Lesson 93 of 1570
DeepSeek V3.5 coding
DeepSeek V3.5 is the open-weights model that keeps punching above its weight class on coding benchmarks at a fraction of the cost.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The price-disruptor keeps going
- 2DeepSeek V3.5
- 3open weights
- 4coding
Concept cluster
Terms to connect while reading
Section 1
The price-disruptor keeps going
DeepSeek V3.5 continues the pattern: frontier-adjacent quality at a reported price about 10x cheaper than US labs. For coding in particular, benchmarks approach flagship GPT and Claude Sonnet results on selected tasks.
- MoE architecture with small active parameter count
- Open weights under a permissive license
- Cheap hosted API from DeepSeek directly
- Strong in Python, TypeScript, Go, Rust
Compare the options
| Factor | DeepSeek V3.5 | Claude Sonnet 4.6 | GPT-5.4 mini |
|---|---|---|---|
| Coding benchmark tier | High | High | High |
| Price per M output | ~$1 | $15 | $4.50 |
| Open weights | Yes | No | No |
| Data trust | Varies by buyer | Enterprise-grade | Enterprise-grade |
OpenAI-compatible. Flip the base URL and try it.
client = OpenAI(api_key=os.environ["DEEPSEEK_API_KEY"], base_url="https://api.deepseek.com")
resp = client.chat.completions.create(model="deepseek-chat", messages=msgs)Providers that host DeepSeek in the US
Together, Fireworks, and Novita all host DeepSeek V3.5 weights in US data centers. You keep the price advantage without the data-residency headache.
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