Kimi K3.1 Pro vs DeepSeek V4.1 Flash 1017 552B

Analysis

Kimi K3.1 Pro and DeepSeek V4.1 Flash 1017 came out six days apart in October 2026, and Moonshot put DeepSeek's model in its own launch table, so these two share more same-harness benchmark numbers than almost any other pair on this site. They sit at opposite ends of the frontier: Kimi is 2.8T parameters with 104B active, DeepSeek is 552B with 16B active, and Kimi needs about three times the memory. The question this page answers is what that extra hardware buys.

Start with memory. Kimi K3.1 Pro's native MXFP4 checkpoint is 1,561.3 GB and needs 1,749.1 GB at an 8k context. DeepSeek ships a 510.3 GB native release with no smaller build this site has verified, so it needs 571.6 GB, about a third as much. The gap barely moves with context, because both models cache very little per token: at the full 1,048,576-token window Kimi's KV cache is 51.5 GB (only 24 of its 93 layers cache) and DeepSeek's is 2.3 GB (its compressed sparse attention stores far fewer entries than tokens), for totals of 1,806.4 GB against 574.1 GB. That decides what hardware you need. DeepSeek fits four 180 GB B200s or even eight 80 GB H100s with the full window in use, at an estimated 76.8 and 59.3 tokens per second. Kimi needs a node of eight 288 GB B300s, where this site estimates 24.2 tokens per second at 8k. Neither fits a single GPU or a 512 GB Mac Studio. On Moonshot's own table, run at max reasoning effort for both, Kimi leads clearly on agentic coding (Terminal-Bench 4.0 65.7 against 42.3, SWE Marathon 51.7 against 40.0), on Humanity's Last Exam with tools (70.0 against 58.8) and on OSWorld V2.1 (79.6 against 69.3). DeepSeek wins some rows too: DeepSWE v1.1 (77.0 against 75.5), APEX-Agents (69.8 against 69.4) and Moonshot's internal exploit test (85.7 against 81.0). On GPQA-Diamond the table lists 94.2 against 92.2; this site's DeepSeek record carries 90.9, DeepSeek's September figure, because the 1017 card has no GPQA row. The biggest gap is reliability: on Moonshot's own Hallu Bench Hard, Kimi gets 8.3% of claims wrong against DeepSeek's 23.3%. Both take text and image input. Licensing favors DeepSeek: plain MIT, against Kimi's custom license, which requires a separate agreement for model-as-a-service businesses past $20M in 12-month revenue. All of these scores are Moonshot's; DeepSeek did not publish a matching table.

Bottom line: For most teams that self-host, DeepSeek V4.1 Flash 1017 is the better buy: about a third of the memory, eight Hopper or four Blackwell cards instead of a B300 node, about three and a half times the estimated decode speed on that same B300 node (84.5 against 24.2 tokens per second), and an MIT license. Pick Kimi K3.1 Pro when the work is long agentic coding or tool-heavy research, where Moonshot's table shows a 10 to 23 point lead, or when wrong answers are expensive, since its hallucination rate on Moonshot's test is under half of DeepSeek's. Neither runs on anything smaller than a multi-GPU server.