Model Comparison

Gemini 3.1 Pro vs Gemini 1.5 Pro

Gemini 3.1 Pro significantly outperforms across most benchmarks. Gemini 1.5 Pro is 1.3x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Gemini 3.1 Pro outperforms in 1 benchmarks (GPQA), while Gemini 1.5 Pro is better at 0 benchmarks.

Gemini 3.1 Pro significantly outperforms across most benchmarks.

Wed Apr 15 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Gemini 1.5 Pro costs less

For input processing, Gemini 3.1 Pro ($2.50/1M tokens) costs the same as Gemini 1.5 Pro ($2.50/1M tokens).

For output processing, Gemini 3.1 Pro ($15.00/1M tokens) is 1.5x more expensive than Gemini 1.5 Pro ($10.00/1M tokens).

In conclusion, Gemini 3.1 Pro is more expensive than Gemini 1.5 Pro.*

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

Lowest available price from all providers
Wed Apr 15 2026 • llm-stats.com
Google
Gemini 3.1 Pro
Input tokens$2.50
Output tokens$15.00
Best providerGoogle
Google
Gemini 1.5 Pro
Input tokens$2.50
Output tokens$10.00
Best providerGoogle
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Gemini 1.5 Pro accepts 2,097,152 input tokens compared to Gemini 3.1 Pro's 1,048,576 tokens. Gemini 3.1 Pro can generate longer responses up to 65,536 tokens, while Gemini 1.5 Pro is limited to 8,192 tokens.

Google
Gemini 3.1 Pro
Input1,048,576 tokens
Output65,536 tokens
Google
Gemini 1.5 Pro
Input2,097,152 tokens
Output8,192 tokens
Wed Apr 15 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 3.1 Pro and Gemini 1.5 Pro support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Gemini 3.1 Pro

Text
Images
Audio
Video

Gemini 1.5 Pro

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 3.1 Pro

Proprietary

Closed source

Gemini 1.5 Pro

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 3.1 Pro was released on 2026-02-19, while Gemini 1.5 Pro was released on 2024-05-01.

Gemini 3.1 Pro is 22 months newer than Gemini 1.5 Pro.

Gemini 3.1 Pro

Feb 19, 2026

1 months ago

1.8yr newer
Gemini 1.5 Pro

May 1, 2024

2.0 years ago

Knowledge Cutoff

When training data ends

Gemini 3.1 Pro has a knowledge cutoff of 2025-01-31, while Gemini 1.5 Pro has a cutoff of 2023-11-01.

Gemini 3.1 Pro has more recent training data (up to 2025-01-31), making it potentially better informed about events through that date compared to Gemini 1.5 Pro (2023-11-01).

Gemini 3.1 Pro

Jan 2025

1.2 yr newer
Gemini 1.5 Pro

Nov 2023

Provider Availability

Gemini 3.1 Pro is available from Google. Gemini 1.5 Pro is available from Google.

Gemini 3.1 Pro

google logo
Google
Input Price:Input: $2.50/1MOutput Price:Output: $15.00/1M

Gemini 1.5 Pro

google logo
Google
Input Price:Input: $2.50/1MOutput Price:Output: $10.00/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Google
Higher GPQA score (94.3% vs 59.1%)
Google
Larger context window (2,097,152 tokens)
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 3.1 Pro
Google
Gemini 1.5 Pro

FAQ

Common questions about Gemini 3.1 Pro vs Gemini 1.5 Pro

Gemini 3.1 Pro significantly outperforms across most benchmarks. Gemini 3.1 Pro is made by Google and Gemini 1.5 Pro is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Gemini 3.1 Pro scores t2-bench: 99.3%, LiveCodeBench Pro: 96.2%, GPQA: 94.3%, MMMLU: 92.6%, BrowseComp: 85.9%. Gemini 1.5 Pro scores XSTest: 98.8%, HellaSwag: 93.3%, GSM8k: 90.8%, BIG-Bench Hard: 89.2%, MGSM: 87.5%.
Both models cost $2.50 per million input tokens.
Gemini 3.1 Pro supports 1.0M tokens and Gemini 1.5 Pro supports 2.1M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (1.0M vs 2.1M). See the full comparison above for benchmark-by-benchmark results.