{"id":21633,"date":"2026-09-28T19:29:00","date_gmt":"2026-09-28T17:29:00","guid":{"rendered":"https:\/\/www.lukaswojcik.com\/blog\/?p=21633"},"modified":"2026-10-06T12:19:40","modified_gmt":"2026-10-06T10:19:40","slug":"ai-modelle-deutsch-englisch-polnisch-vergleich-en","status":"publish","type":"post","link":"https:\/\/www.lukaswojcik.com\/blog\/en\/cloud-ai\/ai-modelle-deutsch-englisch-polnisch-vergleich-en\/","title":{"rendered":"Which AI Models Fit German, English and Polish?"},"content":{"rendered":"

A model may perform convincingly in English while requiring more corrections for Polish inflection or German technical terminology. A general leaderboard therefore cannot settle a three-language editorial decision. This comparison covers GPT, Claude and Gemini candidates documented on 6 October 2026 alongside DeepSeek V4.1 Flash and Qwen3.8-27B.<\/p>\n

\"Assess
A proposed evaluation workflow without claimed model measurements.<\/figcaption><\/figure>\n

Multilingual capability is not a quality guarantee<\/h2>\n

OpenAI and Anthropic document multilingual capabilities. This does not establish a quality ranking for German, English and Polish. Open checkpoints with strong general results also require language-specific evaluation. The chosen Qwen or DeepSeek checkpoint, runtime and quantization remain part of the test configuration. [1<\/a>, 3<\/a>, 7<\/a>, 8<\/a>]<\/p>\n

Different language tasks<\/h2>\n\n\n\n\n\n\n\n\n
Language<\/th>\nMaterial<\/th>\nAcceptance criterion<\/th>\n<\/tr>\n<\/thead>\n
DE<\/td>\nTechnical explanation and short FAQ<\/td>\nTerminology, clear references, neutral style<\/td>\n<\/tr>\n
EN<\/td>\nProduct description and summary<\/td>\nPrecision, natural wording, no additions<\/td>\n<\/tr>\n
PL<\/td>\nTechnical text and localised UI<\/td>\nInflection, vocabulary and natural syntax<\/td>\n<\/tr>\n
DE \/ EN \/ PL<\/td>\nSame factual core<\/td>\nIdentical figures, qualifications and claims<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A protocol without an English advantage<\/h2>\n

An editorial evaluation could contain twelve tasks per language. This is a proposed scope rather than an executed measurement. Each task has a fact checklist, approved terminology and an audience. Some tasks are written directly in the target language; others translate the same source. Language generation and translation quality can then receive separate assessment.<\/p>\n

Scoring covers meaning errors, omitted qualifications, grammar, terminology and unnecessary additions. Smooth wording earns no credit when a figure or uncertainty disappears. Correction minutes are also useful: a usable draft may require less editorial effort than an elegant but unreliable answer.<\/p>\n

Fair cloud and local comparison<\/h2>\n

GPT-6.1 Sol, Claude Sonnet 5.5 and Gemini 3.8 Flash form a practical cloud shortlist. Astra and Opus extend the evaluation for difficult tasks. An appropriate Qwen checkpoint is a candidate for local editorial work; DeepSeek extends the comparison through a suitable API or adequately equipped infrastructure. These are deployment hypotheses rather than declared language-quality winners.<\/p>\n

Local tests record context, chat template and quantization. Moving from Q8 to Q4 creates a new test condition. Ollama provides the execution layer; its memory and runtime conditions require separate treatment from model quality. [9<\/a>]<\/p>\n

Selection by task<\/h2>\n

A three-language editorial workflow does not need a single model for every piece of content. A cheaper candidate may handle short structured outputs while difficult texts receive a second review. Selection depends on errors per language and correction effort. Differences remain visible rather than disappearing inside an overall average.<\/p>\n

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Related tools<\/h2>\n