Which model is better: Gemini 3.8 Flash or OpenAI o3-mini?
Gemini 3.8 Flash is 77.3% cheaper for input tokens ($0.25 vs. $1.10 per 1M tokens) and $1.50 vs. $4.40 for output tokens (4.4x cost advantage). In terms of operational performance, Gemini 3.8 Flash delivers lower response latency at 105ms TTFT (845ms faster than OpenAI o3-mini) with 210 vs. 98 tok/s throughput. Gemini 3.8 Flash leads coding evaluations at 68.5% SWE-bench Verified vs. 49.3% for OpenAI o3-mini and 79.2% vs. 79.7% 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
Gemini 3.8 Flash
OpenAI o3-mini
Net Delta / Advantage
Input Cost / 1M Tokens
$0.25
$1.10
Gemini 3.8 Flash is 4.4x cheaper
Output Cost / 1M Tokens
$1.50
$4.40
Gemini 3.8 Flash (65.9% lower)
Prompt Cache Read / 1M
$0.025
$0.550
Gemini 3.8 Flash is 22.0x cheaper
Prompt Cache Write / 1M
$0.25
$1.10
Gemini 3.8 Flash is 4.4x cheaper
Context Window (Max Output)
1,049k (66k out)
205k (100k out)
Gemini 3.8 Flash (5.1x larger context)
Avg TTFT Response Latency
105ms
950ms
Gemini 3.8 Flash (845ms faster)
Generation Speed (Throughput)
210 tok/s
98 tok/s
Gemini 3.8 Flash (+112 tok/s faster)
SWE-bench Verified (Coding)
68.5%
49.3%
Gemini 3.8 Flash (+19.2% lead)
GPQA Diamond (PhD STEM)
79.2%
79.7%
OpenAI o3-mini (+0.5% lead)
MMLU-Pro (Multi-Discipline)
87.4%
83.2%
Gemini 3.8 Flash (+4.2% lead)
MATH-500 (Quantitative Logic)
95.1%
97.9%
OpenAI o3-mini (+2.8% lead)
Inference Value Score (IVR)
74 / 100
59.7 / 100
Gemini 3.8 Flash (+14.3 pts)
Interactive Monthly Token Economics & ROI Forecaster
Model production workloads across prompt, completion, and cached context tokens, or switch models on the fly.
Choose Gemini 3.8 Flash (Google) for high-throughput agentic workflows, real-time multimodal RAG, live search grounding, and production code generation at flash unit economics — offering a 1,048k context window, 105ms TTFT, and $0.25/1M input pricing ($0.025/1M cached reads).
Choose OpenAI o3-mini (OpenAI) for competitive math reasoning, automated unit test generation, and structured logic pipelines — offering a 204k context window, 950ms TTFT, and $1.10/1M input pricing ($0.550/1M cached reads).
Which is cheaper: Gemini 3.8 Flash or OpenAI o3-mini?
Gemini 3.8 Flash is cheaper at $0.25/1M input tokens ($1.50/1M output) compared to $1.10/1M input ($4.40/1M output) for OpenAI o3-mini.
Which model has lower TTFT latency and higher tok/s throughput: Gemini 3.8 Flash or OpenAI o3-mini?
Gemini 3.8 Flash delivers faster first-token latency at 105ms TTFT (210 tok/s throughput) compared to 950ms TTFT (98 tok/s) for OpenAI o3-mini.
How do Gemini 3.8 Flash and OpenAI o3-mini compare on SWE-bench coding and GPQA Diamond reasoning?
On SWE-bench Verified software engineering, Gemini 3.8 Flash scores 68.5% versus 49.3% for OpenAI o3-mini. On GPQA Diamond graduate-level STEM reasoning, Gemini 3.8 Flash scores 79.2% versus 79.7% for OpenAI o3-mini (and 95.1% vs 97.9% on MATH-500).
When should you choose Gemini 3.8 Flash?
Choose Gemini 3.8 Flash (Google) for high-throughput agentic workflows, real-time multimodal RAG, live search grounding, and production code generation at flash unit economics — offering a 1,048k context window, 105ms TTFT, and $0.25/1M input pricing ($0.025/1M cached reads).
When should you choose OpenAI o3-mini?
Choose OpenAI o3-mini (OpenAI) for competitive math reasoning, automated unit test generation, and structured logic pipelines — offering a 204k context window, 950ms TTFT, and $1.10/1M input pricing ($0.550/1M cached reads).
