Model Comparison

Gemini 1.0 Pro vs Gemini 3.5 Flash-LiteWhich is better in 2026?

Comparing Gemini 1.0 Pro and Gemini 3.5 Flash-Lite across benchmarks, pricing, and capabilities.

Verdict: Gemini 1.0 Pro vs Gemini 3.5 Flash-Lite — which is better?

Gemini 1.0 Pro (by Google) and Gemini 3.5 Flash-Lite (by Google) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

On price, Gemini 1.0 Pro is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Gemini 3.5 Flash-Lite also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.

Choose Gemini 1.0 Pro if…

  • cost matters — it's about 1.1x cheaper per token

Choose Gemini 3.5 Flash-Lite if…

  • you process long inputs — it offers a 1,048,576 token context window
  • you want the most recent training data — it shipped Jul 2026

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Gemini 1.0 Pro and Gemini 3.5 Flash-Litedon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Gemini 1.0 Pro costs less

For input processing, Gemini 1.0 Pro ($0.50/1M tokens) is 1.7x more expensive than Gemini 3.5 Flash-Lite ($0.30/1M tokens).

For output processing, Gemini 1.0 Pro ($1.50/1M tokens) is 1.7x cheaper than Gemini 3.5 Flash-Lite ($2.50/1M tokens).

In conclusion, Gemini 3.5 Flash-Lite is more expensive than Gemini 1.0 Pro.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue Jul 21 2026 • llm-stats.com
Google
Gemini 1.0 Pro
Input tokens$0.50
Output tokens$1.50
Best providerGoogle
Google
Gemini 3.5 Flash-Lite
Input tokens$0.30
Output tokens$2.50
Best providerGoogle
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Gemini 3.5 Flash-Lite accepts 1,048,576 input tokens compared to Gemini 1.0 Pro's 32,760 tokens. Gemini 3.5 Flash-Lite can generate longer responses up to 65,536 tokens, while Gemini 1.0 Pro is limited to 8,192 tokens.

Google
Gemini 1.0 Pro
Input32,760 tokens
Output8,192 tokens
Google
Gemini 3.5 Flash-Lite
Input1,048,576 tokens
Output65,536 tokens
Tue Jul 21 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 3.5 Flash-Lite supports multimodal inputs, whereas Gemini 1.0 Pro does not.

Gemini 3.5 Flash-Lite can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 1.0 Pro

Text
Images
Audio
Video

Gemini 3.5 Flash-Lite

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Gemini 1.0 Pro

Proprietary

Closed source

Gemini 3.5 Flash-Lite

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 1.0 Pro was released on 2024-02-15, while Gemini 3.5 Flash-Lite was released on 2026-07-21.

Gemini 3.5 Flash-Lite is 30 months newer than Gemini 1.0 Pro.

Gemini 1.0 Pro

Feb 15, 2024

2.4 years ago

Gemini 3.5 Flash-Lite

Jul 21, 2026

0 days ago

2.4yr newer

Knowledge Cutoff

When training data ends

Gemini 1.0 Pro has a knowledge cutoff of 2024-02-01, while Gemini 3.5 Flash-Lite has a cutoff of 2026-03-31.

Gemini 3.5 Flash-Lite has more recent training data (up to 2026-03-31), making it potentially better informed about events through that date compared to Gemini 1.0 Pro (2024-02-01).

Gemini 1.0 Pro

Feb 2024

Gemini 3.5 Flash-Lite

Mar 2026

2.1 yr newer

Provider Availability

Gemini 1.0 Pro is available from Google. Gemini 3.5 Flash-Lite is available from Google.

Gemini 1.0 Pro

google logo
Google
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M

Gemini 3.5 Flash-Lite

google logo
Google
Input Price:Input: $0.30/1MOutput Price:Output: $2.50/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google
Less expensive output tokens
Google
Larger context window (1,048,576 tokens)
Supports multimodal inputs
Less expensive input tokens

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Gemini 1.0 Pro and Gemini 3.5 Flash-Lite side-by-side, then vote on the output you prefer.

Gemini 1.0 Pro
✓ Preferred
Gemini 3.5 Flash-Lite
Open in Playground
AI Model Comparison Table
Feature
Google
Gemini 1.0 Pro
Google
Gemini 3.5 Flash-Lite

FAQ

Common questions about Gemini 1.0 Pro vs Gemini 3.5 Flash-Lite.

Which is better, Gemini 1.0 Pro or Gemini 3.5 Flash-Lite?

Gemini 1.0 Pro (Google) and Gemini 3.5 Flash-Lite (Google) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Gemini 1.0 Pro compare to Gemini 3.5 Flash-Lite in benchmarks?

Gemini 1.0 Pro scores FLEURS: 93.6%, BIG-Bench: 75.0%, MMLU: 71.8%, WMT23: 71.7%, EgoSchema: 55.7%. Gemini 3.5 Flash-Lite scores CharXiv-R: 76.5%, OSWorld-Verified: 74.0%, SWE-Bench Pro: 54.2%, Terminal-Bench 2.1: 54.0%, MLE-Bench: 39.2%.

Is Gemini 1.0 Pro cheaper than Gemini 3.5 Flash-Lite?

Gemini 3.5 Flash-Lite is 1.7x cheaper for input tokens. Gemini 1.0 Pro costs $0.50/M input and $1.50/M output via google. Gemini 3.5 Flash-Lite costs $0.30/M input and $2.50/M output via google.

What are the context window sizes for Gemini 1.0 Pro and Gemini 3.5 Flash-Lite?

Gemini 1.0 Pro supports 33K tokens and Gemini 3.5 Flash-Lite supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 1.0 Pro and Gemini 3.5 Flash-Lite?

Key differences include context window (33K vs 1.0M), input pricing ($0.50 vs $0.30/M), multimodal support (no vs yes). See the full comparison above for benchmark-by-benchmark results.

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