DeepSeek-Coder V2
Open Coding Frontier
DeepSeek's June 2024 coding model — 236B mixture-of-experts activating 21B per token, fine-tuned for code in 338 programming languages. At release, it beat Codestral 22B and matched closed- source GPT-4 Turbo on HumanEval. Open under a custom permissive license. The Coder V3 / V3.2 successors keep DeepSeek at the open coding frontier.
Intelligence
Below avg
Cost
Free
Open weights — self-host
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Pushed open-weight coding models past GPT-4 Turbo. By late 2024, serious enterprise coding deployments started defaulting to DeepSeek-Coder for self-hosted Copilot replacements.
Core Capabilities
Generative
Produces images, video, audio, or other media.
Coding
Strong real-world software engineering.
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Multimodal
Combines text, vision, and audio in one model.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Not available
Product / App
Not available
Open Source
Released
Enterprise
—
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- First open-weights coding model to beat GPT-4 Turbo, Claude 3 Opus, and Gemini 1.5 Pro on code + math
- 236B MoE / 21B active — derived from DeepSeek-V2 with 6T extra tokens of code/math pre-training
- HumanEval 90.2%, MBPP 76.2%, LiveCodeBench 43.4%, MATH 75.7% — frontier coding numbers at OSS price
- Programming language support jumped from 86 to 338; context extended from 16K to 128K
- 16B Lite variant (2.4B active) brings most of the quality to a single-GPU deployment
- Set the playbook later refined into DeepSeek V3 / R1 / V4 — coding-specialist via continued pre-training
Best use cases
- Self-hosted code-completion engines and IDE integrations
- Repository-level QA and refactor pipelines using open weights
- Multilingual code (338 languages) including niche legacy ones
- Cost-sensitive bulk inference for code review / lint suggestions
Tools to try
Not ideal for
- Frontier coding by mid-2025 — Claude Opus 4.5 / Sonnet 4.5 / DeepSeek V4 lead by wide margins
- Edge / single-consumer-GPU deployments at the 236B scale (use Lite)
- Non-coding chat — general dialogue is weaker than non-specialist DeepSeek models