New AI Models (November 2026): GPT-6.1 Terra, Gemini 4 Carbon, Kimi K3.1 Pro and the Security Risks of Each

New AI Models (November 2026): GPT-6.1 Terra, Gemini 4 Carbon, Kimi K3.1 Pro and the Security Risks of Each

The major AI models released or announced in late October and November 2026 are GPT-6.1 Terra from OpenAI (released October 26), Gemini 4 Carbon from Google (announced October 28), and Kimi K3.1 Pro from Moonshot (with full open weights promised by November 4), alongside DeepSeek V4.1 Flash 1017 (October 17) and Claude Haiku 5.5 from Anthropic (October 7, just before the window). Every model in this guide was confirmed on the lab's own announcement page, and independent scores come from the Artificial Analysis Intelligence Index, version 5.0. The security stakes are concrete: Carbon leads the index at 59 with a 0.0% attack success rate on Gray Swan's indirect prompt injection benchmark, while Moonshot says Kimi K3.1 Pro's open weights solve 81.0% of expert vulnerability discovery and exploitation tasks before its cyber-data controls.

This is the November 2026 edition of our monthly new AI models guide. We covered the previous batch, including GPT-6, Claude Opus 5.5 and Gemini 4 Argon, in New AI Models (October 2026): GPT-6, Claude Opus 5.5, Gemini 4 Argon, Muse Wonder and the Security Risks of Each. Every model fact below is taken from the lab's own page, and numbers the lab reports for itself are labeled vendor-reported. Independent scores are from Artificial Analysis. Where a figure could not be verified, it is marked as unverified rather than guessed.

The Short Answer: Which New AI Model Is Best?

• Highest independent score: Gemini 4 Carbon, 59 on the Artificial Analysis Intelligence Index (v5.0), ahead of Claude Fable 5 (57) and GPT-6.1 Terra (56).
• Best value near the top: GPT-6.1 Terra at $6 per 1M input tokens and $18 per 1M output tokens, with a 25% off introductory period.
• Cheapest useful models: Gemini 3.6 Flash Lite at $0.50 in and $2.50 out, and Claude Haiku 5.5 at $1 in and $5 out (vendor-reported).
• Best open-weight model: Kimi K3.1 Pro, a 3T-class model with a 1M-token context window; full weights release by November 4, 2026 (Moonshot).
• Most capable on cybersecurity, by the labs' own numbers: GPT-6.1 Terra (58.5% on the HackerGPT-2 cyber benchmark, vendor-reported) and Gemini 4 Carbon (88.2% on SEC-bench Pro for vulnerability finding, vendor-reported).

New AI Models at a Glance

Scores are the Artificial Analysis Intelligence Index, version 5.0, as displayed on the Artificial Analysis model leaderboard on October 30, 2026. The index is a weighted, cost-normalized aggregate of independently run benchmarks. Where this table shows a price that could not be confirmed on a lab page during this run, it says "See vendor page" instead of guessing.

OpenAI: GPT-6.1 Terra and GPT-6 Rosalind

GPT-6.1 Terra

What it is. GPT-6.1 Terra is OpenAI's new flagship in the GPT-6 family, released on October 26, 2026, replacing GPT-6 Sol (June 26) and GPT-6.1 Sol (August 28) as the model behind ChatGPT. OpenAI's changelog describes it as "built for long-running, multi-step work," with improved reliability, lower cost, and better performance on science and agentic tasks.

What is new. A larger context window (1.15M tokens, up from 500K), a 512K max output, and a new Ultra effort level, which OpenAI describes as a higher reasoning effort above Max that runs a multi-agent workflow to deliver the strongest results available on the hardest problems. It supports native image and web search tool calling.

Strengths (vendor-reported). 58.5% on the HackerGPT-2 cyber benchmark, +9 points over GPT-6 Sol. OpenAI reports 1.4x better accuracy and 6.8x lower cost per task on CyberGym (2.0). It is also faster, at roughly 3x the rate of GPT-6 Sol, and 15% cheaper than GPT-6.1 Sol at the same task outcomes on OpenAI's internal evaluations.

Price and availability. $6 per 1M input tokens and $18 per 1M output tokens, with a 25% introductory discount until November 23, 2026. Live in ChatGPT, the Responses API, the Agents SDK, and Codex on October 26.

Independent ranking. 56 on the Artificial Analysis Intelligence Index (v5.0), third overall, ahead of GPT-6.1 Astra and Claude Opus 5.5 (both at 54).

Why it matters for security. The 9-point jump on a cyber benchmark and the 6.8x cost reduction on CyberGym mean the capability to find and exploit vulnerabilities is getting cheaper per task, not just more accurate. Terra is also the first GPT-6 generation where OpenAI explicitly positions agentic, long-running work as the headline use, which is exactly where AI security tooling and AI attack automation meet.

GPT-6 Rosalind (research preview)

What it is. Announced in the changelog on October 30, 2026: "GPT-6 Rosalind is a research preview model family for life sciences reasoning, built on GPT-6 architecture." It is not a general-purpose model replacement.

