From Gemini 3.8 Flash to Hydrogen: 5 API configuration and compatibility changes that must be adjusted

Author’s note: Moving from Gemini 3.8 Flash to Hydrogen means you’ll need to adjust the SDK and context handling, thinking parameters, sampling configuration, caching, and signature management for multi-turn tool calls—all at the same time.

Google officially released Gemini 4 Hydrogen on November 12, 2026. Its API model ID is gemini-4-hydrogen, and it’s positioned for everyday coding and AI agent execution. Many teams’ first instinct is to swap the model name in their code and ship it—only to hit HTTP 400 errors, parameters that have no effect, multi-turn tool-call failures, and truncated long outputs. This article walks through the 5 API configuration changes you must make when upgrading from Gemini 3.8 Flash to Hydrogen, each covered in the order “old behavior → new behavior → how to change it → typical impact.”

This is not a launch-event recap. The parameter names, valid values, SDK version numbers, and error types in this article all come from Google Cloud’s official migration guide and model spec documentation. Third-party benchmarks and community feedback are sourced separately.

Key takeaway: After reading this, you’ll have a checklist you can work through item by item to update your existing Gemini 3.8 Flash call code and migrate your agent and batch workloads to Hydrogen smoothly.

5 Terms You Need to Know Before Migrating to Hydrogen

Before you start changing code, let’s get aligned on a few concepts that come up again and again in the migration guide:

• thinking_level: a thinking-level parameter that controls how deeply the model reasons. Hydrogen uses it in place of the old thinking_budget. The valid settings are Low, Medium, High, and XHigh.
• thought_signature: a thinking signature the model returns in its response. You must preserve it exactly and send it back during multi-turn conversations and tool calls, otherwise the model can’t pick up the reasoning state from earlier.
• continuation_token: a continuation marker that links to the next segment of a long output. Once a single segment hits its limit, you can use it to keep generating.
• Implicit caching: the platform automatically reuses qualifying input prefixes, and the matched portion is billed at the cache price. You don’t need to create cache objects manually.
• Context window: the range of information a single model invocation can hold. On Hydrogen, input, output, thinking, and tool calls all share a unified budget of 1,048,576 tokens.

1. Why Upgrading to Hydrogen Isn’t Just Swapping the Model ID

Gemini 3.8 Flash and Hydrogen may both be in the Gemini family, but their API behavior has quite a few incompatibilities. Per the official migration guide, thinking_budget and the MINIMAL thinking level aren’t supported on Hydrogen — the request just returns HTTP 400. Sampling parameters like temperature, top_p, and top_k don’t throw errors; they’re silently ignored. The first kind of problem shows up during testing. The second kind can leave you thinking “the parameters are still taking effect.”

The other two risks are sneakier. First, signature handling for multi-turn tool calls is stricter. Once the conversation history has been compressed or trimmed, a signature that no longer matches the prefix can trigger HTTP 400. Second, the default thinking level moves from Medium to High. If you don’t set it explicitly, the same request burns more thinking tokens and takes longer. The table below gives an overview of the five adjustments:

Part 2: Adjustment 1 — Hydrogen’s Model ID, SDK Version, and Context Limits

Old behavior → new behavior: Back in the 3.8 Flash era, most projects didn’t care much about SDK versions. After migrating to Hydrogen, the model ID has to become gemini-4-hydrogen; and if you need ultra-long output, the SDK version turns into a hard requirement. The minimum versions listed in Google’s official migration guide are:

How to adjust Hydrogen context and long output

Hydrogen’s 1,048,576 tokens is a unified budget — input and conversation history, thinking tokens, tool-call-related tokens, and the final output all get deducted from that single pool. In other words, you can’t feed in 1 million tokens and then get another 1 million tokens of output on top. For long-document analysis agents, you need to proactively reserve room on the input side for thinking and tool calls.

A single foreground generation segment typically caps out at 65,536 tokens. Once you go past that, there are three options: use auto-continuation with a supported SDK, set background=true in the Interactions API for background long generation, or maintain your own continuation_token and stitch the segments together manually. One thing to watch out for: the official getting-started docs state that long-output continuation currently requires the v1beta1 API — Agent Platform’s v1 doesn’t support it.

