Gemini 4 Carbon: deep multi-step reasoning with thinking depth you control
Google's Gemini 4 Carbon is a reasoning model for deep multi-step coding, math, and science. Built on Gemini 4 Argon, it spends more thinking per turn, with a thinking depth you set from low to high.
Gemini 4 Carbon is Google's frontier reasoning model, built on Gemini 4 Argon and tuned to think harder on the problems that test a model's limits. Argon handles everyday engineering and knowledge work; Carbon spends more thinking per turn on complex, multi-step coding, mathematics, and science. It explores ideas, reasons deeply, and runs its own verification before it answers, with a thinking depth you set. On Segmind it runs behind one synchronous call that returns the answer directly.
ModelGemini 4 CarbonDeep multi-step reasoning for hard coding, math, and science.What it does well
Deep, self-checked reasoning
Carbon is strongest when a problem has many chained steps and one correct answer. At high effort it works through each step and checks itself before committing to a result. On a Hardy-Weinberg genetics problem it conditioned on the dominant phenotype — the step a shallow pass skips — and reached 0.571, where a quick read stops at 0.48. The effort setting lets you trade latency for that depth.
In a large population that is in Hardy-Weinberg equilibrium for a single gene with two alleles (dominant A, recessive a), 16% of individuals show the recessive phenotype. Reason through the problem step by step: find the allele frequencies, work out the three genotype frequencies, and then determine what fraction of the individuals showing the DOMINANT phenotype are heterozygous carriers. Give that fraction as a decimal rounded to three decimal places.
Strong across coding, math, and science
Carbon is built for tasks that reward deliberation. It reasons across physics, chemistry, and the life sciences, synthesizing data and working multi-step problems to a checkable answer. It also handles complex coding and debugging across larger, multi-file changes, where its extended verification loops catch mistakes a single pass misses. State the output format you want, and ask it to show its steps when you need a result you can check.
Multimodal inputs and grounded answers
Carbon reads more than text. Attach an image, audio clip, video, or PDF — and send extra files alongside the prompt — for OCR, chart reading, transcription, and document questions. Turn on web search to ground time-sensitive questions in live Google Search results. For machine-readable output, set a response format: plain JSON, or a strict JSON schema the reply has to match. A large context window lets you paste long documents, transcripts, or code directly into the prompt.
Recommended settings
Match the thinking depth to the task, and state exactly what you want back. These are the settings that change Carbon's behavior most.
| Setting | Value | When |
|---|---|---|
| effort | high | Hard coding, math, and science where correctness matters more than speed |
| effort | low | Quick lookups and interactive drafting |
| response_format | json_object or json_schema | When the output feeds another system |
| web_search | true | When the answer depends on recent facts |
Good to know
- Higher effort takes longer and produces more reasoning; the hardest prompts can occasionally be slow or time out. Step effort down when speed matters.
- Carbon's knowledge has a cutoff (March 2026, per Google's model card). Enable web search for very recent facts.
- It is built for hard, multi-step problems. For simple, latency-sensitive prompts, the lighter Gemini 4 Argon answers faster.
- Only the prompt is required. Image, audio, video, PDF, and files are optional context you add when a question needs them.
Use it from the API
Send the prompt and the effort level in one call; the answer comes back directly.
# pip install "segmind>=1.1.0"
# export SEGMIND_API_KEY="YOUR_API_KEY"
import segmind
result = segmind.run(
"gemini-4-carbon",
prompt="In a large population that is in Hardy-Weinberg equilibrium for a single gene with two alleles (dominant A, recessive a), 16% of individuals show the recessive phenotype. Reason through the problem step by step: find the allele frequencies, work out the three genotype frequencies, and then determine what fraction of the individuals showing the DOMINANT phenotype are heterozygous carriers. Give that fraction as a decimal rounded to three decimal places.",
effort="high",
)
print(result.get("output"))# 1. Submit the request; the reply carries its request_id
REQUEST_ID=$(curl -s -X POST "https://api.segmind.com/v2/gemini-4-carbon" \
-H "x-api-key: YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "In a large population that is in Hardy-Weinberg equilibrium for a single gene with two alleles (dominant A, recessive a), 16% of individuals show the recessive phenotype. Reason through the problem step by step: find the allele frequencies, work out the three genotype frequencies, and then determine what fraction of the individuals showing the DOMINANT phenotype are heterozygous carriers. Give that fraction as a decimal rounded to three decimal places.",
"effort": "high"
}' | jq -r .request_id)
# 2. Check its status until it is COMPLETED
curl -s "https://api.segmind.com/v2/requests/$REQUEST_ID/status" \
-H "x-api-key: YOUR_API_KEY"
# 3. Fetch the result
curl -s "https://api.segmind.com/v2/requests/$REQUEST_ID" \
-H "x-api-key: YOUR_API_KEY"Resources
- Try Gemini 4 Carbon on Segmind
- API reference
- Gemini 4 Carbon — Model Card — what Carbon is, its inputs and outputs, intended uses, benchmark results across coding, science, and math, long context, multimodal, and limitations (Google DeepMind).
- Gemini 4 Carbon is now available — Google's launch overview: higher-precision knowledge work, agentic coding, and frontier scientific and mathematical reasoning.
- #text-generation
- #model-launch
- #reasoning
- #llm
