DeepSeek Coder V2
DeepSeek's specialized coding model. Excellent at code generation, debugging, and explanation.
DeepSeek Coder V2 is code AI model from DeepSeek, priced at €1.40 per 1M input tokens with a 128K tokens context window.
Examples
See what DeepSeek Coder V2 can generate
Write an efficient binary search function in Python that returns the index of a target value or -1 if not found
def binary_search(arr: list[int], target: int) -> int:
left, right = 0, len(arr) - 1
while left <= right:
mid = left + (right - left) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
left = mid + 1
else:
right = mid - 1
return -1
# Example usage:
# sorted_list = [1, 3, 5, 7, 9, 11, 13]
# print(binary_search(sorted_list, 7)) # Output: 3Create a React custom hook called useDebounce that debounces a value by a given delay
import { useState, useEffect } from 'react';
function useDebounce<T>(value: T, delay: number): T {
const [debouncedValue, setDebouncedValue] = useState<T>(value);
useEffect(() => {
const timer = setTimeout(() => {
setDebouncedValue(value);
}, delay);
return () => {
clearTimeout(timer);
};
}, [value, delay]);
return debouncedValue;
}
export default useDebounce;
// Usage:
// const debouncedSearch = useDebounce(searchTerm, 300);Pricing
API Integration
Use our OpenAI-compatible API to integrate DeepSeek Coder V2 into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
// Simple — just pass a string
const reply = await rw.run("deepseek-coder-v2", "Hello! What can you do?");
console.log(reply);
// With message history
const reply2 = await rw.run("deepseek-coder-v2", [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);
// Full response with usage info
const res = await rw.chat("deepseek-coder-v2", [
{ role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);Deep dive — DeepSeek's DeepSeek Coder V2
DeepSeek AI was founded in July 2023 in Hangzhou by Liang Wenfeng, who is also co-founder of the High-Flyer quantitative hedge fund. High-Flyer's GPU cluster (thousands of NVIDIA A100/H800 cards stockpiled before US export controls tightened) bootstrapped DeepSeek's training capacity. The lab is known globally for highly efficient training recipes documented in transparent technical reports. The DeepSeek-Coder line started with V1 (1.3B-33B dense models, November 2023). DeepSeek-Coder V2, released June 2024, brought MoE scaling — a 236B/21B-active model that matched or exceeded GPT-4 Turbo on code benchmarks at release. A 'Lite' 16B-active sibling was also released. DeepSeek's later DeepSeek-V3 (December 2024) and DeepSeek-R1 (January 2025) absorbed many DeepSeek-Coder design lessons. All releases use the permissive DeepSeek License with broad commercial-use rights.
Visit DeepSeek →DeepSeek-Coder V2 is a Sparse Mixture-of-Experts Transformer using the DeepSeekMoE architecture: 160 fine-grained experts plus 2 shared 'always-on' experts per MoE layer, with top-6 routing among the 160. This fine-grained + shared design (introduced in the DeepSeek-MoE paper) gives better expert specialisation than coarse 8x or 16x MoEs at similar active-parameter budgets. The model has 60 layers and 5,120 hidden size, uses Multi-head Latent Attention (MLA) for memory-efficient KV cache, RoPE position embeddings with a 128K context extension, and SwiGLU activations. The 100,000-token DeepSeek BPE tokeniser is shared across the DeepSeek family. Training began from the DeepSeek-V2 base (8.1T tokens pretraining) and added 6T more tokens of code, code-related natural language and math reasoning data. Programming language coverage is 338 languages — broader than any contemporaneous open code model. The model supports fill-in-the-middle via `<|fim_begin|>`, `<|fim_hole|>` and `<|fim_end|>` tokens. Post-training uses supervised fine-tuning plus DeepSeek's Group Relative Policy Optimisation (GRPO) RL method. A 16B-active 'Lite' variant (DeepSeek-Coder-V2-Lite) is also released. Open weights are released under the permissive DeepSeek License Agreement, which allows commercial use.
- Parameters
- 236B total, 21B active per token (160 fine-grained experts + 2 shared, top-6 routing)
- Context
- 128K tokens
- 236B total / 21B active — MoE cheaper to serve than 200B+ dense alternatives
- Matched or exceeded GPT-4 Turbo on HumanEval, MBPP, LiveCodeBench at release
- Supports 338 programming languages — broader than Codestral, CodeLlama or any peer at release
- 128K context for whole-repo and large-file reasoning
- Native fill-in-the-middle (FIM) tokens for IDE completion
- Multi-head Latent Attention for memory-efficient inference
- Strong general reasoning and math from the V2 base — not just code
- Open weights under permissive DeepSeek License (commercial use allowed)
- Best for: high-quality code generation, repository-scale reasoning, polyglot codebases, self-hosted production code AI.
Continued pretraining from DeepSeek-V2 base (8.1T tokens). Added 6T tokens of code-and-math-heavy data: code repositories across 338 languages (60% of the additional mix), code-related natural language (10%), math reasoning data (10%) and web data (20%). Knowledge cutoff November 2023. Post-training is supervised fine-tuning plus GRPO RL on reasoning and code benchmarks.
License: DeepSeek License Agreement. Permissive commercial license with standard acceptable-use restrictions. No revenue threshold or separate-licence requirement — among the most liberal frontier-scale open-weights licenses.
Known limitations
- 236B total weights need ~470GB FP16 (~120GB INT4) — heavy for self-hosting
- Superseded on many benchmarks by DeepSeek-V3 and DeepSeek-V3-Coder successors
- Latency higher than smaller dense code models for short completion calls
- MoE routing means fewer inference engines support it cleanly
- No vision modality
- Filters politically sensitive Chinese topics consistent with regulations
Frequently asked questions
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