Back to LLM Benchmark & Hardware Sizing Engine
Google DeepMindvsOpenAI

Gemini 1.5 Flash vs GPT-4o mini

Side-by-side technical showdown between Gemini 1.5 Flash and GPT-4o mini on TheModelverse. Compare verified LLM benchmark scores, quantization compression, local GPU hardware sizing, and API inference pricing.

Executive Showdown & Winner Breakdown

Who Wins Where: Gemini 1.5 Flash vs GPT-4o mini

Verified across benchmarks, pricing & local VRAM footprint
Software Engineering & Code
+224.5% lead
Leader:Gemini 1.5 Flash(74.3 vs 22.9)

Gemini 1.5 Flash demonstrates higher code generation accuracy and agentic bug resolution.

API Cost & Token Economics
50% cheaper
Leader:Gemini 1.5 Flash($0.07 vs $0.15 / 1M in)

Gemini 1.5 Flash delivers significantly lower input/output token pricing for high-throughput production.

Hardware Sizing & Model Compression

LLM Hardware Sizing & Quantization Sizing

Simulate weight compression levels and dynamic KV-cache expansion to verify whether these models fit on your local hardware or cloud GPU cluster.

Quantization Level
Active Precision
4 bits / parameter
VRAM Reduction
-72.5% vs FP16
Benchmark Retention
96–98% (Sweet Spot)
Context Simulator
Total VRAM Footprint @ 8k Context (INT4 (GGUF / AWQ))
Model 1Cloud Hosted

Gemini 1.5 Flash

Zero Local VRAM
Inference runs fully on provider cloud infrastructure. Zero GPU required locally.
Total Params:
Proprietary
Active Compute:
Dense
Attention Scheme:
GQA / Multi-Head
Max Context:
1000k tokens
Target Hardware: Zero Local VRAM (Managed Cloud API)
Model 2Cloud Hosted

GPT-4o mini

Zero Local VRAM
Inference runs fully on provider cloud infrastructure. Zero GPU required locally.
Total Params:
Proprietary
Active Compute:
Dense
Attention Scheme:
GQA / Multi-Head
Max Context:
128k tokens
Target Hardware: Zero Local VRAM (Managed Cloud API)
GPU & Hardware Tier Compatibility Matrix
Hardware ConfigurationAvailable VRAMGemini 1.5 FlashGPT-4o mini
16 GB VRAM
RTX 4070 / 4080 (16GB), Mac M-Series (16GB)
16 GBCloud APICloud API
24 GB VRAM
1x RTX 3090 / 4090 (24GB), Mac M-Series (32GB)
24 GBCloud APICloud API
48 GB VRAM
2x RTX 4090 (TP=2), 1x L40S, Mac M-Series (64GB)
48 GBCloud APICloud API
80 GB VRAM
1x NVIDIA A100 / H100 (80GB), Mac Studio (128GB)
80 GBCloud APICloud API
160 GB Node
2x H100 (TP=2), 4x L40S, Mac Studio (192GB)
160 GBCloud APICloud API
Multi-Node Cluster
4x–8x H100 Datacenter Cluster
320 GBCloud APICloud API
LLM Benchmark Database

LLM Benchmark Showdown & Head-to-Head Deltas

Normalized evaluation scores across code generation, advanced reasoning, mathematics, and multidisciplinary exams.

MMLU-Pro (Multitask Knowledge)Score % / Points
Gemini 1.5 Flash
78.9
GPT-4o mini
—
HumanEval (Python Code)Score % / Points
Gemini 1.5 Flash
74.3
GPT-4o mini
—
Inference Economics & Cost Simulator

Token Pricing & Scale Economics

Calculate estimated monthly cloud API spend and evaluate the breakeven point vs self-hosted GPU hardware.

10k500k1M
Pricing MetricGemini 1.5 FlashGPT-4o mini
Input Cost (/1M tokens)$0.07$0.15
Cached Input (/1M tokens)——
Output Cost (/1M tokens)$0.30$0.60
Simulated Monthly Bill (100,000 calls)
$16.50
~$0.17 / 1k queries
$33.00
~$0.33 / 1k queries
Self-Hosted Breakeven vs $864/mo GPUCloud API is more cost effectiveCloud API is more cost effective

Architecture Matrix

FeatureGemini 1.5 FlashGPT-4o mini
Release Date5/14/20247/18/2024
Routing / MoEDenseDense
Attention MechanismStandard / GQAStandard / GQA
Source TypeProprietary Commercial APIProprietary Commercial API
Customize or Add a 3rd Model to this Showdown
More Head-to-Head Comparisons for Gemini 1.5 Flash & GPT-4o mini
Gemini 1.5 FlashvsGemini 3.8 FlashGemini 1.5 FlashvsGPT-4o