DeepSeek-V4.1-Flash-1017 vs Gemini 4 Pro (Leaked Benchmark)
A direct, empirical evaluation of DeepSeek-V4.1-Flash-1017 (DeepSeek) against Gemini 4 Pro (Leaked Benchmark) (Google DeepMind).
DeepSeek-V4.1-Flash-1017
Developed by DeepSeek · Open-Weight Distilled Transformer (MIT License) parameters
Top-tier open-weight coding (81.6% SWE-bench Verified), concise reasoning traces with 32% lower token bloat, and fast consumer GPU self-hosting.
Gemini 4 Pro (Leaked Benchmark)
Developed by Google DeepMind · Proprietary Omni-Modal Unified Diffusion Transformer parameters
Repository-scale 2M-token refactoring (88.7% DeepSWE v1.1), headless shell execution (95.3% Terminal-Bench 2.1), and multimodal desktop agent autonomy (86.8% OSWorld 2.0).
| Metric | DeepSeek-V4.1-Flash-1017 | Gemini 4 Pro (Leaked Benchmark) |
|---|---|---|
| SWE-bench Verified | 81.6% | 88.7% |
| MMLU-Pro | 92.8% | 93.8% |
| MATH-500 | 94.8% | 97.8% |
| Context Window | 128,000 tokens | 2,000,000 tokens |
| Input Pricing (per 1M) | $0.14 | $2.25 |
| Output Pricing (per 1M) | $0.28 | $11.25 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-V4.1-Flash-1017 (DeepSeek) alongside Gemini 4 Pro (Leaked Benchmark) (Google DeepMind) highlights key technical trade-offs across neural parameter scale and compute efficiency. DeepSeek-V4.1-Flash-1017 executes across Open-Weight Distilled Transformer (MIT License) total parameters with Multi-Head Latent Attention (MLA) Core active per token, whereas Gemini 4 Pro (Leaked Benchmark) operates on Proprietary Omni-Modal Unified Diffusion Transformer parameters with Dynamic Speculative MoE Backbone on TPU v6 Trillium active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-V4.1-Flash-1017 achieves 81.6% autonomous resolution rate, compared to 88.7% for Gemini 4 Pro (Leaked Benchmark). On complex reasoning benchmarks, DeepSeek-V4.1-Flash-1017 posts 92.8% on MMLU-Pro and 94.8% on MATH-500, against 93.8% and 97.8% respectively for Gemini 4 Pro (Leaked Benchmark).
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $1.82 on DeepSeek-V4.1-Flash-1017 versus $49.50 on Gemini 4 Pro (Leaked Benchmark).
Recommendation: Deploy DeepSeek-V4.1-Flash-1017 when your infrastructure prioritizes top-tier open-weight coding (81.6% swe-bench verified), concise reasoning traces with 32% lower token bloat, and fast consumer gpu self-hosting.. Deploy Gemini 4 Pro (Leaked Benchmark) when workload requirements favor repository-scale 2m-token refactoring (88.7% deepswe v1.1), headless shell execution (95.3% terminal-bench 2.1), and multimodal desktop agent autonomy (86.8% osworld 2.0)..