ModelVerdict

Language gap

Language gap: how much worse is a model outside English

Same kinds of invoices, same instructions, different language. The gap shows how much accuracy a model loses on documents in another language compared with English ones.

ModelCorrect fields 82% – 100%EnglishGermanGapError-free documents (German / English)
Qwen3.8 Flash
Qwen
82.4%93.4%+11.0 pp87% / 57%
Qwen3.8 Flash
Qwen
82.4%91.3%+9.0 pp77% / 57%
Qwen3.8 Flash
Qwen
82.4%90.5%+8.1 pp82% / 57%
Qwen3.8 Flash
Qwen
82.4%88.2%+5.8 pp78% / 57%
Mistral Small 4
Mistral
95.6%97.5%+1.9 pp77% / 58%
Gemini 3.5 Flash Lite
Google
99.2%100.0%+0.8 pp100% / 92%
DeepSeek V4.1 Flash
DeepSeek
99.4%99.9%+0.5 pp99% / 93%
Gemini 3.5 Flash Lite
Google
99.2%99.6%+0.4 pp96% / 92%
Mistral Medium 3.5
Mistral
97.8%98.1%+0.4 pp81% / 79%
Mistral Medium 3.5
Mistral
97.8%98.0%+0.2 pp83% / 79%
DeepSeek V4.1 Flash
DeepSeek
99.4%99.6%+0.2 pp95% / 93%
Claude Sonnet 5
Anthropic
99.8%100.0%+0.2 pp100% / 99%
Gemini 3.5 Flash Lite
Google
99.2%99.4%+0.2 pp93% / 92%
Gemini 3.8 Flash
Google
99.8%99.9%+0.1 pp99% / 98%
Gemini 3.8 Flash
Google
99.8%99.9%+0.1 pp99% / 98%
DeepSeek V4.1 Flash
DeepSeek
99.4%99.4%−0.0 pp93% / 93%
GPT-6 Sol
OpenAI
99.9%99.9%−0.0 pp99% / 99%
GPT-6 Sol
OpenAI
99.9%99.9%−0.0 pp99% / 99%
GPT-6 Sol
OpenAI
99.9%99.9%−0.1 pp99% / 99%
Mistral Medium 3.5
Mistral
97.8%97.6%−0.1 pp75% / 79%
Mistral Small 4
Mistral
95.6%95.4%−0.2 pp56% / 58%
Mistral Small 4
Mistral
95.6%95.2%−0.4 pp65% / 58%
GPT-6 Luna
OpenAI
99.7%99.3%−0.4 pp94% / 97%
GPT-6 Luna
OpenAI
99.7%99.2%−0.5 pp91% / 97%
Gemini 3.5 Flash Lite
Google
99.2%98.6%−0.6 pp85% / 92%
GPT-6 Luna
OpenAI
99.7%98.9%−0.8 pp88% / 97%
GPT-6 Luna
OpenAI
99.7%98.9%−0.8 pp88% / 97%
GPT-6 Sol
OpenAI
99.9%99.0%−0.9 pp89% / 99%
Claude Sonnet 5
Anthropic
99.8%98.8%−1.0 pp87% / 99%
Mistral Small 4
Mistral
95.6%94.5%−1.1 pp53% / 58%
Claude Sonnet 5
Anthropic
99.8%98.6%−1.2 pp96% / 99%
DeepSeek V4.1 Flash
DeepSeek
99.4%98.0%−1.3 pp78% / 93%
Gemma 4 31B
Google
97.6%96.1%−1.5 pp67% / 81%
Gemma 4 31B
Google
97.6%95.2%−2.4 pp67% / 81%
Gemini 3.8 Flash
Google
99.8%97.3%−2.5 pp97% / 98%
Mistral Medium 3.5
Mistral
97.8%94.9%−2.9 pp67% / 79%
Gemma 4 31B
Google
97.6%94.7%−3.0 pp54% / 81%
Gemini 3.8 Flash
Google
99.8%95.9%−3.9 pp95% / 98%
Gemma 4 31B
Google
97.6%93.3%−4.3 pp52% / 81%
Claude Sonnet 5
Anthropic
99.8%93.4%−6.5 pp77% / 99%

Only fields present in both versions of the document are compared (Supplier, Supplier ID (EIN, IČO…), Invoice number, Payment reference, Invoice date, Due date, Net by tax rate, Tax by rate, Total, Currency, Bank account). US invoices have no VAT ID or tax point, so those fields are left out.