llm models
for Builders on a budget
DeepSeek V3.2-Exp vs Qwen 2.5 72B: which is better for Builders on a budget?
TL;DR for Builders on a budget
DeepSeek V3.2‑Exp delivers ultra‑long context and low costs for English/Chinese tasks, while Qwen 2.5 72B brings multilingual open weights at a slightly higher price.
Key Differences
| Feature | DeepSeek V3.2-Exp | Qwen 2.5 72B |
|---|---|---|
| Pricing per token (0.28 vs 0.07) | DeepSeek V3.2-Exp approach | Qwen 2.5 72B approach |
| Language coverage (EN/ZH vs 29+) | DeepSeek V3.2-Exp approach | Qwen 2.5 72B approach |
| Open-sourcing & license | DeepSeek V3.2-Exp approach | Qwen 2.5 72B approach |
| Context length (128k vs 32k+) | DeepSeek V3.2-Exp approach | Qwen 2.5 72B approach |
| Stability vs experimental | DeepSeek V3.2-Exp approach | Qwen 2.5 72B approach |
| Ecosystem maturity | DeepSeek V3.2-Exp approach | Qwen 2.5 72B approach |
Pricing Snapshot
DeepSeek: $0.028/M cached, $0.28/M uncached input & $0.42/M output; Qwen: $0.07/M input & $0.26/M output (2025‑10‑13)
Last reviewed: 2025-10-13
DeepSeek V3.2-Exp
Choose DeepSeek V3.2-Exp if:
- You need long (128k) context & math/coding
- You prioritize lowest cost
- Your tasks are in English or Chinese
Pros
- + Low cost per token
- + Open-source Mixture‑of‑Experts
- + 128k context
- + Strong math & coding abilities
- + JSON & function calling support
Cons
- - Experimental stability
- - Limited language support (EN/ZH)
- - Requires self-hosting for OSS
- - Less mature ecosystem
Qwen 2.5 72B
Choose Qwen 2.5 72B if:
- You want open weights & multilingual support
- You need reasoning & moderation modes
- You prefer a stable release
Pros
- + Open-source model weights
- + Function calling & structured output
- + Multilingual (29+ languages)
- + Low cost ($0.07/M input, $0.26/M output)
- + Reasoning & content moderation modes
Cons
- - Complex to fine-tune
- - Smaller community & docs
- - Less capable than top closed models
- - Compute heavy for local deployment
Also Consider
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