Which model is better: DeepSeek R1 or Gemini 3.5 Flash-Lite?
Gemini 3.5 Flash-Lite is 86.4% cheaper for input tokens ($0.07 vs. $0.55 per 1M tokens) and $0.30 vs. $2.19 for output tokens (7.3x cost advantage). In terms of operational performance, Gemini 3.5 Flash-Lite delivers lower response latency at 78ms TTFT (1372ms faster than DeepSeek R1) with 265 vs. 58 tok/s throughput. Gemini 3.5 Flash-Lite leads coding evaluations at 55.4% SWE-bench Verified vs. 49.2% for DeepSeek R1 and 68.4% vs. 71.5% 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 R1
Gemini 3.5 Flash-Lite
Net Delta / Advantage
Input Cost / 1M Tokens
$0.55
$0.07
Gemini 3.5 Flash-Lite is 7.3x cheaper
Output Cost / 1M Tokens
$2.19
$0.30
Gemini 3.5 Flash-Lite (86.3% lower)
Prompt Cache Read / 1M
$0.140
$0.015
Gemini 3.5 Flash-Lite is 9.3x cheaper
Prompt Cache Write / 1M
$0.55
$0.07
Gemini 3.5 Flash-Lite is 7.3x cheaper
Context Window (Max Output)
131k (33k out)
1,049k (33k out)
Gemini 3.5 Flash-Lite (8.0x larger context)
Avg TTFT Response Latency
1450ms
78ms
Gemini 3.5 Flash-Lite (1372ms faster)
Generation Speed (Throughput)
58 tok/s
265 tok/s
Gemini 3.5 Flash-Lite (+207 tok/s faster)
SWE-bench Verified (Coding)
49.2%
55.4%
Gemini 3.5 Flash-Lite (+6.2% lead)
GPQA Diamond (PhD STEM)
71.5%
68.4%
DeepSeek R1 (+3.1% lead)
MMLU-Pro (Multi-Discipline)
84%
80.1%
DeepSeek R1 (+3.9% lead)
MATH-500 (Quantitative Logic)
97.3%
90.2%
DeepSeek R1 (+7.1% lead)
Inference Value Score (IVR)
64.8 / 100
81.5 / 100
Gemini 3.5 Flash-Lite (+16.7 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Which is cheaper: DeepSeek R1 or Gemini 3.5 Flash-Lite?
Gemini 3.5 Flash-Lite is cheaper at $0.075/1M input tokens ($0.30/1M output) compared to $0.55/1M input ($2.19/1M output) for DeepSeek R1.
Which model has lower TTFT latency and higher tok/s throughput: DeepSeek R1 or Gemini 3.5 Flash-Lite?
Gemini 3.5 Flash-Lite delivers faster first-token latency at 78ms TTFT (265 tok/s throughput) compared to 1450ms TTFT (58 tok/s) for DeepSeek R1.
How do DeepSeek R1 and Gemini 3.5 Flash-Lite compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, DeepSeek R1 scores 49.2% versus 55.4% for Gemini 3.5 Flash-Lite. On GPQA Diamond graduate-level STEM reasoning, DeepSeek R1 scores 71.5% versus 68.4% for Gemini 3.5 Flash-Lite (and 97.3% vs 90.2% on MATH-500).
When should you choose DeepSeek R1?
Choose DeepSeek R1 (DeepSeek) for open-weight chain-of-thought reasoning, synthetic data distillation, and complex mathematical verification — offering a 131k context window, 1450ms TTFT, and $0.55/1M input pricing ($0.140/1M cached reads).
When should you choose Gemini 3.5 Flash-Lite?
Choose Gemini 3.5 Flash-Lite (Google) for high-frequency data pipelines, real-time routing, large-scale document tagging, and ultra-low-cost conversational assistants — offering a 1,048k context window, 78ms TTFT, and $0.07/1M input pricing ($0.015/1M cached reads).