Typical impact: Businesses that batch-generate long reports and long code files are the most likely to get burned. Our recommendation: upgrade the SDK first, then switch the API version to v1beta1, and finally run a regression test with a request that exceeds 65,536 tokens.

pip install -U "google-genai>=2.34.0"
npm install @google/genai@^2.32.0

III. Adjustment 2: Hydrogen’s thinking_budget Becomes thinking_level

Old behavior: 3.8 Flash used thinking_budget to control thinking length by token count and could also select the MINIMAL level, with Medium as the default.

New behavior: Hydrogen only accepts thinking_level, and the valid values are the four levels Low, Medium, High, and XHigh, with High as the default. Continuing to pass thinking_budget or MINIMAL will return HTTP 400. The official recommended uses are: Low for classification, Medium for routine coding, High for multi-step agents, and XHigh for complex, deep reasoning.

The third-party organization Artificial Analysis completed an independent evaluation of all four levels on November 12 (Intelligence Index v5.0.1), which helps illustrate the differences between levels. The time and cost values in the table below are averages across its standardized benchmark tasks; they don’t represent the actual cost of a single conversation for an ordinary user:

Looking at the data, High scores 10 points higher than Medium, while XHigh scores only 3 points higher than High, yet the evaluation cost jumps from $1.87 to $3.16. This shows that the highest level isn’t necessarily the best value, and the same model name can also differ greatly in capability across different levels.

Hydrogen thinking_level modification example

The following examples use the official Google Gen AI SDK syntax. For the casing of enum values, refer to the type definitions in the SDK version you have installed:

from google import genai
from google.genai import types

client = genai.Client()

# Old approach (3.8 Flash): returns HTTP 400 on Hydrogen
# thinking_config=types.ThinkingConfig(thinking_budget=2048)

# New approach: choose Medium for routine coding tasks
coding_config = types.GenerateContentConfig(
    thinking_config=types.ThinkingConfig(thinking_level="medium"),
)

# Multi-step agent: choose High (also the default, but explicitly setting it is recommended)
agent_config = types.GenerateContentConfig(
    thinking_config=types.ThinkingConfig(thinking_level="high"),
)

resp = client.models.generate_content(
    model="gemini-4-hydrogen",
    contents="修复这个函数中的空指针问题：...",
    config=coding_config,
)
print(resp.text)

Typical impact: Because thinking tokens are billed at output rates, projects that don’t explicitly set a level will automatically fall back to High after migration, and both bills and latency will increase. We recommend mapping the old thinking_budget ranges to levels: smaller budgets to Low or Medium, larger budgets to High.

4. Adjustment 3: Clean Up the Deprecated Generation Parameters in Hydrogen

Old behavior: 3.8 Flash let you tune output randomness with temperature, top_p, and top_k, and it also supported penalty parameters and multiple candidates.

New behavior: Hydrogen’s default temperature is 1.0, custom values don’t take effect, top_p is fixed at 0.95, and top_k is fixed at 64. All three are ignored once passed in. The official migration checklist also asks you to handle the following fields:

The official guidance notes that some legacy parameters are simply ignored, while others cause request validation to fail. So troubleshooting can’t be one-size-fits-all: on a 400, check thinking_budget, MINIMAL, and the fields that need removing first; if the output style doesn’t match expectations, then check whether you’re still relying on temperature parameters.

Simplified request config for Hydrogen

config = types.GenerateContentConfig(
    system_instruction=STABLE_SYSTEM_PROMPT,   # Put stable content first — helps implicit caching
    thinking_config=types.ThinkingConfig(thinking_level="medium"),
    response_mime_type="application/json",      # Structured output is still supported
    max_output_tokens=8192,
)

Typical impact: Projects that used to rely on low temperature to keep JSON output stable should switch to structured output instead; projects that relied on prefilled model turns to control format need to move the formatting requirements into the system prompt.

5. Adjustment 4: Hydrogen Moves from Explicit to Implicit Caching

Old behavior: On 3.8 Flash, you could create a cache object first, then reference it in a request with cached_content="cachedContents/xxx".

New behavior: Hydrogen doesn’t support explicit context caching — only implicit caching, and persistent implicit caching is on by default. The minimum cacheable prefix length drops from 6,144 tokens on 3.8 Flash to 4,096 tokens, so relatively short shared system prompts and fixed reference material are much more likely to hit the cache.

