Gemini 4 Hydrogen Lays Out Its Full Specs: 1M Context Window, 4 Thinking Levels, 57% Terminal-Bench Score

Google has officially launched Gemini 4 Hydrogen. Publicly available details are concise, but the published metrics are very concrete: 1M token context window, 4 adjustable thinking levels, $1.50/$7.50 pricing, 51 points on Artificial Analysis, and 57% completion rate on Terminal-Bench 4.1. The original post also specifies the speed performance: it runs 2.5 times faster than Gemini 3.8 Flash.

This piece is sourced from MarkTechPost, written by Michal Sutter. You can access the original article here.

Let's add some background context first. Gemini 4 Hydrogen is described as a high-speed agentic model. Pain points for agent scenarios are usually very practical: multi-round tool calls, long task logs, code repositories and documentation all loaded into the context at the same time, which makes latency and cost quickly turn into unexpected billing burdens. The 1M token window means it can process extremely long materials in one go, but being able to fit the content does not equal being able to use it effectively. The 4 thinking levels, as the name implies, allow callers to adjust the reasoning intensity according to task difficulty, so users do not waste budget on simple tasks and can ramp up computing power for complex tasks when needed.

On the pricing side, the released materials only mark the $1.50/$7.50 figure without clarifying the unit. Per common industry practice, this most likely refers to input/output price per million tokens, but users should refer to the official product page for final confirmation. For teams building agents, this set of metrics is far more straightforward than benchmark scores: for the same round of tool calls, cost and latency will directly determine whether the workflow can be retained in the production pipeline.

The benchmark results are also clearly presented. It scores 51 on Artificial Analysis, and 57% on Terminal-Bench 4.1. Terminal-Bench focuses on terminal and real-world engineering tasks. The 57% score proves it has a decent completion rate on actual command-line type tasks, but it is still far from being fully hands-off and reliable for unattended use. Regular users may not find these numbers meaningful, but developers care more about whether it can maintain stable tool calling performance in long context scenarios, and how much of the 2.5x speedup translates to real-world production requests.

These are all the verifiable details available right now. The model's actual real-world performance, rate limits, and supported regions are not covered in the current release materials. Take these benchmark results as reference for now, and do not rush to adopt it for critical production workloads.