DeepSeek-R1 vs DeepSeek-V3
Detailed performance comparison across SWE-bench Verified, LiveCodeBench, AIME, GPQA Diamond, pricing per million tokens, and hardware requirements.
Open-weights frontier reasoning model trained via large-scale reinforcement learning without supervised cold start.
Frontier open-weights 671B MoE base and chat model with multi-head latent attention (MLA) and dual-pipe training.
Key Strengths & Architectural Trade-offs
Direct synthesis of practical production advantages and limitations.
DeepSeek-R1
- Open-weights under permissive MIT license
- Uncensored and transparent chain-of-thought token visibility
- Extremely cheap API pricing ($0.55 / $2.19 per MTok)
- Full 671B model requires massive multi-GPU hardware for local hosting
- 64k context window is smaller than Claude/Gemini
DeepSeek-V3
- MIT licensed open-weights model
- Ultra-low API cost ($0.14 input / $0.28 output per MTok)
- Excellent tool calling and coding benchmark scores
- 64k context window limit
- Local deployment of full 671B weights requires high-memory cluster
Recommended Use Cases by Workload
- •Private on-premise deep reasoning & math evaluation
- •Self-hosted coding and security audit pipelines
- •Synthetic training data generation for smaller models
- •General enterprise LLM workloads at minimal cost
- •Code generation and API translation
- •Multilingual translation and summarization
Data Accuracy & Verification Notice
AI model specifications, pricing records, and benchmark metrics published on this platform are compiled directly from authoritative sources (official provider documentation, research papers, and verified evaluation harnesses). Benchmark results reflect specific test harnesses and prompting methodologies; scores are not directly comparable across differing evaluation setups.
Select 2 to 5 Models to Compare
Compare benchmark scores, architecture, context limits, pricing, and hardware requirements side-by-side.
Choose from Model Catalog (18 models)
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