How to adapt: Remove every caches.create call and every cached_content reference, and instead optimize the order in which you assemble your prompt. Google recommends putting stable shared content at the front of the input and the ever-changing user question at the end:

1. System prompt and role definition (almost never changes)
2. Shared knowledge base, API docs, coding standards (changes rarely)
3. Conversation history (appended turn by turn)
4. Current user question (changes every time)

Typical impact: On pricing — per the official Google AI Studio pricing page (verified November 13, 2026, in USD per million tokens) — implicit cache hit input for standard Hydrogen calls is $0.075, and $0.0375 in Batch and Flex modes, far below the standard input rate of $1.50. If you drop dynamic variables (timestamps, user IDs, and the like) at the start of the prompt, the prefix is different every time and the cache will never hit.

6. Adjustment 5: Maintaining thought_signature correctly in Hydrogen

Old behavior: On 3.8 Flash, plenty of agent frameworks rebuild the conversation history themselves, keeping only the text and the tool call results — so when the signatures got dropped, there was usually no visible impact.

New behavior: In Hydrogen, multi-turn conversations need to keep the signatures of all relevant parts from earlier turns, including the signature carried by the final text part. The official migration guide lays out the rules for parallel tool calls like this:

• Multiple parallel functionCalls must live in the same model message;
• thought_signature goes only on the first parallel functionCall part;
• Subsequent functionResponses must come back with the matching call ID, name, and order;
• When the same model reuses a signature and the history prefix has changed, you may get an HTTP 400 by default.

How to update signature handling in Hydrogen

The safest approach is not to rebuild the parts by hand, but to append the content object returned by the model to the history as-is:

history = [types.Content(role="user", parts=[types.Part(text=question)])]
resp = client.models.generate_content(
    model="gemini-4-hydrogen", contents=history, config=agent_config
)
model_content = resp.candidates[0].content
history.append(model_content)          # append it as-is, and the signature comes along with it

calls = [p.function_call for p in model_content.parts if p.function_call]
responses = [
    types.Part.from_function_response(name=c.name, response=run_tool(c))
    for c in calls                      # keep the same order as the functionCall parts
]
history.append(types.Content(role="user", parts=responses))

For agents that compact or trim history (sliding windows, summary replacement, and so on), the history prefix is bound to change. In that case you can configure prefix_mismatch_behavior="DROP" as described in the migration guide, so the model drops the old signature when the prefix doesn’t match instead of throwing an error. For exactly where this field goes in the request, defer to the official migration guide.

Typical impact: custom agent harnesses, coding assistants that retrieve files in parallel, and long-running chat bots with context compaction are hit the hardest — it’s worth writing a dedicated integration test for signature round-tripping.

7. Hydrogen Migration Checklist and Minimal Invocation Example

Quick validation through the OpenAI-compatible endpoint

If your team’s production code runs on the OpenAI SDK, start by validating Hydrogen’s output quality through the compatible endpoint before deciding whether to migrate to the native SDK. Whether a model is listed and which parameters can be passed through depends on the platform’s model list and docs:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"  # APIYI OpenAI-compatible endpoint
)

resp = client.chat.completions.create(
    model="gemini-4-hydrogen",
    messages=[{"role": "user", "content": "用 Python 写一个带重试的 HTTP 请求函数"}],
)
print(resp.choices[0].message.content)

import json
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://api.apiyi.com/v1"  # One unified endpoint, makes old-vs-new model comparison easy
)

TOOLS = [{
    "type": "function",
    "function": {
        "name": "read_file",
        "description": "读取仓库中的文件内容",
        "parameters": {
            "type": "object",
            "properties": {"path": {"type": "string"}},
            "required": ["path"],
        },
    },
}]

STABLE_SYSTEM_PROMPT = "你是代码修复助手。只修改与问题相关的代码。"

def read_file(path: str) -> str:
    with open(path, encoding="utf-8") as f:
        return f.read()

def run_agent(question: str, model: str = "gemini-4-hydrogen", max_steps: int = 8) -> str:
    # Keep the stable system prompt at the front to help with implicit cache hits
    messages = [
        {"role": "system", "content": STABLE_SYSTEM_PROMPT},
        {"role": "user", "content": question},
    ]
    for _ in range(max_steps):
        resp = client.chat.completions.create(
            model=model, messages=messages, tools=TOOLS
        )
        msg = resp.choices[0].message
        # Write the entire assistant message back into history as-is, without trimming any fields
        messages.append(msg.model_dump(exclude_none=True))

        if not msg.tool_calls:
            return msg.content

        # Return each result one by one, following the original order of tool_calls
        for call in msg.tool_calls:
            args = json.loads(call.function.arguments)
            result = read_file(**args) if call.function.name == "read_file" else ""
            messages.append({
                "role": "tool",
                "tool_call_id": call.id,
                "content": result,
            })
    return "达到最大步数，任务未完成"

if __name__ == "__main__":
    print(run_agent("检查 utils.py 中的 parse_date 函数是否有时区问题"))

