |
| Malay | 0.69 | 0 | 6 | 27 |
| Dutch | 0.67 | 0 | 10 | 27 |
| Galician | 0.67 | 0 | 9 | 27 |
| Portuguese (European) | 0.65 | 0 | 17 | 27 |
| Russian | 0.64 | 0 | 14 | 27 |
| Spanish (European) | 0.64 | 0 | 19 | 27 |
| Indonesian | 0.62 | 0 | 18 | 27 |
| Italian | 0.62 | 0 | 17 | 27 |
| Chinese Simplified | 0.61 | 0 | 21 | 27 |
| French | 0.6 | 0 | 15 | 27 |
| German | 0.6 | 0 | 14 | 27 |
| Chinese Traditional | 0.59 | 0 | 19 | 26 |
Units — Time against the median language of the model: × the model median
The time per 1,000 words in a language divided by the median over the languages of the same model, the median across models. Models timed by text in 80 or more languages, translated without context in the request.