大模型网摘DeepSeek V4.1-Flash-1017 vs Kimi K3.1 Pro: Choosing Between Two 1M-Context Models
October turned the 1M-token context window into table stakes for open-weight labs: DeepSeek's current Flash tier is served by the V4.1-Flash-1017 snapshot, while Moonshot AI shipped the 3T-class Kimi K3.1 Pro on October 23. Both swallow a million tokens in one go, but they differ sharply in architecture, billing and strengths — worth putting side by side.
Specs: Two Different Routes
V4.1-Flash-1017 is a 552B-backbone MoE model. According to the official technical report (arXiv:2609.19969), it uses a Causal Encoder-Decoder (CED) architecture: decoding activates 16B parameters per token while prefill needs only 8B — a clear fit for the input-heavy workloads agents generate. Combined with CSA2 cross-layer KV reuse and FP4 KV caching, the global KV cache footprint drops to 890 bytes per token, roughly a quarter of V4-Flash's, and SWA Bounded Replay cuts the persistent cache to about one-eighth. The model was pretrained on 45T multimodal tokens and accepts image input; V4-Pro-0813 does not.
K3.1 Pro takes the tonnage route: 3T-class open weights, a post-training scale-up of K3 (2.8T), a 1,048,576-token context, and thinking-only operation that cannot be switched off. The companion K3.1 Pro Highspeed keeps identical capabilities with roughly 2-3x output speed at about 2x the quota usage. Full weights are promised by November 4.
Benchmarks: Different Coordinate Systems
The Flash tier scores 77.0% Pass@1 on DeepSWE v1.1 at an average cost of about $0.20 per task. K3.1 Pro posts 65.7% on Terminal-Bench 4.0, where GPT-6 Astra scores 58.2% and Claude Opus 5.5 66.4%; officially reported figures include 61.2% on Kimi Code Bench 3.0 Long-horizon (K3: 55.95%) and 92.7% on BenchCAD. Caveat: the two labs run different hard benchmarks, so the numbers cannot be converted into a single ranking — each should be read against its own reference set.
Cost and Concurrency: Metered vs Subscription
deepseek-flash is metered with peak/off-peak pricing per million tokens: $0.15/$0.30 for cache-miss input (off-peak/peak), $0.60/$1.20 for output, cache hits from $0.003, and a 2,500-request concurrency limit — details in our API quick reference. K3.1 Pro today lives mainly inside Kimi Code subscriptions: available on Plus and above, with 1M context reserved for the Pro tier; Highspeed burns roughly 2x quota. No public metered pricing for K3.1 Pro had been published as of this writing — check the official platform page.
Match the Model to the Workload
For high-concurrency long-document processing, vision input and cost-sensitive batch jobs, deepseek-flash is the smoother fit: $1.20/M peak output, near-free cache hits and 2,500 concurrency. For terminal coding agents, long-horizon agentic tasks and 3D/game creation, K3.1 Pro's benchmark readings are stronger, and Highspeed eases the wait on a thinking-only model. For self-hosting, Flash weights are MIT-licensed and ready to deploy, while K3.1 Pro's full weights are not out yet — license and quantization details pending, so a fair weight-to-weight comparison must wait until early November. See also our open-weight flagship spec sheet, the same-family V4-Pro-0813 vs V4.1-Flash breakdown, and our K3.1 Pro release coverage.