DeepSeek R2 just went open-source and it's matching GPT-4o on 9 of 12 benchmarks — for literally $0 in API costs

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open-source benchmark cost-reduction local-ai fine-tuning deepseek gpt-4o

Summary

DeepSeek R2, a new open-source model, matches GPT-4o on nine of twelve benchmarks while running locally on a single A100 for zero API cost, potentially transforming the economics of AI deployment.

The benchmark sheet dropped this morning and people are losing it in the ML community. **What DeepSeek R2 scores:** •MMLU: 90.8 (GPT-4o: 88.7) •HumanEval coding: 93.2 — new open-source SOTA •MATH reasoning: 88.9 •Runs on a single A100, fully local, zero API costs Hugging Face hit 300k downloads in the first 6 hours. The open-source community is already fine-tuning it for medical, legal, and finance use cases. The cost gap is now absurd: GPT-4o charges \~$0.015/1k tokens. DeepSeek local = **$0.00**. For high-volume use cases, this is a 50x cost reduction overnight. The 'closed model moat' argument is officially dead. Every startup bleeding $40k/month on OpenAI has a real migration path now.
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DeepSeek has made its V4-Pro API price cut of 75% permanent, with per-million cached input tokens at just $0.003625 and output tokens at $0.87, about 34 times cheaper than OpenAI's GPT-5.5. The model has 1.6 trillion parameters but requires only 49 billion active parameters, supports a 1-million-token context, and leads in coding and reasoning benchmark tests.