gemini-4-carbon
Google Vertex AI/@eu/chat50% off
Gemini 4 Carbon is Google's strongest Gemini 4 reasoning model, built for complex multi-step coding and debugging, long-horizon agentic workflows, cybersecurity and open-ended scientific research. It reasons more deeply than Gemini 4 Argon over a 1M token context.MoreLess
Which id to call
vertex/gemini-4-carbon@euThis exact deployment on Google Vertex AI in eu, with no routing and no failover. Send it as the model field.
Input /1M
$1.10
$2.20 list
$0.06 cached
Output /1M
$5.50
$11.00 list
5.0x input
Context
1.0M
66K output
Added
Nov 2026
chat
Capabilities 7/8
Provider rates
What gemini-4-carbon costs
Provider prices per 1M tokens, updated November 10, 2026.moreless
These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.
This endpoint is discounted. List is $2.20 per 1M input and $11.00 per 1M output, and the rates below are what you pay. The discount applies to every request on this endpoint, with nothing to claim or enter.
Input /1M
$1.10
$2.20 list
Output /1M
$5.50
$11.00 list
Cache write /1M
$1.10
Cache read /1M
$0.06
What a workload costs
- 100K input + 10K output
- $0.17
- 1M input + 100K output
- $1.65
- 10M input + 1M output
- $16.50
At the rates above, before caching. A cache read costs $0.06 per 1M, so repeated context lands under these figures.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to vertex/gemini-4-carbon@eu. Existing OpenAI SDK code needs no other edit.
123456789101112131415from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1", ) response = client.chat.completions.create( model="vertex/gemini-4-carbon@eu", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Released 2026-10-28
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.
Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.
Same provider
More from Google Vertex AI
Newest first, on the same provider and the same key.
Reference
gemini-4-carbon questions
How much does gemini-4-carbon cost?
gemini-4-carbon is priced at $1.10 per million input tokens and $5.50 per million output tokens when accessed via Requesty. Those figures include a 50% discount on this endpoint, off a list rate of $2.20 per million input tokens and $11.00 per million output tokens. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.
What is the context window of gemini-4-carbon?
gemini-4-carbon has a context window of 1.0M tokens, with a maximum output of 66K tokens per response. That's roughly 1,398 words of input you can fit in a single prompt.
How does gemini-4-carbon perform on benchmarks?
gemini-4-carbon scores 60.0% on Intelligence Index. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can gemini-4-carbon do?
gemini-4-carbon supports vision input, tool calling, extended reasoning, prompt caching, web search, structured outputs (JSON schema), computer use. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use gemini-4-carbon with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "vertex/gemini-4-carbon@eu". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run gemini-4-carbon through Requesty?
Yes. gemini-4-carbon runs through Requesty's OpenAI-compatible API, served from Google Vertex AI in eu. You do not host the model yourself: point base_url at Requesty, set the model to "vertex/gemini-4-carbon@eu", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
What region is this deployment?
This variant of gemini-4-carbon is deployed in eu. Region-specific endpoints matter for data residency, latency to your users, and compliance requirements (GDPR, HIPAA). Other regions for the same model may be listed on the Google Vertex AI provider page.
Call gemini-4-carbon through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.