DeepSeek-V3

DeepSeek-V3

Quick answer: DeepSeek-V3 is DeepSeek's December 2024 flagship Mixture of Experts model, scoring 88.5% on MMLU, 90.2% on MATH, 27.2% on LiveCodeBench, and 34.5% on BigCodeBench. At $0.27/$1.10 per 1M tokens with MIT license and open weights, it was the leading cost-efficient frontier model at launch — matching GPT-4o-class performance at a fraction of the price.

At a Glance

Where DeepSeek-V3 leads

• MIT license — fully open weights with no commercial restrictions
• $0.27/$1.10 per 1M — among the lowest prices for GPT-4-class performance
• 90.2% MATH — competitive with frontier models on mathematics
• 88.5% MMLU — matches GPT-4 Turbo at 5× lower cost
• 34.5% BigCodeBench — strong general coding capability
• MoE architecture: 671B total, ~37B active — efficient inference

Where it lags

• 27.2% LiveCodeBench — below GPT-4.1, o-series models
• 3.6% CyberGym — limited cybersecurity task performance
• 24.9% SimpleQA — moderate factual accuracy
• 128K context vs 1M for GPT-4.1 and Gemini 2.5 Pro
• Superseded by DeepSeek-V3-0324 and later versions

Best for: Cost-efficient frontier AI with open weights; self-hosting at GPT-4 capability level; high-volume pipelines where MIT licensing enables unrestricted deployment.

What DeepSeek-V3 Is

DeepSeek-V3 was released December 26, 2024, establishing DeepSeek as a leading provider of cost-efficient frontier AI. Its 671B MoE architecture (with ~37B active parameters at inference) delivers GPT-4-class performance at $0.27/$1.10 per 1M tokens — a breakthrough in cost/performance ratio that reshaped the competitive landscape.

The model's MATH score (90.2%) and MMLU score (88.5%) placed it among the top tier of models at launch, competitive with GPT-4o and Claude 3.5 Sonnet at 10× lower price. Its MIT license allows unrestricted commercial use, self-hosting, and fine-tuning without attribution requirements.

DeepSeek-V3 has since been updated (DeepSeek-V3-0324, then further versions), with each version improving benchmark scores while maintaining the same cost-efficient MoE architecture and open licensing approach.

Specifications

Pricing

Open weights also available for self-hosting at no per-token cost. Pricing per DeepSeek pricing page.

Context Window

DeepSeek-V3 has a 128,000-token context window — sufficient for most document analysis and coding tasks, though below the 1M-token context of GPT-4.1 and Gemini 2.5 Pro.

Public Benchmark Scores

DeepSeek-V3 vs Alternatives

DeepSeek-V3 vs GPT-4 Turbo: higher MMLU (88.5% vs 86.4%) and MATH (90.2% vs 72.6%) at 37× lower cost. The value case for DeepSeek-V3 is strong for any workload that fits within 128K context.

Use DeepSeek-V3 via API

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_DEEPSEEK_API_KEY",
    base_url="https://api.deepseek.com/v1",
)

response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[{"role": "user", "content": "Solve this maths problem step by step..."}],
)
print(response.choices[0].message.content)

Related Models & Benchmarks

• Compare: GPT-4.1 · Llama 4 Maverick · Qwen3 32B
• Benchmarks: MMLU · MATH · BigCodeBench · LiveCodeBench

Frequently Asked Questions

Specs and scores sourced from DeepSeek's official V3 release (December 2024) and Benchgen evaluations. Pricing cited to the DeepSeek platform pricing page. Last updated 2026-07-24.