LLMgoogle

Google: Gemini 2.5 Flash (batch)

Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in "thinking" capabilities, enabling it to provide responses with greater...

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

All models

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

Verdict

Gemini 2.5 Flash batch mode trades real-time response for aggressive cost reduction — $0.15/Mtok input makes it the cheapest multimodal option from a major vendor. Context window matches the full 1M tokens of the synchronous version, and you get the same vision, audio, and video capabilities. The catch: batch jobs queue and complete on Google's schedule, typically within hours. Reach for this when you're processing large document sets, video archives, or image collections where latency doesn't matter and budget does.

Best for

Bulk document processing at scale
Video archive analysis and tagging
Cost-sensitive multimodal workflows
Overnight data enrichment pipelines
Large image dataset classification

Strengths

Input pricing at $0.15/Mtok undercuts synchronous Gemini 2.5 Flash by 70% and beats most text-only models on cost. The full 1M token context window handles book-length documents or hour-long video transcripts in a single pass. Multimodal support spans images, audio, video, and file uploads — rare at this price point. Batch processing removes rate limit friction when you're pushing hundreds of requests through overnight jobs.

Trade-offs

Batch mode means no streaming, no real-time interaction, and unpredictable completion times — Google quotes 'within 24 hours' but doesn't guarantee sub-hour turnaround. You lose the ability to course-correct mid-conversation or react to intermediate outputs. Performance benchmarks aren't published separately for batch mode, so assume parity with synchronous Flash but verify on your workload. If your use case needs answers in seconds or interactive refinement, this isn't the model.

Specifications

Provider: google
Category: llm
Context length: 1,048,576 tokens
Max output: 65,535 tokens
Modalities: file, image, text, audio, video
License: proprietary
Released: 2025-06-17

Pricing

Input: $0.15/Mtok
Output: $1.25/Mtok
Model ID: google/gemini-2.5-flash: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

$8.45

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

Batch Video Summarization

Watch this video and provide a structured summary: list the main topics covered, timestamp any key moments or transitions, and note any on-screen text or graphics that appear. Format as a bulleted list.

Open in a Project →

Document Set Classification

Read this document and assign it to one of these categories: [list your categories]. Provide a one-sentence justification for your choice and flag any ambiguous cases.

Open in a Project →

Image Dataset Labeling

Describe this image in 2-3 sentences, focusing on the main subject, setting, and any notable details. Then provide 5-7 keyword tags suitable for search indexing.

Open in a Project →

Audio Transcription Enrichment

Transcribe this audio file and identify distinct speakers if multiple voices are present. After the transcript, summarize the overall tone and any action items or decisions mentioned.

Open in a Project →

Overnight Data Extraction

Extract all contact information, dates, and monetary amounts from this document. Return results as a JSON object with keys: names, emails, phones, dates, amounts. If a field is missing, use null.

Open in a Project →

Compare with

Compare Google: Gemini 2.5 Flash (batch) with…

More language models

See all language models →