Gemini 3.5 Flash is Google's stable multimodal reasoning model, focused on coding agents, subtask collaboration, and long-running workflows that require continuous progress. It delivers analysis, code, and structured results in text, making it suitable for balancing reasoning capabilities and response efficiency through repeated planning, tool calls, and feedback checks, while also using image understanding for complex tasks.
Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud/v1",
)
response = client.chat.completions.create(
model="gemini-3.5-flash",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
Clarify capacity, input and output, and invocation methods before choosing a model.
Native capacity and modality describe model capabilities; this platform's actual input formats, tool usage, and response methods vary by the selected invocation entry point.
Learn what gemini-3.5-flash can bring to your work.
Gemini 3.5 Flash focuses not on writing all the code at once, but on adapting to a cycle of analyzing requirements, proposing changes, receiving test feedback, and continuing to refine. You can input code snippets, error logs, and constraints together to have it generate patch suggestions and testing ideas; tests are executed by tools provided by the application, making it easy to retain records of each review round.
Million-scale native input capacity is suited to organizing lengthy code, documents, and task histories, while image understanding can supplement screenshots, charts, and interface information. Clearly specifying the items to compare, evidence to focus on, and delivery format in the prompt can keep the output centered on specific questions rather than merely compressing large amounts of material into a generic summary.
Tool workflows should be organized according to the tool definitions and result formats of the selected public interface. The model is responsible for planning, interpreting results, and generating call suggestions; querying, running code, and writing are completed by the execution environment provided by the application. Actual completion status should come from tool returns and verification records, not be judged solely from the model's description.
Start with specific tasks to find where the model can be effective.
Input relevant functions, failing cases, logs, and expected behavior, and have the model first list possible causes, then propose modification plans and a regression testing checklist. After the application runs the tests, feed the results back into the next round, ultimately delivering reviewable modification suggestions, risk notes, and test items, suitable for development assistance requiring multiple iterations.
Suitable for reviewing specifications or technical reports in stages: first extract constraints, then compare version differences, and finally create an action list. Require each suggestion to correspond to a location in the material and identify conflicts that remain unexplained; this type of continuous work better demonstrates the ongoing-task positioning of 3.5 Flash, rather than merely generating a one-time generalized summary.
Combine product screenshots, charts, and textual requirements into text-and-image messages, allowing the model to identify key content and break down to-dos, such as organizing interface issues, explaining chart changes, or generating acceptance steps. Output is organized as text or JSON for easy entry into a ticketing system; screenshot analysis and actual interface operation are different tasks, and clicks will not be completed automatically.
Choose based on task complexity, input materials, and expected results.
gemini-3.5-flash is the stable version invocation name, while gemini-3-flash-preview is the preview version name; clearly specify the target when integrating. Existing applications that have established workflows around the output format, tool loops, and test sets of 3.5 Flash can continue evaluating with this model. When comparing newer Flash models, validate them on the same tasks rather than judging migration benefits solely by version number.
When multimodal analysis, coding iteration, and continuous planning are needed, 3.5 Flash is a balanced choice. For simple classification, fixed-field extraction, and retryable batch processing, compare Flash Lite; for difficult reasoning and complex analysis, compare Pro. Focus on task success rate, number of tool rounds, and total output volume, and do not treat the length of a single response as a substitute metric for reasoning quality.
Based on the characteristics of gemini-3.5-flash, first validate small tasks whose results can be checked.
You can ask directly: Organize materials in stages according to business goals, compare options, and form a conclusion. For each stage, list the evidence used and information still needed, and output the inputs required for the next stage.
Define stage goals and tool responsibilities; use intermediate results and final deliverables to check whether ongoing tasks have deviated from requirements.
Use the full model ID gemini-3.5-flash, first confirm the public request format and available parameters on the API page, then connect your application. Retain result parsing, exception handling, and relevant evidence, and use the same set of real samples to evaluate whether it is suitable for continued use.
Before formal use, understand output quality and capability scope.
Answers to common questions when using gemini-3.5-flash.
Use Chat Completions, set model to gemini-3.5-flash, organize text and images in messages, and read responses from choices; for streaming interactions, handle incremental updates as described in the documentation. The capabilities and message format of native Generate Content should be verified independently; do not infer that the platform exposes all features from the vendor's native specification.
In Chat Completions, write message content as an array of content blocks, combining text and image_url, and place an accessible image URL or Base64 data URI in image_url.url. The text should specify the objects of interest and output requirements, such as extracting fields, comparing differences, or explaining charts.
Prepare the document text, table data, or clear page screenshots relevant to the question, and specify whether you need a summary, comparison, or particular information extracted. Submit content in formats supported by the selected public API; a PDF URL cannot be used as image_url. Request that results retain original-text locations, field evidence, and unconfirmed items, and verify key numbers against the source material.
Gemini 3.5 Flash uses reasoning tokens, and an output budget that is too small may be exhausted before the final answer is formed. The usage guide recommends setting max_tokens to 512 or higher; complex tasks should allow even more space, and you should use finish_reason, usage, and the actual text to determine whether the budget needs to be increased.
No. Google lists gemini-3.5-flash as Stable and gemini-3-flash-preview as Preview. To call this model, use the full ID gemini-3.5-flash; when replacing models, especially retest structured outputs, tool loops, and long-task performance rather than putting it into use immediately after only changing the name.
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