Configure the Gemini 3.8 Flash API

One-sentence takeaway

Google’s official materials show that the API model ID for Gemini 3.8 Flash is gemini-3.8-flash, aimed at long-horizon software engineering, autonomous agents, and complex enterprise workflows. Calls should use thinking_level (low, medium, or high) rather than migration-era settings such as temperature, top_p, top_k, or thinking_budget. This conclusion applies only to the Gemini API / Interactions API behavior documented by Google; it does not mean that third-party clients have already added synchronized support.

Use cases

• Suitable tasks: Long-horizon coding, multi-file refactoring, agents that require repeated tool calls, and complex multi-step analysis.

• Unsuitable tasks: Tasks requiring image or audio generation, the Live API, or non-text model output; the official model page lists image generation, audio generation, and Live API as unsupported.

• Applicable model version: The stable gemini-3.8-flash; the stable-version string on the official model page is this ID.

• Applicable client, agent, or API: Google AI Studio / Gemini API; the latest model guide demonstrates invocation through the Interactions API. Parameter mappings for other SDKs or third-party agents must be checked independently.

• Recommended reasoning levels and parameters: Start representative complex tasks at medium (the default); use low for latency-sensitive tasks and high for deep reasoning, mathematics, or difficult multi-step tasks. minimal is unsupported and returns an error.

Ready-to-use content

Minimum configuration from the official Python example (with the model ID and configuration fields preserved):

from google import genai

client = genai.Client()
interaction = client.interactions.create(
    model="gemini-3.8-flash",
    input="Analyze the race-condition risks in this code and provide a safe refactoring plan.",
    generation_config={
        "thinking_level": "medium"
    }
)

print(interaction.output_text)

Migration checklist:

model = "gemini-3.8-flash"
thinking_level = "low" | "medium" | "high"

Remove: temperature, top_p, top_k, candidate_count
Replace: thinking_budget -> thinking_level
Avoid: minimal (Gemini 3.8 Flash does not support it)

Test/workflow steps

1. Record the complete model string gemini-3.8-flash in request logs rather than logging only “Gemini Flash”.

2. Start with medium to establish quality, latency, and token baselines for representative tasks; then compare low for latency-sensitive tasks and high for complex tasks.

3. When migrating old calls, remove temperature, top_p, top_k, and candidate_count, and change thinking_budget to the string enum thinking_level.

4. If using a multi-turn Interactions API, normalize previous_interaction_id server-side according to the official migration guide; do not prefill model turns.

5. Record tool calls, failed loops, input/output tokens, and total latency; the claim of “fewer failed loops” is launch positioning and cannot replace your own task evaluation.

Raw evidence and data

Scope and limitations

• The official release announcement says that 3.8 Flash improves on tasks including DeepSWE v1.1, Vals Finance Agent V2, Harvey's Legal Agent Benchmark, and HLE-Verified. However, this article is categorized as configuration and does not recast vendor benchmark claims as independent evaluation conclusions.

• “The most intelligent Flash model” and “fewer failed loops” are Google’s product positioning. No single, publicly available third-party replication study exists, so these claims cannot support promises of a specific success rate.

• Token limits, capability switches, and pricing may change with the API, region, account tier, or subsequent updates. Recheck the official model and pricing pages before launch.

• thinking_level controls only the reasoning levels disclosed in the official documentation. It should not be inferred to represent a fixed number of reasoning tokens, fixed latency, or a quality guarantee.

• Computer use is marked Preview; production environments require additional permission isolation, tool auditing, and human confirmation.

Source excerpts or observations (for compliant short quotation only)

The actionable change in the official guide is to unify reasoning control under thinking_level, with medium as the default starting point. The official release announcement, meanwhile, emphasizes that the model performs additional reasoning and iterative tool calls on complex tasks. Neither replaces regression testing of your own agent harness.