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Penaakulan DeepSeek V3 dan R1
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DeepSeek is an AI model developer whose releases include DeepSeek-R1 and later versioned model families.
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Technical claims, benchmark results, licenses and API features must be tied to the exact release.
The DeepSeek-R1 paper describes an RL-oriented reasoning approach; R1-Zero uses large-scale reinforcement learning without an initial supervised-fine-tuning stage, while R1 adds cold-start data and additional stages. The official R1 repository states MIT terms for that release’s code and model weights. In September 2026, DeepSeek announced V4.1-Flash as a 552B mixture-of-experts model with 8B active parameters for input and 16B for output, plus native visual understanding. Official API model metadata lists a 1,048,576-token context window for V4.1-Flash. These details belong to specific releases and should not be generalized across the model family.
Compare the R1 and R1-Zero training descriptions in the dated technical report.
Check the official R1 repository for the license stated for that release.
For V4.1-Flash, verify the architecture and active-parameter counts against its September 2026 release post.
Before integrating an API model, confirm the current model identifier and supported modalities in the official model list.
DeepSeek model names, routing and API specifications can change. Check current official release posts and model metadata before using a particular version. Treat company-reported benchmark comparisons as scoped to their stated tasks and conditions; do not infer that one model is universally cheapest or best.
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DeepSeek is an AI model developer whose releases include DeepSeek-R1 and later versioned model families. Technical claims, benchmark results, licenses and API features must be tied to the exact release.
DeepSeek’s dated release announcement introduced DeepSeek-R1 on January 20, 2025; this identifies the model without making a comparative price or performance claim.
DeepSeek’s September 10, 2026 release announcement describes V4.1-Flash as a 552B MoE model with 8B active parameters for input and 16B for output. These details apply to this version only.
The R1 paper and official repository are published by DeepSeek-AI; this is directly supported by the release rather than an inferred corporate-origin claim.
The primary report says R1-Zero used large-scale RL without supervised fine-tuning as a preliminary step, whereas R1 adds cold-start data and a multi-stage process.
The R1 technical report describes rule-based rewards for verifiable accuracy and format adherence in its reinforcement-learning stages; it does not establish that every DeepSeek model uses the same recipe.
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Penaakulan DeepSeek V3 dan R1
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