Model Comparison

Gemini 1.5 Pro vs Gemini 1.0 ProWhich is better in 2026?

Gemini 1.5 Pro significantly outperforms across most benchmarks. Gemini 1.0 Pro is 5.8x cheaper per token.

Verdict: Gemini 1.5 Pro vs Gemini 1.0 Pro — which is better?

Gemini 1.5 Pro (by Google) and Gemini 1.0 Pro (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.

Gemini 1.5 Pro outperforms in 7 benchmarks (FLEURS, GPQA, MATH, MathVista, MMLU, MMMU, WMT23), while Gemini 1.0 Pro is better at 0 benchmarks. Gemini 1.5 Pro significantly outperforms across most benchmarks.

On price, Gemini 1.0 Pro is roughly 5.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Gemini 1.5 Pro also accepts a larger context window (2,097,152 input tokens), making it the stronger choice for long documents and large codebases.

Choose Gemini 1.5 Pro if…

  • you want the strongest raw capability — it leads on 7 of 7 shared benchmarks
  • you process long inputs — it offers a 2,097,152 token context window
  • you want the most recent training data — it shipped May 2024

Choose Gemini 1.0 Pro if…

  • cost matters — it's about 5.8x cheaper per token
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Performance Benchmarks

Comparative analysis across standard metrics

7 benchmarks

Gemini 1.5 Pro outperforms in 7 benchmarks (FLEURS, GPQA, MATH, MathVista, MMLU, MMMU, WMT23), while Gemini 1.0 Pro is better at 0 benchmarks.

Gemini 1.5 Pro significantly outperforms across most benchmarks.

Wed Jun 10 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Gemini 1.0 Pro costs less

For input processing, Gemini 1.5 Pro ($2.50/1M tokens) is 5.0x more expensive than Gemini 1.0 Pro ($0.50/1M tokens).

For output processing, Gemini 1.5 Pro ($10.00/1M tokens) is 6.7x more expensive than Gemini 1.0 Pro ($1.50/1M tokens).

In conclusion, Gemini 1.5 Pro is more expensive than Gemini 1.0 Pro.*

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

Lowest available price from all providers
Wed Jun 10 2026 • llm-stats.com
Google
Gemini 1.5 Pro
Input tokens$2.50
Output tokens$10.00
Best providerGoogle
Google
Gemini 1.0 Pro
Input tokens$0.50
Output tokens$1.50
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 1.0 Pro's 32,760 tokens. Both models can generate responses up to 8,192 tokens.

Google
Gemini 1.5 Pro
Input2,097,152 tokens
Output8,192 tokens
Google
Gemini 1.0 Pro
Input32,760 tokens
Output8,192 tokens
Wed Jun 10 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 1.5 Pro supports multimodal inputs, whereas Gemini 1.0 Pro does not.

Gemini 1.5 Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 1.5 Pro

Text
Images
Audio
Video

Gemini 1.0 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 1.5 Pro

Proprietary

Closed source

Gemini 1.0 Pro

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 1.5 Pro was released on 2024-05-01, while Gemini 1.0 Pro was released on 2024-02-15.

Gemini 1.5 Pro is 3 months newer than Gemini 1.0 Pro.

Gemini 1.5 Pro

May 1, 2024

2.1 years ago

2mo newer
Gemini 1.0 Pro

Feb 15, 2024

2.3 years ago

Knowledge Cutoff

When training data ends

Gemini 1.5 Pro has a knowledge cutoff of 2023-11-01, while Gemini 1.0 Pro has a cutoff of 2024-02-01.

Gemini 1.0 Pro has more recent training data (up to 2024-02-01), making it potentially better informed about events through that date compared to Gemini 1.5 Pro (2023-11-01).

Gemini 1.5 Pro

Nov 2023

Gemini 1.0 Pro

Feb 2024

3 mo newer

Provider Availability

Gemini 1.5 Pro is available from Google. Gemini 1.0 Pro is available from Google.

Gemini 1.5 Pro

google logo
Google
Input Price:Input: $2.50/1MOutput Price:Output: $10.00/1M

Gemini 1.0 Pro

google logo
Google
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Google
Larger context window (2,097,152 tokens)
Supports multimodal inputs
Higher FLEURS score (6.7% vs 6.4%)
Higher GPQA score (59.1% vs 27.9%)
Higher MATH score (86.5% vs 32.6%)
Higher MathVista score (68.1% vs 46.6%)
Higher MMLU score (85.9% vs 71.8%)
Higher MMMU score (65.9% vs 47.9%)
Higher WMT23 score (75.1% vs 71.7%)
Google
Less expensive input tokens
Less expensive output tokens

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 1.5 Pro
Google
Gemini 1.0 Pro

FAQ

Common questions about Gemini 1.5 Pro vs Gemini 1.0 Pro.

Which is better, Gemini 1.5 Pro or Gemini 1.0 Pro?

Gemini 1.5 Pro significantly outperforms across most benchmarks. Gemini 1.5 Pro is made by Google and Gemini 1.0 Pro is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 1.5 Pro compare to Gemini 1.0 Pro in benchmarks?

Gemini 1.5 Pro scores XSTest: 98.8%, HellaSwag: 93.3%, GSM8k: 90.8%, BIG-Bench Hard: 89.2%, MGSM: 87.5%. Gemini 1.0 Pro scores BIG-Bench: 75.0%, MMLU: 71.8%, WMT23: 71.7%, EgoSchema: 55.7%, MMMU: 47.9%.

Is Gemini 1.5 Pro cheaper than Gemini 1.0 Pro?

Gemini 1.0 Pro is 5.0x cheaper for input tokens. Gemini 1.5 Pro costs $2.50/M input and $10.00/M output via google. Gemini 1.0 Pro costs $0.50/M input and $1.50/M output via google.

What are the context window sizes for Gemini 1.5 Pro and Gemini 1.0 Pro?

Gemini 1.5 Pro supports 2.1M tokens and Gemini 1.0 Pro supports 33K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 1.5 Pro and Gemini 1.0 Pro?

Key differences include context window (2.1M vs 33K), input pricing ($2.50 vs $0.50/M), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

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