Gemini 3.8 Flash (batch)

Overview

Gemini 3.8 Flash (batch) is a Google model released on September 2, 2026. Google describes it as its most intelligent Flash model, with significant gains over Gemini 3.7 Flash in software engineering, agentic tasks and multi-step reasoning. Those claims position it for work that benefits from more involved problem solving, though the available facts do not quantify the gains.

The model accepts text, images, video, files and audio, and produces text. It has a 1,048,576-token context window and a maximum output of 65,536 tokens. Reasoning, vision and tools are supported. The batch designation distinguishes this listing from Gemini 3.8 Flash; the facts provide no further description of batch behavior or scheduling.

For builders, the notable combination is broad input modalities, a large context window and tool support. That may suit applications that need to bring different kinds of material into a text-producing workflow, or pass substantial context into a request. Suitability still depends on how a system handles the model's actual inputs, outputs and operating constraints.

Key features

• Multimodal input: accepts text, image, video, file and audio inputs, with text output.
• Large context: supports a context window of 1,048,576 tokens.
• Reasoning: reasoning is listed as supported, and Google highlights multi-step reasoning among the areas improved over Gemini 3.7 Flash.
• Tool support: tools are supported, which makes it a candidate for workflows that connect model responses to external actions or services. The available facts do not specify particular tools or integrations.
• Vision: vision is supported, alongside image, video and file inputs.
• Longer responses: maximum output is 65,536 tokens.

Google also highlights software engineering and agentic tasks as areas of improvement over the previous generation. These are directional descriptions rather than task-specific benchmarks: no scores, evaluation methods or measured performance figures are provided here.

Pricing

Input costs USD 0.375 per 1M tokens. Output costs USD 1.875 per 1M tokens. The separate input and output rates make usage estimates dependent on both the amount of prompt material and the length of generated responses. The facts do not specify other charges or pricing conditions.

Platforms

The model is identified as Google’s, with the model ID google/gemini-3.8-flash:batch. Its listed official site is OpenRouter. The supplied facts do not name additional platforms or provide deployment details, so teams should verify that the route they plan to use supports the inputs and tool behavior their application requires.

Who it's for

This model is worth considering for teams building text-producing applications that need to accept several input formats, work with a substantial context window, or use reasoning and tools. Software engineering and agentic workflows are especially relevant areas to evaluate given Google's stated improvements.

It may also fit teams comparing versions within Google's Flash family. The batch listing has a distinct price and model ID from the standard-named listing only insofar as the facts identify it separately; the supplied details do not explain operational differences between them. Compare the batch option with Gemini 3.7 Flash (batch) and other family members based on the requirements and rates that matter to your stack.

Pros and cons

Pros

• Supports text, image, video, file and audio inputs.
• Offers a 1,048,576-token context window and a maximum output of 65,536 tokens.
• Includes listed support for reasoning, vision and tools.
• Google identifies software engineering, agentic tasks and multi-step reasoning as areas of significant improvement over Gemini 3.7 Flash.

Cons

• The stated improvements are not accompanied by benchmarks or quantified results in the available facts.
• The facts do not explain batch operation, tool integrations or deployment options.
• Output costs USD 1.875 per 1M tokens, five times the USD 0.375 per 1M-token input rate.

Alternatives

For a nearby family comparison, consider Gemini 3.7 Flash (batch), which is the predecessor named in Google's improvement claims. Other listed family options include Gemini 3.6 Flash, Gemini 3.6 Flash (batch), Gemini 3.5 Flash Lite and Gemini 3.5 Flash Lite (batch). The supplied facts do not give their specifications or prices, so a direct value comparison requires those details.

For other discovery paths, browse Google models, newest models, vision language models, reasoning models, longest-context models or models with tool calling.

Verdict

Gemini 3.8 Flash (batch) has a broad input range, a million-token-scale context window, tool and reasoning support, and a clear set of intended strengths in software engineering and agentic work. Its stated gains over Gemini 3.7 Flash are promising context for evaluation, not a substitute for measured results. Teams should weigh the USD 0.375 per 1M-token input price against USD 1.875 per 1M-token output, and verify that the batch route fits their integration needs. It is a credible candidate for multimodal and context-heavy workflows, but the facts alone do not establish how it will perform on a particular workload.