Alibaba Qwen vs DeepSeek

Model line-ups

Qwen3.8 arrived in August 2026. Its model cards describe a hybrid architecture that interleaves Gated DeltaNet linear-attention layers with gated attention, in three sizes. Qwen3.8-27B is a dense vision-language model with thinking mode on by default. Qwen3.8-Flash-Next is a sparse model listed at 125B parameters with 6B activated, plus a 51B n-gram embedding and a 4B multi-token-prediction module. Qwen3.8-2.4T-A95B activates 95B of its 2.4 trillion parameters. Alibaba states that the hosted Qwen3.8-Max is "the official version based on Qwen3.8-2.4T-A95B with more features", including vision input, a non-thinking mode, one-million-token context by default and built-in tools, so the downloadable weights are not identical to the product.

DeepSeek's cadence in 2026 was a V4 preview on 24 April, a public-beta V4-Flash update on 31 July, a generally available V4-Pro on 13 August and V4.1-Flash on 10 September. The V4 technical report (April 2026) gives V4-Pro as 1.6T parameters with 49B activated and V4-Flash as 284B with 13B activated; the August V4-Pro-0813 model card lists 1.7T. V4.1-Flash is a new design, a 40-layer causal encoder-decoder that accepts images and text, and the retired V4-Flash API names now route to it.

Licences

This is the clearest dividing line. Both current DeepSeek models are MIT-licensed, which permits commercial use, modification and redistribution with only a notice requirement. Qwen uses three licences across one generation. Qwen3.8-27B is Apache 2.0. Qwen3.8-Flash-Next uses the Qwen Community License 1.0, which requires a separate commercial licence for companies running a model-as-a-service or AI work-assistant business, and requires the model name to be displayed prominently in products exceeding 100 million monthly active users or US$20 million in monthly revenue. The 2.4T model carries a licence tagged "qwen3.8-max" whose text could not be opened for this comparison; anyone planning commercial use should read it first.

For most businesses the practical reading is that DeepSeek's weights and Qwen3.8-27B can be used without negotiation, while Qwen's larger weights need legal review, especially for anyone reselling inference.

API pricing

List prices as of 6 October 2026, per million tokens. On Alibaba Cloud Model Studio's Singapore deployment, qwen3.8-max is $2 input and $6 output; qwen3.7-plus is $0.40 and $1.60 up to 256K input tokens, rising to $1.20 and $4.80 beyond; qwen3.8-flash is $0.15 and $0.47. DeepSeek introduced peak and off-peak pricing in August 2026. Peak hours are 01:00–04:00 and 06:00–10:00 UTC on weekdays. At peak, deepseek-v4-pro is $1.32 input on a cache miss and $3.96 output, and deepseek-flash is $0.30 and $1.20; off-peak prices are half of those.

So DeepSeek's flagship is about a third cheaper than Qwen's at peak and about two-thirds cheaper off-peak, while Qwen's flash tier undercuts DeepSeek's at peak and roughly matches it off-peak. DeepSeek's cache-hit input prices are far lower again, which favours agent loops that resend a long, stable prefix. Unit prices are not cost per task: a model that reasons at length before answering generates more billable tokens for the same job. See AI model pricing for how to compare on a per-task basis.

Context and efficiency

Both families now treat a one-million-token context as standard. DeepSeek's V4 report attributes this to architecture: hybrid compressed sparse attention that, by its account, needs 27% of the single-token inference FLOPs and 10% of the KV cache of its predecessor at million-token length. The V4.1-Flash card claims a further reduction to roughly a quarter of V4-Flash's KV cache. Qwen3.8 open weights are native to 262,144 tokens and extensible to about one million. All of these are vendor-reported.

Benchmarks and fine-tuning

Model-card scores should be read as claims. On Terminal Bench 2.1, Alibaba reports 73.0 for Qwen3.8-27B and DeepSeek reports 87.9 for V4-Pro-0813 and 90.6 for V4.1-Flash at maximum reasoning effort; Alibaba reports 86.6 on Terminal Bench for the 2.4T model. On SWE-bench Pro, Alibaba reports 61.7, 62.5 and 67.7 for its three sizes. Harnesses, effort settings and benchmark versions differ, and no independent evaluation was reviewed here.

For teams that want to adapt a model, size settles the matter. Qwen3.8-27B is the only current model from either vendor that is dense, permissively licensed and small enough for common fine-tuning and LoRA workflows. Its card recommends SGLang and vLLM for serving. DeepSeek's V4.1-Flash activates few parameters per token, but its 552B backbone still has to be held in memory, which puts it in multi-GPU server territory.