# Gemini 4 Carbon (Gemini Carbon 4) vs Polygres

Gemini 4 Carbon (Gemini Carbon 4) records 4,339,429 monthly visits with +12.7% month-over-month growth and Freemium pricing. Polygres records 4,018 monthly visits with -52.3% growth and Freemium pricing.

| Metric | [Gemini 4 Carbon (Gemini Carbon 4)](https://openaitools.app/tool/gemini-4-carbon) | [Polygres](https://openaitools.app/tool/polygres) |
| :--- | :--- | :--- |
| Website URL | https://deepmind.google/models/gemini/ | https://polygres.com |
| Categories | Large Language Models (LLMs), AI Models, AI Code Assistant, AI Agent | AI Developer Tools, AI Agent, AI Knowledge Graph, AI API |
| Pricing Model | Freemium | Freemium |
| Monthly Visits | 4,339,429 | 4,018 |
| MoM Growth Rate | +12.7% | -52.3% |
| Primary Market | United States | Global |
| Direct Visits | 899,124 | 1,605 |
| Review Score | 4.8 | 4.1 |
| Saved Count | 1,420 | 0 |
| Supported Platforms | Website, API | Website |

## Gemini 4 Carbon (Gemini Carbon 4)

Gemini 4 Carbon (frequently searched as Gemini Carbon 4) is Google DeepMind's flagship frontier model announced on October 28, 2026, arriving just two weeks after Gemini 4 Argon. Built for sustained, multi-day agentic engineering and autonomous cyberdefense, Gemini 4 Carbon debuted at #1 on the Artificial Analysis Intelligence Index v5.0 with a score of 59—outperforming Claude Opus 5.5 (58), GPT-6 Astra (57), and Gemini 4 Argon (50) while using 29% fewer output tokens per task than Claude Opus 5.5.

Across the cybersecurity defense lifecycle, Gemini 4 Carbon establishes a decisive frontier lead: it scores 88.2% on SEC-bench Pro for discovering real-world software vulnerabilities, 54.8% on ExploitGym for validating exploitability, and 71.0% on Collinear's CWE-bench for generating root-cause patches. Internal Google evaluations show more than a 2x uplift in generating vulnerability-free code over Gemini 4 Argon, and the Chrome security team reported 10x higher patch quality compared to competing frontier models. Beyond security, Carbon jumps +89 Elo to #4 (1768) on WebDev Arena, reaches 96.2% F1 on GraphWalks (256K–1M long-context reasoning), scores 80.0% on RiemannBench, and ranks #1 of 93 models on Vals AI's Legal Research Bench.

Google is rolling out Gemini 4 Carbon through a deliberate staged release: initial access began via Google's Fairwind Program for vetted cybersecurity defenders and U.S. government pre-release testing partners, followed by broader availability across Google AI Ultra and Pro subscriptions, Google AI Studio, Vertex AI, and the agent-first Google Antigravity IDE. API pricing launches with a 50% introductory discount at $2.00 per 1M input tokens and $10.00 per 1M output tokens ($4.00 / $20.00 standard pricing after the promotional period), with cached input tokens discounted by 95% ($0.10–$0.20 per 1M).

**Pricing & Plans:** API introductory launch pricing (50% off): $2.00 / 1M input tokens and $10.00 / 1M output tokens ($4.00 / $20.00 standard rate), plus 95% off cached input tokens ($0.10–$0.20 / 1M). Staged access via Google's Fairwind Program, Google AI Studio, Vertex AI, and Google AI Ultra ($99.99–$199.99/mo) & Pro ($19.99/mo).

