Most developers know DeepSeek R1 is cheaper than GPT-4o. What most do not know is by exactly how much — and what you actually give up to get there.
We pulled the live pricing and benchmark data straight
from the InferenceBench leaderboard
-- which tracks 297 AI models across 19 providers,
updated daily - so the numbers you are reading are
current, not guesswork.
DeepSeek R1 costs $0.55/M input tokens and $2.19/M output. GPT-4o costs $2.50/M input and $10.00/M output. That is 4.5x cheaper on input and 4.6x cheaper on output. For reasoning-heavy tasks the quality is comparable. For real-time chat and multimodal workloads, GPT-4o still leads.
A team processing 10 million tokens per month pays around $35,000 on GPT-4o. The same workload on DeepSeek R1 runs about $16,500. That is $18,500 per month — or $222,000 per year
Quality — where it matters
On MMLU, DeepSeek V3.2 scores 88.5 versus GPT-4o's 87.2 — slightly ahead at roughly 10x lower input cost. On HumanEval code benchmarks, DeepSeek scores 82.6% versus GPT-4's 80.5%.
For reasoning, math, and code tasks, the quality gap most developers assume exists simply is not there.
The real trade-off — speed
DeepSeek R1 generates internal reasoning tokens before responding. Time to first token is 850ms or more. For batch workloads, that is acceptable. For real-time chat interfaces, it is not.
Choose DeepSeek R1 for:Code analysis, math, document parsing, RAG pipelines, batch workloads
Choose GPT-4o for:Real-time chat, voice interfaces, multimodal tasks, enterprise compliance workloads (SOC 2, HIPAA)
Self-hosting changes everything
GPT-4o has no self-hosting option. DeepSeek R1 is open-weight — you can run it on your own infrastructure. At high volume (above 50 million tokens per month), self-hosted R1 on H100 or A100 GPUs can fall significantly below any API rate.
Use the InferenceBench ROI calculator to find your exact break-even point between API and self-hosted.
Test before you decide
The InferenceBench Model Arena lets you send your actual prompts to two models simultaneously, read both responses without knowing which model wrote which, and vote for the better one. No SDK setup. Free to use.
The winner is frequently not the one you expected.
The cost difference is real. The quality difference,
for most workloads, is not.
The right model depends on what you are building —
not on which name carries more brand weight.
Test both on your actual prompts before you decide.
Compare both models on the InferenceBench Leaderboard →
Test them in the Model Arena →
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