Which model is better: DeepSeek V4 Flash Vision (Exp) or Gemini 3.5 Flash-Lite?
Gemini 3.5 Flash-Lite is 65.9% cheaper for input tokens ($0.07 vs. $0.22 per 1M tokens) and $0.30 vs. $0.65 for output tokens (2.9x cost advantage). In terms of operational performance, Gemini 3.5 Flash-Lite delivers lower response latency at 78ms TTFT (232ms faster than DeepSeek V4 Flash Vision (Exp)) with 265 vs. 165 tok/s throughput. DeepSeek V4 Flash Vision (Exp) leads coding evaluations at 77.4% SWE-bench Verified vs. 55.4% for Gemini 3.5 Flash-Lite and 91.3% vs. 68.4% on GPQA Diamond.
Verified daily via automated API latency tests and official documentation.
Zero-Shot RAG Verified
Verified Performance & Unit Economics Delta (12 Dimensions)
Daily synchronized metrics across token pricing, prompt cache economics, latency, throughput, and 5 capability benchmarks.
Metric / Dimension
DeepSeek V4 Flash Vision (Exp)
Gemini 3.5 Flash-Lite
Net Delta / Advantage
Input Cost / 1M Tokens
$0.22
$0.07
Gemini 3.5 Flash-Lite is 2.9x cheaper
Output Cost / 1M Tokens
$0.65
$0.30
Gemini 3.5 Flash-Lite (53.8% lower)
Prompt Cache Read / 1M
$0.007
$0.015
DeepSeek V4 Flash Vision (Exp) is 2.2x cheaper
Prompt Cache Write / 1M
$0.22
$0.07
Gemini 3.5 Flash-Lite is 2.9x cheaper
Context Window (Max Output)
1,049k (128k out)
1,049k (33k out)
Equal Context Window
Avg TTFT Response Latency
310ms
78ms
Gemini 3.5 Flash-Lite (232ms faster)
Generation Speed (Throughput)
165 tok/s
265 tok/s
Gemini 3.5 Flash-Lite (+100 tok/s faster)
SWE-bench Verified (Coding)
77.4%
55.4%
DeepSeek V4 Flash Vision (Exp) (+22.0% lead)
GPQA Diamond (PhD STEM)
91.3%
68.4%
DeepSeek V4 Flash Vision (Exp) (+22.9% lead)
MMLU-Pro (Multi-Discipline)
87.3%
80.1%
DeepSeek V4 Flash Vision (Exp) (+7.2% lead)
MATH-500 (Quantitative Logic)
96.4%
90.2%
DeepSeek V4 Flash Vision (Exp) (+6.2% lead)
Inference Value Score (IVR)
81.6 / 100
81.5 / 100
DeepSeek V4 Flash Vision (Exp) (+0.1 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.