DeepSeek-V3
Frontier open-weights 671B MoE base and chat model with multi-head latent attention (MLA) and dual-pipe training.
64k
Tokens
8.192k
Output limit
MoE (Multi-Head Latent Attention)
Model family
671B (37B active)
Total / Active
$0.14
Per 1M tokens
$0.28
Per 1M tokens
Model Overview
DeepSeek-V3 is an open-weights 671B parameter Mixture of Experts model activating 37B parameters per token. Featuring Multi-Head Latent Attention (MLA) and Multi-Token Prediction (MTP), it achieves state-of-the-art general language, coding, and mathematical benchmark performance with industry-low API pricing ($0.14/$0.28 per MTok).
Developer Implementation Notes
Unbeatable API price-performance ratio. Uses Multi-Token Prediction (MTP) for speculative decoding speedup.
Key Strengths
- MIT licensed open-weights model
- Ultra-low API cost ($0.14 input / $0.28 output per MTok)
- Excellent tool calling and coding benchmark scores
- MLA architecture drastically reduces KV cache memory consumption
Limitations & Boundaries
- 64k context window limit
- Local deployment of full 671B weights requires high-memory cluster
Capabilities & Modalities
Best Production Use Cases
- General enterprise LLM workloads at minimal cost
- Code generation and API translation
- Multilingual translation and summarization
Verified Benchmark Results
Standardized evaluations with methodology notes and authoritative citation links.
Pricing & Inference Cost Calculator
Token Cost Estimator – DeepSeek-V3
Calculate projected inference spend with prompt caching
Quick Workload Presets
$0.2800
2.00M input tokens
$0.1400
0.50M output tokens
$0.42
Avg: $0.00042 / req
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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.