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DeepSeek Coder 33B Instruct

DeepSeek's 33B-parameter code model that matches GPT-3.5 Turbo on code benchmarks, built for complex code generation and instruction-following across multiple languages.

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about

The largest model in the DeepSeek Coder series was the first open-source model to beat GPT-3.5 Turbo on competitive programming benchmarks, scoring 27.8% pass@1 on LeetCode Contest problems. It outperforms CodeLlama-34B by 7.9% on HumanEval Python despite sharing a similar parameter count, trained on 2 trillion tokens with an additional 200B-token context extension phase.

Licensedeepseek
Context window(in thousands)16384

Use cases for DeepSeek Coder 33B Instruct

  1. Competitive programming: As the first open-source model to beat GPT-3.5 Turbo on LeetCode benchmarks at 27.8% pass@1, it handles algorithmic problem-solving with optimized solutions across difficulty levels.
  2. Multi-language code generation: Averaging 50.3% across 8 languages on HumanEval multilingual, it generates production code in Python, Java, C++, TypeScript, and others from natural language specifications.
  3. Project-level code completion: The 16K context window and training on 2 trillion tokens support multi-file code understanding, enabling it to complete functions with awareness of imports, types, and dependencies across a project.

Quality

Arena EloN/A
MMLUN/A
MT BenchN/A

DeepSeek Coder 33B Instruct scores 79.3% on HumanEval and 50.3% on HumanEval multilingual (8-language average), outperforming Code Llama 70B Instruct (67.8% HumanEval) on the same sheet despite being half the size. It was the first open-source model to beat GPT-3.5 Turbo on LeetCode benchmarks at 27.8% pass@1. Standard MMLU is not published for this code-focused model.

Claude-Opus-4-6

1501

GLM-5

1456

gpt-5.1

1455

Kimi-K2.5

1454

gpt-5.2

1440

pricing

The cost of running DeepSeek Coder 33B with Telnyx Inference is $0.0003 per 1,000 tokens. Generating code for 1,000,000 programming tasks at 1,000 tokens each would cost $300, half the cost of Code Llama 70B ($600) while outperforming it on HumanEval (79.3% vs 67.8%).

What's Twitter saying?

  • Developers on Reddit and Hacker News praise DeepSeek Coder 33B for exceptional real-world coding performance, often ranking it superior to CodeLlama 70B and nearly matching GPT-4, with strong local deployment appeal.
  • Benchmarks highlight state-of-the-art results on HumanEval (81%), MultiPL-E, MBPP, DS-1000, and APPS, outperforming other open-source code models.
  • Community notes it runs efficiently on a single RTX 3090 when 4-bit quantized, though some benchmarks rank it oddly below smaller variants like 6.7B.

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faqs

Is DeepSeek an instruct model?

DeepSeek Coder 33B Instruct is the instruction-tuned variant of the DeepSeek Coder family, fine-tuned for following natural language instructions to generate code. The base model and instruct variant serve different use cases.

Which DeepSeek model is best for coding?

DeepSeek Coder 33B Instruct is the strongest model in the original DeepSeek Coder series for code generation tasks. For newer alternatives, DeepSeek V2 and V3 offer improved performance, available on Hugging Face.

How to use DeepSeek for coding?

DeepSeek Coder 33B can be accessed through hosted inference providers or deployed locally using the Hugging Face Transformers library. Telnyx offers API access for production code generation workloads.

Which programming language is DeepSeek written in?

DeepSeek Coder models are implemented in Python using PyTorch, but they generate code in multiple programming languages including Python, Java, C++, JavaScript, and many others.

Is DeepSeek Coder 33B good?

DeepSeek Coder 33B Instruct performs well on code generation benchmarks, competing with Code Llama 34B at the time of release. It handles code completion, generation, and infilling across multiple languages through hosted inference platforms.

Can I use DeepSeek Coder for free?

DeepSeek Coder 33B is released under a permissive license that allows free commercial use. Weights are available on Hugging Face for self-hosting, and hosted inference is available through various providers.

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