What is new. It is the first OpenAI release aimed squarely at scientific reasoning, with a new rosalind endpoint alongside the standard gpt-6.1 endpoint.

Price and availability. API access as a research preview, pricing listed as see vendor page during this run.

Why it matters for security. A dedicated scientific reasoning endpoint signals that labs are segmenting their model lines by domain. Security teams should note that "GPT-6" in a procurement document is no longer one model; Terra, Astra, Luna, Terra Mini and Rosalind all ship under the same family name with different behavior profiles.

Google: Gemini 4 Carbon

What it is. Gemini 4 Carbon is Google's next frontier model, announced on October 28, 2026, two weeks after Gemini 4 Argon. Google calls it "our strongest model with state-of-the-art cybersecurity and long horizon capabilities."

What is new. Carbon is built for sustained agentic workflows, with Google's teams reporting it running "for days to persist through dead ends" on multi-step engineering tasks. It builds on Argon's multi-layered safety stack and improves on alignment, honesty and prompt injection resistance.

Strengths (vendor-reported). Carbon ranks #1 on the recently updated v5.0 of the Artificial Analysis Intelligence Index at 59. On cybersecurity benchmarks, evaluated without guardrails, it reports 88.2% on SEC-bench Pro for finding vulnerabilities, 54.8% on ExploitGym for exploitation, and 71.0% on Collinear's CWE-bench for patching. The Chrome team reportedly saw Carbon perform more than 10x better than another lab's large frontier model when generating high quality patches, and Google reports a more than 2x uplift over Argon on internal secure-code generation. On prompt injection, where Argon recorded a 0.7% attack success rate on Gray Swan's Indirect Prompt Injection Benchmark, Carbon achieves 0.0%.

Price and availability. Carbon launches at Argon's introductory price: $2 per 1M input tokens and $10 per 1M output tokens, with cached input priced 95% off the input price. After the introductory period, $4 in and $20 out. Access starts with a subset of cyber defenders through the Fairwind Program, with broader availability to developers, businesses and consumers to follow.

Independent ranking. 59 on the Artificial Analysis Intelligence Index (v5.0), the highest score on the leaderboard.

Why it matters for security. Carbon is the first flagship to be gated behind a cyber-defender rollout rather than a general public launch. Google is also the first lab to tie a specific model release to the U.S. government's voluntary pre-release model testing process. For defenders, the combination of the index lead and the 0.0% IPI result makes Carbon the reference point for prompt injection resilience. For attackers, the ExploitGym and CWE-bench numbers show a single model that can find, validate and patch, which compresses the vulnerability lifecycle.

Moonshot: Kimi K3.1 Pro

What it is. Kimi K3.1 Pro is Moonshot's first model in the K3.1 series and what Moonshot calls "our most capable 3T-class open-weight model to date," with a 1-million-token context window. A companion model, Kimi K3.1 Pro Highspeed, delivers 2 to 3x the speed of K3.1 Pro.

What is new. K3.1 Pro is a post-trained version of Kimi K3 with substantially larger post-training compute, optimized for large-scale joint reinforcement learning across hundreds of downstream domains. It targets coding, 3D creation, financial research and agentic work.

Strengths (vendor-reported). On Terminal-Bench 4.0, K3.1 Pro scores 65.7%, ahead of GPT-6 Astra (58.2%) and narrowly behind Claude Opus 5.5 (66.4%). On Moonshot's internal Kimi Code Bench 3.0 Long-horizon, it scores 61.2% versus 55.95% for K3. On BenchCAD, it scores 92.7% with tools, close to Claude Opus 5.5 (96.2%) and GPT-6 Astra (95.9%). On OfficeQA Pro v2, it reaches state-of-the-art at 64.4. Moonshot also reports that its claim-level hallucination rate on an internal stress-test benchmark fell from 23.4% for K3 to 8.3%.

The cyber capability disclosure. Moonshot is unusually explicit about the model's offensive capability. On xdev-bench-hard, an in-house benchmark of expert-curated vulnerability discovery and exploitation tasks, the unchecked checkpoint solves 90.5% of problems at pass@3, while the final K3.1 Pro, after cyber-related data removal at both mid-training and the RL stage, reaches 81.0%. Moonshot says K3.1 Pro's cyber capability "remains behind that of frontier proprietary models that have been publicly available for at least three months."

Price and availability. Available now on Kimi.ai, Kimi Work, Kimi Code and the Kimi API (kimi-k3.1-pro and kimi-k3.1-pro-highspeed), with Zero Data Retention offered for API customers. Full model weights release by November 4, 2026.

Independent ranking. Not yet ranked on the Artificial Analysis Intelligence Index as of the October 30 leaderboard pull; the open-weight release may precede its first independent score.

Why it matters for security. A 3T-class open-weight model that solves 81% of expert vulnerability tasks, even after deliberate capability reduction, changes the threat math for self-hosted and air-gapped environments. The disclosure is a template for what open-weight release notes should include: what the model could do before controls, what it can do after, and where it still trails proprietary frontier models.

