This week, 357 translation tasks were completed by 4 models. A sample of 3 was selected for a multi-model blind comparison; the best overall: gpt-o3 (average score 8.3/10).
This Week's Translation Statistics
| Model | Language | Translation Volume | Average Time | Average Quality Score |
|---|---|---|---|---|
| deepseek-v4-flash | en | 71 | 44s | Not scored |
| claude-sonnet-4.6 | ja | 178 | 49.3s | Not scored |
| passthrough | en | 105 | 0s | Not scored |
| native-english | en | 1 | - | Not scored |
| deepseek-v4-flash | zh | 1 | 68.2s | Not scored |
| deepseek-v4-flash:backtranslate | en | 1 | 13.1s | Not scored |
Sampled Comparative Evaluation
Evaluation 1: Original Title
| Model | Accuracy | Fluency | Terminology | Readability | Total Score |
|---|---|---|---|---|---|
| claude-sonnet-4.6 | 9 | 8 | 8 | 9 | 8 |
| deepseek-v4-pro | 8 | 7 | 8 | 7 | 7 |
| gpt-o3 | 9 | 9 | 9 | 9 | 9 |
claude-sonnet-4.6
✓ Paragraph structure is clear and logical connections are natural; for example, 「Smoke評価は1日あたりわずか10問、各次元2問構成である」 accurately conveys the small-sample nature.
✗ The ending is truncated, resulting in incomplete content; for example, the final sentence 「誠実性評価の」 is clearly unfinished.
deepseek-v4-pro
✓ The title translation stays close to the original; for example, 「GPT-o3のコード実行が24.7ポイント急落」 directly corresponds to the core information.
✗ The content is presented in JSON array form, making reading coherence poor; for example, multiple short sentences repeating 「下落幅は」 feel stiff.
gpt-o3
✓ Terminology is consistent and expression is natural; for example, 「問題抽選による変動の可能性がより高い」 accurately and fluently conveys the causal analysis.
✗ Some long sentences are slightly verbose; for example, the explanatory paragraph on rising material constraints could be further condensed.
Conclusion: The three versions are close in overall quality. The gpt-o3 version is slightly better in fluency, terminology consistency, and readability; the claude version has clear structure but suffers from truncation; the deepseek version's JSON format affects reading experience. It is recommended to prioritize the gpt-o3 version.
Evaluation 2: Alibaba Leads $300 Million Investment in UniPat AI at $2.5 Billion Valuation; AI Evaluation Sector Sees First Major Capital Bet from a Tech Giant
| Model | Accuracy | Fluency | Terminology | Readability | Total Score |
|---|---|---|---|---|---|
| deepseek-v4-flash | 7 | 8 | 6 | 7 | 7 |
| deepseek-v4-pro | 8 | 8 | 8 | 7 | 8 |
| gpt-o3 | 8 | 7 | 8 | 8 | 8 |
deepseek-v4-flash
✓ Sentence transitions are fairly natural; 「with Tencent and Hongtai participating as follow-on investors」 clearly expresses the follow-on investment relationship.
✗ Terminology is inconsistent; 「Qwen lab」 and later 「Alibaba's Qwen lab」 are mixed, which can cause confusion.
deepseek-v4-pro
✓ Terminology is unified; 「Tongyi Lab」 is used consistently throughout.
✗ Content is truncated; 「The overall deal structure shows that the AI evaluation segment is gradually becoming a」 is not fully presented.
gpt-o3
✓ Paragraph logic is clear; 「Alibaba’s decision to lead the investment shows that...」 expresses the causal relationship well.
✗ Some expressions are slightly stiff; the translation of the 「Fact Reconstruction」 subheading is a bit rigid.
Conclusion: The three versions are close in overall quality. B and C are better than A in terminology consistency, and C is slightly better in readability, but B has obvious truncation. It is recommended to prioritize C or a repaired B.
Evaluation 3: Cognition Releases SWE-2, Using the Kimi K3 Base to Achieve a New Balance in Code Model Cost-Effectiveness
| Model | Accuracy | Fluency | Terminology | Readability | Total Score |
|---|---|---|---|---|---|
| claude-sonnet-4.6 | 9 | 8 | 9 | 8 | 8 |
| deepseek-v4-pro | 9 | 9 | 9 | 9 | 9 |
| gpt-o3 | 8 | 8 | 8 | 8 | 8 |
claude-sonnet-4.6
✓ Technical expressions such as 'linear cost penalty mechanism' are translated accurately; 「ペナルティ値はベースモデルのパレートフロンティアの局所的な傾きに基づいて調整される」 faithfully reproduces the original mechanism description.
✗ Some paragraph transitions are slightly stiff; for example, 「学習データ量は3倍に拡大され」 lacks a natural transition from the preceding text and has a slight translationese tone.
deepseek-v4-pro
✓ The title translation is natural; 「コストパフォーマンスに新たな均衡を実現」 both preserves the original meaning and conforms to Japanese expression conventions; body terminology such as 「Paretoフロンティア」 is used consistently and fluently.
✗ 「反復的バリデータ・フライホイール」 feels somewhat coined; the original 'iterative validation flywheel' emphasizes iterative validation more, and there is a slight trace of over-literal translation here.
gpt-o3
✓ 「費用対効果」 fits the Japanese business context, and 「コスト・性能のトレードオフ空間を変化させた」 is clearly expressed.
✗ The end of the content is clearly truncated; text after 「た」 is missing, which is an omission; some terms such as 「rolloutサービス」 are not adapted into Japanese, making them slightly jarring.
Conclusion: Version B is the best overall, with the best balance of accuracy, fluency, and readability; Versions A and C are next, with C losing more points due to truncation.
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