DeepSeek, Qwen, Llama: how we choose between them on a per-workload basis
There is no best open-weights model in 2026. There are three good ones, each strongest at a different shape of work. Here is how we route between them.
Customers ask us 'which open-source model should we use?' and we always push back. The right question is 'which open-source model should I use for this specific workload?' because the answer is meaningfully different across the three models we serve in volume: Llama-3.3, Qwen-3, and DeepSeek-V3.
Llama-3.3-70B
Best at: English-first chat, RAG, instruction following, structured extraction with stable schemas, long-context recall up to 128k. The most predictable model in production — when something does not work as expected, it is usually a prompt issue, not a model issue.
Worst at: non-English (especially Chinese, Japanese, Korean), code generation in less common languages, deep multi-step reasoning where the chain of thought matters more than the final answer.
Qwen-3-72B
Best at: multilingual workloads (especially CJK), code generation across a wide range of languages, structured output with novel schemas, strong tool-calling ergonomics. The model that most often surprises us in a good way.
Worst at: very long context retrieval past 64k where it lags Llama, and English-first creative writing where Llama still has a stylistic edge.
DeepSeek-V3
Best at: reasoning chains, math, code at the cutting edge (algorithm design, debugging gnarly issues), and any workload where you want to see the model's working before the final answer. The model we reach for when the problem is hard enough that intermediate reasoning matters.
Worst at: high-throughput chat where the verbose reasoning output is a liability, simple classification (it overthinks), and latency-sensitive applications where you want the answer in the first tokens.
Llama for breadth, Qwen for languages and code, DeepSeek for reasoning. Three good answers. Pick by workload.
How we route in production
For customers who want a single model, we recommend Llama-3.3 as the safest default. For customers who want to optimize, we suggest a small router up front: classify the request type, send simple chat to Llama, send anything that looks like code or non-English to Qwen, send anything tagged as 'hard reasoning' or 'show your work' to DeepSeek.
The routing logic is small — a fast classifier or a few keyword rules — and the cost-quality improvement is substantial. We have customers running this setup at production scale with no observable downside compared to picking a single model.