Gemini 1.5 Flash
Gemini 1.5 Flash is worth evaluating for rag, long context, and classification when its provider route and context window match the workload.
Use it for
- Teams evaluating rag, long context, and classification
- Workloads that can use a 1m context window
- Buyers comparing 2 tracked provider routes
Do not use it for
- Vision or document-understanding workloads
- Family
- Gemini 1.5
- Released
- 2024-05-14
- Context
- 1m
- Architecture
- Decoder Only
- Knowledge cutoff
- 2024-05
- Specialization
- general
- Training
- finetuned
Cheapest of 2 routes · GCP Vertex AI
About
Gemini 1.5 Flash is a large language AI model by Google, crafted for speed and efficiency in high-volume scenarios 145. As a lightweight model, it's optimized for fast processing and cost-effectiveness, making it ideal for real-time applications and high-frequency tasks 567. With its multimodal capabilities, Gemini 1.5 Flash effectively processes and reasons across multiple data types, including text, images, audio, video, and PDFs 145. Despite its smaller size compared to Gemini 1.5 Pro, it excels in tasks like summarization, chat applications, and data extraction from lengthy documents, employing "knowledge distillation" to transfer essential knowledge from larger models 5. Additionally, it features an extensive context window of up to 1 million tokens, allowing it to manage large information volumes effectively 456.
Gemini 1.5 Flash is a model in the Gemini 1.5 family. The structured metadata tracks a 1m-token context window and structured outputs. This page tracks provider routes through GCP Vertex AI and Google AI Studio, with the cheapest tracked route listed at $0.075 input and $0.3 output per 1M tokens. Headline tracked benchmarks include MMLU PRO 59.1.
Top use-case fit
RAG
Included by capability and metadata signals in the decision map.
Long context
Included by capability and metadata signals in the decision map.
Classification
Q/$ C1 relevant benchmark in the decision map.
Provider price ladder
Compare all 2Compare API pricing across 2 providers for input and output tokens, batch, and cached reads when available.
| Provider | Input / 1M | Output / 1M | Route |
|---|---|---|---|
| GCP Vertex AI | $0.075 | $0.300 | Serverless |
| Google AI Studio | - | - | ServerlessPartial |
Capabilities
Benchmark peer barsfor Classification
Benchmark scores(1)
| Benchmark | Score | Version | Source |
|---|---|---|---|
| MMLU PRO | 59.1 | — | https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro |
Migration checks
No linked migration route is available for this model yet.