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Gemini 3.5 Flash vs o1

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Gemini 3.5 Flash and o1.

Google

Gemini 3.5 Flash

Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...

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OpenAI

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...

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Technical Specifications

SpecificationGemini 3.5 Flasho1
ProviderGoogleOpenAI
Context Window1,048,576 tokens200,000 tokens
Agent Suitability88/10088/100
Time to First Token (TTFT)200 ms2500 ms
Deployment Modelmanaged apimanaged api
Production Stabilitystablestable
API AvailableYesYes
Released Date2026-05-192024-12-17

API Pricing Comparison

Input Price per Million Tokens

Gemini 3.5 Flash

$1.50

o1

$15.00

Output Price per Million Tokens

Gemini 3.5 Flash

$9.00

o1

$60.00

Want to test both models live?

Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.

Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
9050.0%vs9180.0%
Gemini 3.5 Flash
o1
HumanEvalPython coding & logic synthesis
9210.0%vs9450.0%
Gemini 3.5 Flash
o1
MATHComplex mathematical problem solving
8500.0%vs9480.0%
Gemini 3.5 Flash
o1
GPQAGraduate-level expert reasoning
6820.0%vs7830.0%
Gemini 3.5 Flash
o1
HellaSwagCommonsense reasoning and inference
9780.0%vs9200.0%
Gemini 3.5 Flash
o1
MT-BenchMulti-turn conversation flow quality
920.0%vs940.0%
Gemini 3.5 Flash
o1

Gemini 3.5 Flash Quirks & Gotchas

  • โ–ธExcellent multimodal performance โ€” native video understanding
  • โ–ธTool calling via Google's native function_declarations in Vertex AI

o1 Quirks & Gotchas

  • โ–ธReasoning model โ€” high latency by design, not for real-time use
  • โ–ธBest for complex math/code reasoning where accuracy > speed
  • โ–ธUse o3-mini when you need reasoning with lower latency