- **#1 on Artificial Analysis Intelligence Index v5.0 (Score 59):** Leads overall frontier intelligence across HLE-Diamond, Terminal-Bench-Science (64.8%), AA-Omniscience, and AutomationBench-AA, scoring 9 points higher than Gemini 4 Argon at comparable per-task cost ($7.76).
- **Full-Lifecycle Cybersecurity Defense (SEC-bench Pro 88.2%):** State-of-the-art autonomous vulnerability discovery (88.2% SEC-bench Pro), exploit validation (54.8% ExploitGym), and root-cause patching (71.0% CWE-bench), generating 2x more secure code than Argon.
- **Multi-Day Long-Horizon Agentic Engineering:** Persists for days through dead ends and complex tool-call loops across large codebases and low-level systems, scoring 65.2% on Terminal-Bench 4.0 and 1768 Elo (#4, +89 Elo) on WebDev Arena.
- **1M-Token Native Multimodal Context (96.2% GraphWalks F1):** Processes text, code, images, audio, speech, and video within a 1-million-token context window, achieving 96.2% F1 on 256K–1M GraphWalks and 72.5% on Chartography.
- **Hardened Alignment & Prompt-Injection Resilience:** Combines chain-of-thought monitoring, real-time misuse defenses, and high-pressure alignment training that cuts unsupported claims and fabrications by 2x compared to Gemini 4 Argon.

**Strengths:** Ranked #1 overall on Artificial Analysis Intelligence Index v5.0 (59), ahead of Claude Opus 5.5 (58) and GPT-6 Astra (57); Unmatched cybersecurity benchmark leadership on SEC-bench Pro (88.2%), ExploitGym (54.8%), and CWE-bench (71.0%); Aggressive 50% launch API discount ($2 / $10 per 1M tokens) with 95% off context caching and 29% lower output token consumption than Claude Opus 5.5; True 1M-token multimodal context window with a 12-point jump on 256K–1M GraphWalks (96.2% F1 vs Argon's 84.2%)

**Limitations:** Staged rollout starts with Google's Fairwind Program for cyber defenders before general availability in Google AI Studio and Gemini apps; Standard API pricing doubles to $4.00 / $20.00 per 1M tokens once the introductory 50% launch discount period ends; High reasoning effort modes exhibit longer time-to-first-answer-token latency (9.75s–23.45s) on deep multi-step tasks

[Profile (Markdown)](https://openaitools.app/tool/gemini-4-carbon.md)

## Polygres

Polygres is a retrieval platform that treats PostgreSQL as the backbone for AI-native applications. Rather than forcing teams to migrate to a new vector-only database, it virtualises a GraphRAG-style retrieval layer on top of Postgres, so existing relational records, embeddings and text indexes stay where they are while agents gain the ability to search by meaning, follow relationships and match exact wording in one request.

The platform bundles four complementary retrieval modes: semantic vector search for meaning, graph traversal for multi-hop relationships and shortest-path style queries, full-text search for exact terms, and hybrid search that blends them. Developers reach these capabilities through a hosted playground, a CLI for managing projects and imports, and a typed Python SDK for production applications.

Behind the managed platform are two open-source engines, pgGraph for graph traversal inside Postgres and pgContext for AI search, which teams can inspect, benchmark and self-host. Polygres supports both a managed database hosted by the company and connecting an existing Postgres instance, and it markets itself around agent memory, grounded answers with sources, product semantic search and recommendation systems.

**Pricing & Plans:** Polygres follows a freemium model with a free tier for getting started and paid managed plans for production workloads. Exact tiers are published on the company's pricing page, so check there for current numbers before budgeting.

- **Unified retrieval API:** Query graph, vector, full-text and hybrid retrieval through one consistent interface instead of stitching multiple systems together.
- **Graph traversal on Postgres:** The open-source pgGraph engine follows relationships across your records, enabling multi-hop and shortest-path retrieval without moving data.
- **Vector and hybrid search:** Semantic search over embeddings combines with sparse and full-text matching so queries that mix meaning and exact terms still work.
- **Developer toolkit:** A dashboard playground, CLI for imports and configuration, and a typed Python SDK make it straightforward to prototype and then ship.
- **Managed or bring-your-own Postgres:** Start on a Polygres-hosted database or connect an existing Postgres instance and extend what you already run.

