llmreference

Gemini 3.5 Models by Google DeepMind

Google DeepMindProprietaryHighlight
1 model2026Up to 1.0M ctxFrom $1.5/1M input

About

Gemini 3.5 is Google DeepMind's Flash-tier model family delivering sustained frontier-level intelligence for agentic and coding tasks at higher speed and lower cost than larger Gemini models.

Current Variants

Use-when guidance is derived from seed capabilities, context, release, and replacement fields.

1 in view

Use when the workload needs 1.0M context, reasoning, and tool use.

2026-051.0M contextreasoningtool use

Release Timeline

1 release group
2026-05
1 current
Gemini 3.5 Flash
1.0M contextreasoningtool use
Current

Specifications(1 models)

Gemini 3.5 model specifications comparison
ModelReleasedContextVisionMultimodalReasoningFn CallingTool UseStructured OutputsCode Exec
Gemini 3.5 Flash2026-051MYesYesYesYesYesYesYes

Available From(2 providers)

Pricing

Gemini 3.5 model pricing by provider
ModelProviderInput / 1MOutput / 1MType
Gemini 3.5 FlashGoogle AI Studio$1.5$9Serverless
Gemini 3.5 FlashGCP Vertex AI$1.5$9Serverless

Comparisons

All comparisons →

Frequently Asked Questions

What is Gemini 3.5 used for?
Gemini 3.5 is used for vision and multimodal work, reasoning, and agent workflows and tool use. The family description and listed model capabilities point to those workloads as the best fit.
How does Gemini 3.5 compare to Gemma 4?
Gemini 3.5 by Google DeepMind is strongest where you need vision and multimodal work, while Gemma 4 by Google DeepMind is the closest related family to check for vision and multimodal work. Gemini 3.5 has 1 listed variant and reaches up to 1.0M context, while Gemma 4 reaches up to 256K context, so compare the specs and pricing tables before choosing a production model.
Which Gemini 3.5 model should I use?
For the lowest listed input price, start with Gemini 3.5 Flash through Google AI Studio at $1.5/1M input tokens. For the most capable/latest local choice, evaluate Gemini 3.5 Flash with 1.0M context and reasoning, tool use, function calling, structured outputs, and multimodal inputs.

Models(1)