🚀 Getting-started tip: While you’re migrating, keep 3.8 Flash around as a control group and send the same batch of real requests to both the old and new models. With APIYI apiyi.com you can call both models using a single API key, and new users get free test credits — perfect for running a small-traffic comparison before switching everything over.

Frequently Asked Questions (FAQ)

Hydrogen Migration Key Takeaways

• It’s more than a rename: Switching the model ID to gemini-4-hydrogen is only step one — four other configuration items need to be adjusted in tandem.
• The SDK is a hard requirement: Long-output continuation requires Python >= 2.34.0 and JS >= 2.32.0 (Interactions API), and you must use v1beta1.
• Thinking parameters get an upgrade: thinking_budget and MINIMAL return HTTP 400, and the default tier is now High.
• Sampling parameters stop working: temperature, top_p, and top_k are ignored; penalty, multiple candidates, and prefill turns need to be removed.
• Caching depends on structure: Explicit caching is gone — a stable prefix has to reach 4,096 tokens before it hits implicit caching.
• Signatures must be complete: Parallel call signatures appear only on the first functionCall, responses come back in order, and compressed history can be set to DROP.
• Compare before switching: Call the old and new models with the same key through APIYI apiyi.com, validate with a small amount of traffic, then migrate everything.

Summary

Key points for upgrading from Gemini 3.8 Flash to Hydrogen:

1. Foundation layer: Update the model ID and SDK, and get a handle on the unified 1,048,576-token quota and the 65,536-token output limit per segment;
2. Parameter layer: Replace thinking_budget with thinking_level, and clean up the deprecated sampling and generation fields;
3. State layer: Redesign your prompt structure so it hits implicit caching, and preserve and return thought_signature exactly as-is.

Work through the checklist in this article item by item, write regression tests for tool calls and long outputs, and then switch real traffic over to the new model. To quickly compare how the old and new models perform, APIYI apiyi.com offers an OpenAI-compatible interface plus free test credit for new users.

📚 References

1. Gemini 4 Hydrogen Developer Migration Guide: Official compatibility changes and migration checklist
2. Link: docs.cloud.google.com/gemini-enterprise-agent-platform/models/guides/gemini-4-hydrogen

3. Description: The primary source for details like the minimum SDK version, signing rules, and prefix_mismatch_behavior

4. Getting Started with Gemini 4 & Migration Differences: Notes on long output and API versions

5. Link: docs.cloud.google.com/gemini-enterprise-agent-platform/models/start/get-started-with-gemini-4

6. Description: Explains that the continuation feature requires the v1beta1 API

7. Google Cloud Hydrogen Model Specs: Table of context, parameters, and feature support

8. Link: docs.cloud.google.com/gemini-enterprise-agent-platform/models/gemini/4-hydrogen

9. Description: Fixed values for temperature, top_p, and top_k, plus cache support details

10. Official Google Launch Blog: Gemini 4 Hydrogen announcement

11. Link: blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-hydrogen/

12. Description: Product positioning and official comparison data against 3.8 Flash

13. Google AI Studio Pricing Page: Official API pricing

14. Link: aistudio.google.com/docs/pricing

15. Description: Standard, Batch, Flex, and Priority pricing, plus cache-hit pricing

16. Artificial Analysis Independent Benchmark: Evaluation across four thinking levels

17. Link: artificialanalysis.ai/models/gemini-4-hydrogen

18. Description: Third-party intelligence index, benchmark duration, and cost data

19. Google DeepMind Model Card: Capabilities, limitations, and safety evaluation

20. Link: deepmind.google/models/model-cards/gemini-4-hydrogen/
21. Description: Known limitations and official cross-model benchmarks

Author: APIYI Technical Team
Technical Discussion: If you run into compatibility issues during migration, feel free to reach out to us through the APIYI website. For more integration resources, check out the docs.apiyi.com documentation center.

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