This week, 423 translation tasks were completed by 3 models. A sample of 2 articles was selected for multi-model blind evaluation and comparison. Best overall: deepseek-v4-pro with an average score of 9/10.
This Week’s Translation Statistics
| Model | Language | Translation Volume | Average Time | Average Quality Score |
|---|---|---|---|---|
| deepseek-v4-flash | en | 71 | 20.9s | Not evaluated |
| claude-sonnet-4.6 | ja | 211 | 36s | Not evaluated |
| passthrough | en | 135 | 0s | Not evaluated |
| deepseek-v4-flash:backtranslate | en | 4 | 27.8s | Not evaluated |
| native-english | en | 1 | - | Not evaluated |
| deepseek-v4-flash | zh | 1 | 8.3s | Not evaluated |
Sampled Comparative Evaluation
Evaluation 1: CUDA 15 Claims 40% MoE Performance Gain: Efficiency Breakthrough or Deepening Compute Monopoly?
| Model | Accuracy | Fluency | Terminology | Readability | Total Score |
|---|---|---|---|---|---|
| claude-sonnet-4.6 | 8 | 7 | 9 | 8 | 8 |
| deepseek-v4-pro | 9 | 8 | 9 | 9 | 9 |
| gpt-o3 | 9 | 9 | 8 | 8 | 8 |
claude-sonnet-4.6
✓ Terminology consistency is fairly strong; “混合エキスパート（MoE）” is translated accurately and used consistently throughout.
✗ Some sentences are slightly stiff; for example, “低レベルライブラリの最適化により” reads with an obvious trace of translation.
deepseek-v4-pro
✓ The structure is clear; “基盤ライブラリの最適化” is a natural expression, and the logical flow is smooth.
✗ The word “主張” in the title carries a slightly subjective tone, deviating somewhat from the neutral tone of “宣称” in the original.
gpt-o3
✓ The language is relatively natural; “性能向上は最大40％に達すると主張している” is close to standard Japanese usage.
✗ Terminology consistency is slightly weaker, with “訓練” and “トレーニング” used interchangeably rather than fully unified.
Conclusion: Version B is the best overall, with balanced accuracy, fluency, and readability. Version C is natural in language but slightly inconsistent in terminology. Version A is highly faithful but somewhat weaker in fluency. The differences among the three are not large, but B is more recommended.
Evaluation 2: The OpenAI-Anthropic Race Sparks Panic: Is AI Developing Too Fast?
| Model | Accuracy | Fluency | Terminology | Readability | Total Score |
|---|---|---|---|---|---|
| claude-sonnet-4.6 | 8 | 7 | 8 | 8 | 8 |
| gpt-o3 | 9 | 8 | 9 | 8 | 8 |
claude-sonnet-4.6
✓ Terminology is handled fairly professionally; expressions such as “マルチモーダル理解” and “質的な飛躍” accurately correspond to the technical meaning of the original.
✗ Some sentences are slightly lengthy; for example, the handling of “次世代の汎用人工知能を最終的に掌握するのは誰か？” in the first paragraph feels somewhat stiff and adds to the reading burden.
gpt-o3
✓ The overall expression is more concise and natural; for example, the handling of “所有権” and “コントロールされる” aligns with the original Chinese meaning and reads smoothly.
✗ Some paragraph transitions are slightly weak; for instance, the title translation “所有する者が未来を所有するのか？” in the third paragraph feels somewhat repetitive and does not better distinguish the tonal levels.
Conclusion: The overall quality of the two versions is close. gpt-o3 has a slight edge in accuracy and terminology consistency, while claude-sonnet-4.6 performs evenly in readability and structure. Selection is recommended based on the final use case.
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