AI Models · Paid vs Paid

o1-pro Paid vs Gemini 3.5 Flash Lite Paid

o1-pro vs Gemini 3.5 Flash Lite compared — price per token, context window, multimodality, openness and which to choose. Full 2026 breakdown.

Prices & specs refreshed from live data · olud.ai

Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.

o1-proPaidOpenAI
$150 /M input
$600 /M outputManaged API (no infra to run)
TypeProprietary
Context window200K tokens
MultimodalYes
Self-hostNo
Gemini 3.5 Flash LitePaidGoogle
$0.3 /M input
$2.5 /M outputManaged API (no infra to run)
TypeProprietary
Context window1M tokens
MultimodalYes
Self-hostNo
Choose Gemini 3.5 Flash Lite for the lower output price ($2.5/M vs $600/M). It also gives you the larger 1M context window.

o1-pro vs Gemini 3.5 Flash Lite specs

Speco1-proGemini 3.5 Flash LiteWinner
MakerOpenAIGoogle
TypeProprietaryProprietary
Context window200K tokens1M tokensGemini 3.5 Flash Lite
Input price$150/M$0.3/MGemini 3.5 Flash Lite
Output price$600/M$2.5/MGemini 3.5 Flash Lite
Vision / multimodalYesYes= Tie
Tool / function callingNoYesGemini 3.5 Flash Lite
Self-hostableNo (API only)No (API only)
LicenseProprietaryProprietary

Price gap & when to choose each

240×cheaper per output token

Gemini 3.5 Flash Lite is ~240× cheaper than o1-pro on output tokens ($2.5 vs $600 per M tokens).

Choose o1-pro if…
  • You want frontier performance through a managed API
  • You value reliability & ecosystem
  • You don't want to manage infrastructure
Choose Gemini 3.5 Flash Lite if…
  • You want frontier performance through a managed API
  • You need the longer 1M context window
  • You want the lower output price
  • You value reliability & ecosystem
  • You don't want to manage infrastructure

Feature comparison

Capabilityo1-proGemini 3.5 Flash Lite
Open weights (downloadable)
Self-hostable
Runs fully offline
Vision / multimodal
Tool / function calling
1M+ context window

Benchmarks: o1-pro vs Gemini 3.5 Flash Lite

Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).

o1-pro
Gemini 3.5 Flash Lite
Intelligence index
12.4
22.7
Coding index
49.3
GPQA
83.8%
Humanity's Last Exam
18.8%
Long Context Reasoning
76%
SciCode
41.3%
τ-Bench Banking
17.5%
Terminal-Bench
53.6%
Speed
0 tok/s
345 tok/s
Latency
0s
7.71s
Intelligence per $
0
26.7

Benchmark data by Artificial Analysis.

How o1-pro and Gemini 3.5 Flash Lite score

🏆 Best value & openness: Gemini 3.5 Flash Lite (3.4 vs 2.4 / 5)
Criteriono1-proGemini 3.5 Flash Lite
Cost-efficiency2.04.5
Context window4.05.0
Openness1.51.5
Self-hosting1.01.0
Multimodality3.55.0

Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.

What each model is

o1-pro Paid

OpenAI · Proprietary

The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and provide...

Gemini 3.5 Flash Lite Paid

Google · Proprietary

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.

Other models in these families

These variants are tracked but not compared here — one page per family keeps the comparison readable.

Other variants tracked
Gemini 3.5 Flash Lite (batch)Gemini 3.1 Flash LiteGemini 3.1 Flash Lite (batch)Gemini 2.5 Flash LiteGemini 2.5 Flash Lite (batch)Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image)

Frequently asked questions

o1-pro vs Gemini 3.5 Flash Lite — which is cheaper?

Gemini 3.5 Flash Lite is cheaper on output ($2.5/M vs $600/M).

Which has the larger context window?

Gemini 3.5 Flash Lite offers the larger context window (1M tokens).

o1-pro vs Gemini 3.5 Flash Lite — which should I pick in 2026?

Choose Gemini 3.5 Flash Lite for the lower output price ($2.5/M vs $600/M). It also gives you the larger 1M context window.

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