> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apiyi.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini 4 Hydrogen Is Live: Faster, Cheaper Agent Execution

> Google's speed tier of Gemini 4 is now on APIYI: a 1M shared window, four thinking levels with high as default, 57% on Terminal-Bench 4.1, and $1.50/$7.50 per 1M tokens. We tested both endpoints, tools, audio/video input and a 326K-token document.

## Key Takeaways

* **Available now**: Google released Gemini 4 Hydrogen on 12 November and APIYI connected on 14 November. Model name `gemini-4-hydrogen`, available in the `default` and `svip` groups on both Gemini-native and OpenAI-compatible endpoints
* **Priced at \$1.50 / \$7.50** per 1M tokens, with cached input at \$0.075. The price is **flat across the full 1M window** — a standard rate, not a limited-time promotion, with no expiry date
* **Google's "daily execution" tier**: adapted from Carbon and positioned as the upgrade path from 3.8 Flash. At the default `high` level it finishes tasks **2.5x faster**, scores **57%** on Terminal-Bench 4.1 versus 18.2%, and cuts fabricated claims by more than 5x
* **One 1M window, four thinking levels**: input, thinking and output share 1,048,576 tokens; a synchronous request returns up to 65,536 tokens, with long decode for more; `thinkingLevel` supports `low`, `medium`, `high` and `xhigh`, defaulting to `high`
* **Tested before launch**: both endpoints, streaming, function calling with `thoughtSignature` round-trips, JSON Schema, image, PDF, audio, video, a 326K-token needle test and six-way concurrency all passed

## Background

The Gemini 4 family shipped three models in three weeks: Argon for long-horizon work (opened 13 October), Carbon for the hardest problems (opened 10 November), and Hydrogen for everyday execution (12 November). It drops the Flash branding, but Google's upgrade advice for 3.8 Flash users is to move to Hydrogen.

Hydrogen is adapted from Carbon, and the trade-off is deliberate: **not the deepest single-shot reasoning, but speed, reliability and cost per step**. Google pitches it at coding, tool calls, test runs and document reading — the high-frequency agent loop rather than multi-day architecture work. It also introduced Orchestrator: Carbon explores and plans, Hydrogen executes, and Google says the pair reaches near-Carbon success rates with 24% less quota and 19% faster completion on average.

APIYI connected two days after launch, using the same access pattern as the rest of the Gemini 4 family: either the native endpoint or the OpenAI-compatible one.

## Detailed Analysis

### Official benchmarks

All figures below are **published by Google** and were not independently reproduced by APIYI:

| Benchmark | What it measures | Gemini 4 Hydrogen |
| - | - | - |
| Artificial Analysis Intelligence Index | General intelligence | 51 (`high`) / 54 (`xhigh`); Argon is 50 |
| Terminal-Bench 4.1 | Terminal agent tasks | 57% vs 18.2% for 3.8 Flash |
| HLE-Diamond | Hard reasoning | 58.8% |
| Agents' Last Exam v1.1 | Long-horizon agent tasks | 71.6% |
| AutomationBench-AA | End-to-end business workflows | 76.1% |
| Multimodal Knowledge Agent | Multimodal knowledge work | 53.4% |
| Vals Tax Agent / Legal Research | Tax and legal research | 52.3% (#1) / 55.8% (tied #1) |

On safety, Google reports up to 81% less overreach and 79% less under-service in agentic tasks, 12x fewer misaligned actions across tool-use and coding evaluations, and more than 2.3x resilience to indirect prompt injection. These are vendor claims too; production deployments still need their own guardrails.

### Four thinking levels

`thinkingLevel` is the main cost and latency dial:

* `low`: fewest tokens and lowest latency, for high-throughput extraction, classification and short Q\&A
* `medium`: interactive coding, live UI prototyping and latency-sensitive tool calls
* `high` (default): complex coding, agent execution, tool calling and multimodal document work
* `xhigh`: new with Hydrogen, for multi-step problems where `high` falls short; Google warns it can overthink simple prompts and burn tokens for no better answer

Migration notes: 3.8 Flash defaulted to `medium` while Hydrogen defaults to `high`; `minimal` is no longer accepted (the native API returns an error), and `thinkingBudget` is deprecated — sending it together with the level returns 400. Thinking tokens are billed as output, so higher levels cost more.

