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Gemini 3.5 Flash-Lite vs o1

Detailed technical comparison between Gemini 3.5 Flash-Lite (Google) and o1 (OpenAI). Review live API token pricing, context window capabilities, time-to-first-token latency, and verified benchmark scores side-by-side.

⚡ Executive Summary & Verdict

Comparison Snapshot

Gemini 3.5 Flash-Lite: 0 Winsvso1: 6 Wins
Context Leader

Gemini 3.5 Flash-Lite

1,048,576 tokens
Agentic Tool-Calling

Tie

Equal Capability
Lowest Latency (TTFT)

Tie

Equal Speed
Input Price Leader

Gemini 3.5 Flash-Lite

$0.30 / MTok
Googleactive

Gemini 3.5 Flash-Lite

Gemini 3.5 Flash-Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.

View Gemini 3.5 Flash-Lite Full Specs →
OpenAIactive

o1

The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...

View o1 Full Specs →

Technical Specifications

🏆 = Superior Spec
SpecificationGemini 3.5 Flash-Liteo1
ProviderGoogleOpenAI
Context Window1,048,576 tokens🏆200,000 tokens
Agent SuitabilityN/A88/100
Time to First Token (TTFT)N/A2500 ms
Deployment Modelmanaged apimanaged api
Production Stabilitybetastable
API AvailableYesYes
Released Date2026-07-212024-12-17

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.5 Flash-Lite

$0.30

o1

$15.00

Output Price per Million Tokens

Gemini 3.5 Flash-Lite

$2.50

o1

$60.00

💡 Cost Ratio: Gemini 3.5 Flash-Lite is 50.0x cheaper per input token than o1.

Want to test both models live?

Run side-by-side prompt benchmarks in our dynamic multi-model Sandbox. Compare execution speeds, latency metrics, and compute actual costs in real-time.

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Benchmark Performance Metrics

Standardized Scores (0–100%)

Scores show verified raw accuracy percentages across standardized AI evaluation suites. Higher bars indicate superior performance in that domain.

MMLUGeneral knowledge & multi-task understanding
73.2%vs91.8%+18.6% o1
Gemini 3.5 Flash-Lite
o1 🏆
HumanEvalPython coding & logic synthesis
69.4%vs94.5%+25.1% o1
Gemini 3.5 Flash-Lite
o1 🏆
MATHComplex mathematical problem solving
42.6%vs94.8%+52.2% o1
Gemini 3.5 Flash-Lite
o1 🏆
GPQAGraduate-level expert reasoning
30.0%vs78.3%+48.3% o1
Gemini 3.5 Flash-Lite
o1 🏆
HellaSwagCommonsense reasoning and inference
77.2%vs92.0%+14.8% o1
Gemini 3.5 Flash-Lite
o1 🏆
MT-BenchMulti-turn conversation flow quality
8.0%vs9.4%+1.4% o1
Gemini 3.5 Flash-Lite
o1 🏆

Gemini 3.5 Flash-Lite Quirks & Gotchas

No developer gotchas reported.

o1 Quirks & Gotchas

  • ▸Reasoning model — high latency by design, not for real-time use
  • ▸Best for complex math/code reasoning where accuracy > speed
  • ▸Use o3-mini when you need reasoning with lower latency

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