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DeepSeek
DeepSeek V4 Flash 0731
VS
Moonshot AI (Kimi)
kimi-k2.6
Analysis By: The WhichLLMModel Editorial Team|Updated: June 2026
Live Hardware Compatibility Simulator
GPU Dedicated VRAM16 GB
System Host RAM32 GB
DeepSeek V4 Flash 0731OOM / Exceeds Limit
VRAM Footprint (Q4):145.97 GB
Active GPU Load:12.61 GB
kimi-k2.6OOM / Exceeds Limit
VRAM Footprint (Q4):587.69 GB
Active GPU Load:0 GB
Hardware & Architecture Shootout Matrix
| Metric / Spec | DeepSeek V4 Flash 0731 | kimi-k2.6 |
|---|---|---|
| Parameter Scale | 284B Parameters | 1000B Parameters |
| Attention Architecture | Standard GQA / MHA | MLA Low-Rank Latent |
| Max Context Window | 1.05M | 262.14k |
| Available GGUF Quants | 2 Quant Formats | 2 Quant Formats |
| Full Model Page | Hardware Details | Hardware Details |
Intelligence & Benchmark Head-to-Head
Coding Benchmark (SWE-bench / HumanEval)
DeepSeek V4 Flash 073154.4%
kimi-k2.658.6%
Reasoning Benchmark (GPQA / MMLU)
DeepSeek V4 Flash 0731N/A%
kimi-k2.690.5%
Frequently Asked Questions about DeepSeek V4 Flash 0731 vs kimi-k2.6
kimi-k2.6 is cheaper than DeepSeek V4 Flash 0731. kimi-k2.6 has a blended cost of $NaN/1M tokens, which is about 1.0x cheaper than DeepSeek V4 Flash 0731 at $NaN/1M tokens.
For coding tasks, DeepSeek V4 Flash 0731 scores 54.4% on deep-swe (frontier deep software engineering and end-to-end repository problem solving), while kimi-k2.6 scores 58.6% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning).
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