### The 1M window and output limits

Input, thinking and output share **1,048,576 tokens** at a single flat rate, with no long-context surcharge. A synchronous request returns at most **65,536 tokens**; Google says long decode (background or continuation) can push toward 1M.

We ran a needle test on APIYI with a 520K-character document: `promptTokenCount` came back as **326,238** and the model found the buried ID in 29.7 seconds. **A full 1M window was not tested**; validate your own document shape at 200K–300K tokens before scaling up.

### API changes

Four things change when moving from 3.8 Flash:

* **Sampling parameters are ignored**: `temperature`, `top_p` and `top_k` are accepted but have no effect. Pipelines that relied on low temperature need prompt-level constraints or structured output instead.
* **No explicit caching**: `cachedContents` is unavailable; only implicit prefix caching for prompts of 4,096+ tokens applies, and hits are not guaranteed. Model costs as if there were no cache.
* **`thoughtSignature` must be returned**: multi-turn and tool calls need the signature from the previous response passed back verbatim, or the request may fail with 400. This is a Gemini 4 requirement, not Hydrogen-specific.
* **Knowledge cutoff is March 2026**, so time-sensitive work still needs search or your own data.

## Pre-launch testing

We ran 30 scenarios on APIYI at low concurrency, covering both endpoints, streaming, tools, structured output, multimodal input and long context.

**What passed**: non-streaming and streaming on both endpoints; function calling produced `get_weather({"city":"Beijing"})` and continued correctly after the tool result; JSON Schema returned valid JSON; receipt OCR, chart reading, PDF extraction, audio ID recall and video frame reading were all correct; the 326K-token needle test passed; six concurrent requests all succeeded.

**Latency**: 10 interleaved short requests gave a P50 of **3.5 seconds**. Streaming first token arrived in 7–10 seconds because thinking emits nothing until it finishes, then content arrives in chunks — frontends should show a "thinking" state rather than expect instant first-token output.

**One limit to avoid**: thinking tokens count against `max_tokens`. With a small budget such as 300, thinking-heavy requests returned empty content with `finish_reason=length`; 600 and above produced text reliably. **Keep `max_tokens` at 1024 or higher by default, and 2048+ for harder tasks.**

## Pricing and availability

| Item | Price (per 1M tokens) |
| - | - |
| Input | \$1.50 |
| Output (including thinking) | \$7.50 |
| Cached input | \$0.075 (95% off input) |

* Model name `gemini-4-hydrogen`, available in the `default` and `svip` groups.
* Native endpoint: `POST /v1beta/models/gemini-4-hydrogen:generateContent`; OpenAI-compatible endpoint: `POST /v1/chat/completions`.
* Unlike Carbon and Argon's limited-time rates, Hydrogen's \$1.50 / \$7.50 is the **standard price** with no expiry.
* Recharge promotions can be stacked; see [Recharge Promotions](https://docs.apiyi.com/en/faq/recharge-promotions.md).

> [!NOTE]
>
> Model pricing is aligned with the official site and may change; the table above is for reference only. The **Model Pricing** tab in the top navigation is authoritative: [Model Pricing](https://docs.apiyi.com/en/models/index.md).

## Summary and recommendations

Hydrogen is not a cheaper Carbon; it is the **default execution tier**. Reach for it for everyday coding, tool loops, document processing and multimodal extraction, and keep Carbon or Argon for planning, architecture and root-cause work.

Suggested starting points:

* **Start at the default level**: run `high`, then evaluate whether `medium` or `low` meets your quality bar, or `xhigh` is needed.
* **Give `max_tokens` room**: thinking is part of the budget, and a small cap yields an empty response.
* **Return signatures**: pass `thoughtSignature` back verbatim in multi-turn and tool flows.
* **Test long documents in stages**: 326K tokens is verified; stress-test larger inputs first.
* **Model cost without cache**: implicit caching helps when it hits, but it is not guaranteed.

The model is ready to call on APIYI with the same access pattern as the rest of the Gemini family.

> [!NOTE]
>
> Official sources: `blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-hydrogen/` and `deepmind.google/models/model-cards/gemini-4-hydrogen/`; data retrieved 13 November 2026.


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