DeepSeek +1 · released 2023-11-02
DeepSeek Coder 1.3B uses about 0.012 Wh – 0.012 Wh of electricity to answer a typical question. That places it 9 of 266 among the models we can measure, and every figure here is calculated the same way as every other model.
The capability figure of 62.54 is a composite over 58 benchmarks — Epoch AI's Capabilities Index, which folds in software engineering, agentic tool use, long-context comprehension and creative writing as well as science reasoning. It is one number standing for a lot of separate tests, so treat a small gap between two models as noise rather than a ranking.
No human head-to-head votes exist for this model. It has been benchmarked but not, as far as we can tell, put in front of people to compare against anything else — worth knowing before treating its scores as a verdict on its answers.
Inferred The maker publishes no size at all, so it is inferred from how capable the model is and when it was released. A wide bracket, not a figure. Every model on this site is calculated the same way, so the comparison holds even where the absolute value carries uncertainty. Our figures are good to roughly a factor of two to three, which is why we only ever state a difference of 5× or more.
Energy cannot be worked out without knowing how big a model is. Of the 3 models from DeepSeek +1 that we track, 0 can be assessed at all — the rest publish too little for anyone outside the company to calculate anything.
Capability and the roster come from Epoch AI, whose Capabilities Index is the 58-benchmark composite quoted above. Energy is calculated with EcoLogits and calibrated against laboratory measurements from ML.ENERGY. Human preference comes from LMArena. All four are public and free; the full method is here.
These figures are published under CC BY 4.0 — use them anywhere, including commercially, with credit to The AI Footprint.
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