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
22.2
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
409 tok/s
Latency
0s
5.69s
Intelligence per $
0
26.1

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 LiteNano 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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