October 2026 changed the open-model race in a single week. Three strong contenders now offer a 1-million-token context window, but they suit very different budgets, workloads and setups. This is a condensed explainer based on the full comparison on NeurAITool.
Read the full NeurAITool comparison here: DeepSeek vs Qwen vs Kimi: Best Open AI Model in 2026
The three models at a glance
DeepSeek V4.1-Flash-1017, from DeepSeek, is a 552B mixture-of-experts model with a 1M-token context. Its weights are open under an MIT licence.
Qwen3.8, from Alibaba, spans a flagship tier of 2.4T total parameters (about 95B active) down to much smaller models, all with a 1M-token context at the top end. Its smaller sizes are Apache 2.0.
Kimi K3.1 Pro, from Moonshot AI, is a 2.8T model (104B active) with a 1M-token context. It is available now through the API, while its full weights are still to come.
What they cost
Prices are per 1 million tokens, input / output:
- DeepSeek V4.1-Flash-1017: $0.15 / $0.60 off-peak.
- Qwen3.8-Max API: about $2 / $6.
- Kimi K3.1 Pro: $2 / $10.
DeepSeek is therefore far cheaper for high-volume work, while Kimi and Qwen sit in a similar flagship price range.
A key benchmark caveat
Treat headline scores with care. Most are vendor-reported figures, or come from a single independent index, and different test setups can produce different results. Run your own tasks before committing to a model for production use.
That caveat matters most for Kimi: Moonshot has promised its full weights by 4 November 2026, but they had not yet been published at the time of the original article. Until then, Kimi is best described as soon-to-be-open rather than fully open today.
The verdict
- Kimi K3.1 Pro for capability: the closest of the three to the closed frontier on coding and agent-style work.
- DeepSeek V4.1-Flash-1017 for value: the cheapest by a wide margin, with MIT weights you can use freely.
- Qwen3.8 for local use and ecosystem: the only family here that stretches from a laptop-friendly model to a flagship, backed by a large fine-tuning community.
In short, there is no single winner for everyone. Pick Kimi for maximum capability, DeepSeek for value, and Qwen for local deployment and ecosystem breadth.
This condensed version is based on the original NeurAITool article: https://www.neuraitool.com/2026/10/deepseek-vs-qwen-vs-kimi-best-open-ai.html
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