**Strengths:** Keeps your existing PostgreSQL instead of forcing a migration to a specialised vector store; Combines four retrieval modes in one platform, which reduces the number of moving parts in a RAG stack; Core engines pgGraph and pgContext are open source, so the internals are inspectable and self-hostable; Familiar Postgres tooling, SQL knowledge and connection patterns carry over for most teams

**Limitations:** Team and community are relatively early-stage, so long-term support and ecosystem maturity are still developing; Being a newer product, third-party tutorials and integrations are thinner than for established vector databases; Fully managed convenience depends on using the hosted offering rather than the open-source engines alone

[Profile (Markdown)](https://openaitools.app/tool/polygres.md)

## Frequently Asked Questions

### How do Gemini 4 Carbon (Gemini Carbon 4) and Polygres compare in monthly traffic?

Gemini 4 Carbon (Gemini Carbon 4) has 4,339,429 monthly visits, while Polygres has 4,018. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Gemini 4 Carbon (Gemini Carbon 4) has a month-over-month growth rate of +12.7%; Polygres has -52.3%.

### How does pricing compare?

Gemini 4 Carbon (Gemini Carbon 4): Freemium. API introductory launch pricing (50% off): $2.00 / 1M input tokens and $10.00 / 1M output tokens ($4.00 / $20.00 standard rate), plus 95% off cached input tokens ($0.10–$0.20 / 1M). Staged access via Google's Fairwind Program, Google AI Studio, Vertex AI, and Google AI Ultra ($99.99–$199.99/mo) & Pro ($19.99/mo). Polygres: Freemium. Polygres follows a freemium model with a free tier for getting started and paid managed plans for production workloads. Exact tiers are published on the company's pricing page, so check there for current numbers before budgeting.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | Highly efficient reasoning models with exceptionally low API pricing, but offers less specialized multimodal agentic tooling. | [.md](https://openaitools.app/tool/deepseek.md) |
| 2 | [OpenAI Platform](https://openaitools.app/tool/openai) | 234,678,639 | -9.6% | Paid | Industry-standard proprietary frontier models with powerful developer APIs, but lacks Google's native massive context window. | [.md](https://openaitools.app/tool/openai.md) |
| 3 | [Qwen 4 (Alibaba Qwen4)](https://openaitools.app/tool/qwen-4) | 35,101,353 | -6.2% | Freemium | Powerful open-weight multimodal models with strong multilingual support, but requires self-hosting or third-party inference. | [.md](https://openaitools.app/tool/qwen-4.md) |
| 4 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Unified API gateway offering access to hundreds of models, but lacks a single native frontier model. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 5 | [BigModel](https://openaitools.app/tool/bigmodel) | 5,700,686 | -17% | Freemium | Zhipu AI's robust GLM language and multimodal APIs, but with less global developer ecosystem support. | [.md](https://openaitools.app/tool/bigmodel.md) |
| 6 | [Gemma Open Models by Google](https://openaitools.app/tool/gemma-open-models-by-google) | 4,900,914 | -13.5% | Freemium, Paid | Lightweight open-weights models from Google for local deployment, but less capable than frontier closed models. | [.md](https://openaitools.app/tool/gemma-open-models-by-google.md) |
| 7 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Serverless inference for open-source models with high-performance GPU clusters, but requires managing your own model weights. | [.md](https://openaitools.app/tool/together-ai.md) |
| 8 | [Tencent Hunyuan](https://openaitools.app/tool/tencent-hunyuan-large-model) | 1,485,463 | -1.2% | Contact for Pricing | Comprehensive multimodal foundation model family from Tencent, but primarily targeted at Asian enterprise markets. | [.md](https://openaitools.app/tool/tencent-hunyuan-large-model.md) |
| 9 | [Exa](https://openaitools.app/tool/exa-ai) | 1,062,574 | +16.8% | Freemium, Contact for Pricing | Web search API and crawler built for AI agents | [.md](https://openaitools.app/tool/exa-ai.md) |
| 10 | [You.com](https://openaitools.app/tool/you) | 948,349 | +7% | Freemium | Search and inference APIs that give AI models real-time web data | [.md](https://openaitools.app/tool/you.md) |

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Source: https://openaitools.app/compare/gemini-4-carbon-vs-polygres

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