The cost to use this model is around $0.435 and $0.87 per million tokens (the basic unit of information a model can handle) of input and output. So the pricing structure remains the same, but what changed is the weights underneath.
So the widely circulated scores describe a build DeepSeek considered unfinished. Nobody outside the company has independently benchmarked 0813 yet.
What DeepSeek’s table claimsThe company published a comparison across 10 agent benchmarks. On the eight where Fable 5 or another model has the advantage, the gaps are small.
Average Fable 5’s relative lead across the benchmarks and you get 5.3%. Strip out Humanity’s Last Exam without tools—where DeepSeek scores 42.7 against 53.3, a 10.6% gap that skews everything—and the remaining rows average 2.8%.
Pricing is public on both sides, and this is where the comparison stops being close. Fable 5 runs $10 per million input tokens and $50 per million output. V4 Pro runs $0.435 and $0.87, with cached input at $0.003625. On blended rates that’s $30 against $0.65—roughly 46 times, or 4,600% of the cost. That’s the kind of spread that matters when you’re running a business and using AI tools at scale.
DeepSeek scored these itself, on infrastructure it hasn’t released. Its July note specified DeepSeek Harness minimal mode “to be released soon,” running at max effort with high creativity. Two of the ten benchmarks, DSBench-FullStack and DSBench-Hard, are internal test sets with no public leaderboard to check them against.
