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Google: Gemini 2.5 Pro (batch)

Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...

Anyone in the Project can @-mention Google: Gemini 2.5 Pro (batch) with the team's shared context - pooled credits, one chat, one memory.

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Starter is free forever - 1 Project, 100 credits/month, 1 MCP. No card.

Verdict

Gemini 2.5 Pro batch mode trades real-time responsiveness for 50% cost savings on both input and output tokens compared to standard Gemini 2.5 Pro. Jobs complete within 24 hours, making this the right choice for high-volume document processing, overnight data analysis, or any workflow where you can queue requests and wait. Skip it if you need sub-second latency or interactive chat — the batch delay kills those use cases.

Best for

Overnight document classification at scale
Batch video content analysis
Cost-sensitive long-context summarization
Scheduled data extraction pipelines
Non-interactive multimodal processing

Strengths

The 1M token context window handles book-length documents, hour-long video transcripts, and massive codebases in a single pass. Multimodal support spans text, images, audio, video, and file uploads — rare breadth at this price point. Input cost of $0.63/Mtok undercuts most competitors by 40-60% when you can tolerate batch latency. The same Gemini 2.5 Pro reasoning quality applies; you're trading time, not capability.

Trade-offs

Batch jobs complete within 24 hours with no guaranteed SLA, so this is unusable for user-facing features or real-time workflows. You lose the ability to stream responses or adjust mid-generation. Output pricing at $5.00/Mtok remains high relative to models like GPT-4o or Claude Sonnet 3.5, so cost savings shrink on generation-heavy tasks. No public benchmarks yet means you're relying on Google's claims about parity with standard Gemini 2.5 Pro.

Specifications

Provider: google
Category: llm
Context length: 1,048,576 tokens
Max output: 65,536 tokens
Modalities: text, image, file, audio, video
License: proprietary
Released: 2025-06-17

Pricing

Input: $0.63/Mtok
Output: $5.00/Mtok
Model ID: google/gemini-2.5-pro:batch

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.

Team cost calculator

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$34.10

17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.

Providers

Provider-level routing data is not available yet for this model.

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Classify Support Tickets

Read this support ticket and classify it into one of these categories: billing, technical, feature request, or urgent escalation. Return only the category name and a one-sentence reason.

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Extract Invoice Data

Extract the following fields from this invoice image: vendor name, invoice number, date, line items with quantities and prices, subtotal, tax, and total. Return as JSON.

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Summarize Video Transcripts

This is a transcript of a 90-minute product planning meeting. Summarize the key decisions made, action items assigned with owners, and any unresolved questions. Use bullet points.

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Analyze Code Repositories

Review this codebase for SQL injection vulnerabilities. For each issue found, cite the file path, line number, vulnerable code snippet, and recommended fix.

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Batch Translate Documents

Translate this product description from English to Spanish. Maintain technical terminology consistency and preserve all formatting, including bullet points and headings.

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