Gemini 1.5 Pro

Gemini 1.5 Pro

Quick answer: Gemini 1.5 Pro is Google DeepMind's long-context multimodal model, released February 2024. It introduced an industry-first 2M-token context window — enough to hold an entire large codebase, hours of audio, or thousands of documents in a single prompt — at $1.25/$5 per million tokens for prompts up to 128K. It is the benchmark reference for long-context retrieval and multi-modal processing at scale.

At a Glance

Where Gemini 1.5 Pro leads

• 2M-token context — the largest context window in its class at release; processes entire codebases, long video transcripts, and multi-document sets in one shot
• True multimodal inputs — text, images, audio, and video all natively supported; can analyse an hour-long video and answer questions about specific timestamps
• Strong long-context retrieval: near-perfect recall on the "needle in a haystack" evaluation at 1M tokens
• Lowest price in the frontier tier for inputs up to 128K ($1.25/1M input tokens)

Where it lags

• Superseded for reasoning-heavy tasks by Gemini 2.5 Pro, Gemini 3 Pro, and later models
• Lower GPQA Diamond (46.2%) and coding benchmarks than Claude 3.5 Sonnet or GPT-4o
• Output capped at 8,192 tokens per response

Best for: workflows that need to process very long documents, large audio/video files, or multi-file codebases in a single API call — especially when cost per token matters.

What Gemini 1.5 Pro Is

Gemini 1.5 Pro was announced on February 15, 2024, and made generally available on May 24, 2024. It is built on a Mixture-of-Experts (MoE) architecture — an approach where the model activates only a subset of its parameters per token, allowing Google DeepMind to scale effective capacity without proportionally increasing compute. This architectural choice is what enabled the 2M-token context window: the model can efficiently attend over much longer sequences than dense transformers of similar inference cost.

The 2M window is the model's defining capability. Google's internal evaluations at launch showed near-perfect (>99%) recall on the "needle in a haystack" test at 1M tokens — a task that requires finding a specific fact buried in an enormous context. This translates to real-world use cases that are impractical on 128K or 200K models: analysing a full codebase during a refactor, processing a 60-minute audio recording end-to-end, or running a RAG system over thousands of documents in a single call.

From a Benchgen perspective, Gemini 1.5 Pro is an important baseline for long-context agent evaluation. Many enterprise workflows involve multi-document research, long transcript processing, or large-file ingestion — tasks where context length is the first bottleneck, not model intelligence. Understanding its performance on BenchGen's Goal Completion and Memory Utilisation dimensions for long-horizon tasks gives teams a cleaner read on whether their bottleneck is context length or model capability.

Specifications

Pricing

Context caching and batch mode offer additional cost reductions. Source: Google AI pricing.

Context Window

Gemini 1.5 Pro has a 2,000,000-token context window — roughly 3,000 pages of text, or about 22 hours of audio, in a single request — with a maximum output of 8,192 tokens. This is the defining specification: it allows entire codebases, multi-chapter documents, long legal discovery sets, or full video transcripts to be processed without chunking. Google's internal evaluations reported near-perfect recall on the "needle in a haystack" test at 1M tokens, indicating the model uses rather than merely stores the extended context.

Note: prompts above 128K tokens are billed at double the standard rate ($2.50/$10 per 1M).

Public Benchmark Scores

Scores are reported by Google DeepMind and shown for context. They are not Benchgen measurements. See the technical report for full harness details.

Gemini 1.5 Pro vs Alternatives

Gemini 1.5 Pro's value proposition is clear: the cheapest per-token price at the frontier tier for short contexts, and the largest context window by a significant margin. It trades off on reasoning depth — GPT-4o and Claude 3.5 Sonnet post meaningfully higher GPQA and coding scores. The choice is whether your task is context-bound or reasoning-bound. (Rival scores from respective provider announcements; not Benchgen measurements.)

How Gemini 1.5 Pro Performs on Real Agent Tasks

Gemini 1.5 Pro's agent profile is shaped by its context window more than its per-step reasoning. For agents that need to hold large state — entire repositories, multi-session conversation history, or large document sets — it removes the chunking and retrieval overhead that clips goal completion rates in shorter-context models. A coding agent that can read a full codebase in context tends to make more coherent diffs and fewer "hallucinated file path" errors than one that retrieves chunks.

Where it is more limited is in the reasoning-per-step quality: its GPQA score of 46.2% reflects a gap versus GPT-4o and Claude 3.5 Sonnet on tasks requiring sustained, multi-hop logical inference. For Benchgen evaluations, teams typically find strong Goal Completion on document-heavy workflows and lower Tool-Call Accuracy on complex, nested tool chains — especially for tasks involving more than 3–4 simultaneous tool dependencies.

Use Gemini 1.5 Pro via API

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")
model = genai.GenerativeModel("gemini-1.5-pro-002")

response = model.generate_content(
    "Summarise the key risks in this contract set: ..."
)
print(response.text)

Related Models & Benchmarks

• Compare: GPT-4o · Claude 3.5 Sonnet · Gemini 3 Pro · Gemini 3 Flash
• Benchmarks: SWE-bench Verified · MMLU-Pro

Frequently Asked Questions

Specs and scores sourced from the Google DeepMind Gemini 1.5 technical report and Google AI pricing. Third-party benchmark scores attributed inline. Last updated 2026-06-19.

Gemini 1.5 Pro

Gemini 1.5 Pro is a large language model developed by Google.