How the Meaning of Accuracy Shifts Between DeepSeek Claude and GPT-4o Models
Accuracy's meaning varies between DeepSeek Claude and GPT-4o models depending on the task. Synthetic benchmarks and real-world scenarios reveal differences in how these models perform.
The handling of hallucinations differs notably. DeepSeek-V3 tends to provide confidently incorrect answers for niche API documentation not well-represented in its training data, inventing method names that sound plausible but do not exist. Claude 3.5 Sonnet, however, is more likely to admit uncertainty or offer caveats in such situations. Claude still edges out DeepSeek in raw factual precision for obscure data. Logic-heavy tasks, however, show less disparity, with DeepSeek-V3 performing almost equally to GPT-4o and occasionally surpassing Claude, especially in puzzles requiring step-by-step deduction.
Performance across specific categories breaks down as follows:
Coding and Syntax
- Claude 3.5 Sonnet excels at "one-shot" accuracy, understanding prompt intent better than other models, delivering correct code on the first try.
- DeepSeek-V3 is efficient with boilerplate and algorithms but demands precise prompting; vague instructions significantly reduce output accuracy.
- GPT-4o offers a middle-ground performance but shows signs of stagnation, with a more generic approach.
Reasoning and Mathematics
- DeepSeek-V3 demonstrates surprising accuracy in mathematical proofs, handling multi-step reasoning with rigor rivaling specialized "o1" models.
- GPT-4o often takes shortcuts in its reasoning, leading to errors in long-form arithmetic problems.
- Claude 3.5 Sonnet strictly follows instructions, reliably complying with constraints like library avoidance or response length limits.
- DeepSeek-V3 occasionally ignores formatting constraints in system prompts despite providing correct answers.
Benchmarking methods should move beyond generic prompts like "Hello World." Introducing "contradiction traps" provides a more accurate measure. For example:
markdown
The city of X is known for its blue mountains, but it is actually located in a flat desert.
Based on this, describe the scenery of city X without mentioning the word 'flat'.
GPT-4o sometimes fails by mentioning the desert's flatness or hallucinating about blue mountains. Claude typically adheres to constraints, while DeepSeek's performance is mixed but often creative in phrasing.
"Accuracy" proves a misleading metric; "reliability" is more appropriate. DeepSeek serves those adept at guiding it, while Claude offers consistent correctness without oversight.
All Replies （2）
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I want to try this tonight, but first I’ll explicitly test DeepSeek-V3’s hallucination threshold by asking it to generate a niche API method name for a hypothetical but obscure third-party SDK—then compare its confidence level to Claude 3.5 Sonnet’s more cautious responses. You missed the latency trade-off though, especially when using vLLM for the serving part.
God, I'm so relieved to find this. Bookmarked it for later, but does the 4.2% gap actually matter? I ran a set of 50 complex Python logic puzzles involving nested loops and state management and found that DeepSeek-V3 matched GPT-4o almost one-to-one, so the small difference might not be significant in practical applications.