Intelligence
10 scoreGoogle · Flagships Analysis
Gemini 1.5 Pro (Sep)
Intelligence, Performance & Price Analysis
Canonical slug: gemini-1-5-pro · Canonical model registry at build time
Input Price
$0.00Output Price
$0.00Executive Assessment
Routing Verdict & Tradeoffs
Ethen Routing Verdict
Capable everyday model
Gemini 1.5 Pro (Sep '24) scores 10 (estimated) on the Artificial Analysis Intelligence Index, placing it above average among other non-reasoning models in a similar price tier (median: 8).
Good for routine tasks; route complex reasoning and premium workloads to stronger models.
Task Fit Assessment
| Workload | Rating | Notes |
|---|---|---|
| Complex reasoning & agentic workflows | viable | Intelligence score 10 handles routine reasoning but may struggle with open-ended agentic tasks. |
| Cost-sensitive pipelines | optimal | Output pricing at $0.00 is very competitive for high-volume workloads. |
Cost Pressure Analysis
Low
Pricing is competitive — input $0.00, output $0.00. Suitable for sustained production use.
Technical Specifications
Architecture and Limits
| Specification | Value | Validation Authority |
|---|---|---|
| Model type | Proprietary | inferred |
| Reasoning | No | faq |
| Input modalities | Gemini 1.5 Pro (Sep '24) supports image input. | faq |
| Output modalities | Gemini 1.5 Pro (Sep '24) supports text only output. | faq |
| Context window | Gemini 1.5 Pro (Sep '24) has a context window of 2.0M tokens. This determines how much text and conversation history the model can process in a single request. | faq |
| Open weights / source | No, Gemini 1.5 Pro (Sep '24) is proprietary. The model weights are not publicly available. | faq |
| Parameters | Gemini 1.5 Pro (Sep '24) is a proprietary model and Google has not disclosed the model size or parameter count. | faq |
| Knowledge cutoff | Gemini 1.5 Pro (Sep '24) has a knowledge cutoff of August 2024. The model's training data includes information up to this date. | faq |
Evidence charts
Profile visualizations
Charts are the same canonical evidence cards previously published for this slug, contained inside the D18D page grammar.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Retrieved 2026-07-08
Chart source and provenance are listed in Methodology & sources below.
- Source:
- Published benchmark dataset
- Retrieval date:
- 2026-07-08
- Methodology:
- Independent test run by Artificial Analysis on dedicated hardware.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Methodology
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for Gemini 1.5 Pro (Sep).
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Frequently Asked Questions
Model FAQs & Technical Disclosures
Gemini 1.5 Pro (Sep '24) was released on September 24, 2024.
Gemini 1.5 Pro (Sep '24) was created by Google.
Gemini 1.5 Pro (Sep '24) scores 10 (estimated) on the Artificial Analysis Intelligence Index, placing it above average among other non-reasoning models in a similar price tier (median: 8).
No, Gemini 1.5 Pro (Sep '24) is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.
Gemini 1.5 Pro (Sep '24) supports image input.
Gemini 1.5 Pro (Sep '24) supports text only output.
Yes, Gemini 1.5 Pro (Sep '24) supports image input and can analyze, describe, and answer questions about images.
No, Gemini 1.5 Pro (Sep '24) is not multimodal. It only supports image input.
Gemini 1.5 Pro (Sep '24) has a context window of 2.0M tokens. This determines how much text and conversation history the model can process in a single request.
No, Gemini 1.5 Pro (Sep '24) is proprietary. The model weights are not publicly available.
Gemini 1.5 Pro (Sep '24) is a proprietary model and Google has not disclosed the model size or parameter count.
Gemini 1.5 Pro (Sep '24) achieves a score of 10 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Gemini 1.5 Pro (Sep '24) has a knowledge cutoff of August 2024. The model's training data includes information up to this date.
Check the providers page for current API availability.
Visit the providers page to see availability.