DeepSeek: V4.1 Flash 1017

What it is. An update to the smallest model in DeepSeek's V4 architecture family, released on October 17, 2026. It keeps the deepseek-flash API name and pricing structure.

What is new. A performance-cost Pareto improvement over V4.1 Flash: higher benchmark scores with reduced task runtime and output length. Reported figures (vendor-reported) include HLE (w/o tools) 42.6, Terminal-Bench 4.0 at 42.3, DeepSWE v1.1 at 77.0, and Chartography (w/tools) at 83.0.

Price and availability. API price unchanged from V4.1 Flash; DeepSeek notes API prices were reduced with the original V4.1 release, so consult the vendor pricing page for current per-token figures.

Independent ranking. 50 on the Artificial Analysis Intelligence Index (v5.0), tied with Claude Haiku 5.5.

Why it matters for security. DeepSeek remains the value benchmark for high-volume agentic work. When the cheapest useful model keeps improving while holding price, the barrier to running AI agents at scale drops further, which is the condition under which shadow AI deployments tend to grow.

Anthropic: Claude Haiku 5.5

What it is. Anthropic's fastest, most capable small model, announced on October 7, 2026. It sits just before the October 15 window this guide covers, but it is the current small-model reference point for every other vendor's lightweight tier.

What is new. Built for high-volume work: summarization, subagents and browser use.

Price and availability. $1 per 1M input tokens and $5 per 1M output tokens (vendor-reported), available in the Claude apps and API.

Independent ranking. 45 on the Artificial Analysis Intelligence Index (v5.0).

Why it matters for security. Small fast models are where subagent fleets run. If a large model plans the attack, a model like Haiku 5.5 is often the workhorse that executes the tedious steps. Security programs that only evaluate their flagship model miss the tier that actually touches the system.

Open-Weight Models

The open-weight column keeps mattering for two reasons. First, a 3T-class model with 81% vulnerability-task performance (Moonshot's own number) is now reachable by any team that can host it, which shifts vulnerability discovery away from a handful of frontier labs. Second, open weights let defenders run evaluation and red-teaming on infrastructure they control, which is the only way to verify what a model will actually do inside your environment. Both effects push the same direction: the capability gap between what a lab can build and what an organization can run is narrowing.

What Is Coming Next

• Gemini 4 Carbon general availability. Google is phasing Carbon to more trusted partners through the Fairwind Program before making it available to developers, businesses and consumers.
• GPT-6 Rosalind expansion. The research preview has a rosalind endpoint today; OpenAI has not stated a GA date or a full product surface.
• Kimi K3.1 Pro weights. Full model weights release by November 4, 2026, which is the date to watch for independent benchmarking of the open-weight release.

No other major release was announced on the lab pages checked for this guide during the October 15 to November 2 window.

How to Choose a Model

What These Models Change for Security

1. Cyber capability is now a stated spec. OpenAI and Google both publish cyber benchmark numbers for their flagships, and Moonshot quantifies the offensive capability it chose to remove. The question for a security team is no longer "can this model do this?" but "which lab published the number, and how would we reproduce it?"
2. Gated rollout is the new frontier pattern. Carbon reaches cyber defenders before the general public, through a named program and the U.S. government's voluntary pre-release testing process. Expect this to become the norm, and expect access to be a negotiating item.
3. Prompt injection claims need the benchmark name. "0.0% attack success rate" is only meaningful next to the benchmark: Gray Swan's Indirect Prompt Injection Benchmark, as Argon achieved 0.7% on the same test. Treat unlabeled injection-resistance claims as unverified.
4. Cheap, capable small models feed shadow AI. When the useful tier is $1 in / $5 out, running unapproved agents becomes a line item, not a project. Inventory what runs on Haiku-class models the same way you inventory what runs on the flagship.
5. Data retention is a vendor-specific control. Moonshot offers Zero Data Retention on the Kimi API. Confirm retention terms per model and per endpoint, because a single platform can host models with different handling.
6. Self-hosting changes the duty profile. Running an open-weight model that can find and exploit vulnerabilities puts you in charge of the sandbox, the prompt boundaries and the exploit output. The hosting decision is a security decision, not an IT one.

A 30-Day Checklist for Any New Model

1. Confirm the release on the lab's own page, not a tracker or news summary.
2. Record the exact model ID, version date and API endpoint name.
3. Pull the lab's own benchmark numbers and label them vendor-reported.
4. Pull the Artificial Analysis Intelligence Index score and version (v5.0 as of this writing).
5. Check the model's stated context window, max output and pricing, including any introductory period.
6. Check data retention and Zero Data Retention availability for that specific endpoint.
7. Run a prompt injection test using a named public benchmark, not an internal ad-hoc set.
8. Run a small vulnerability discovery task in a sandboxed environment and review the output.
9. Document the model's agentic permissions: which tools, which credentials, which egress.
10. Set a re-evaluation date for the day the introductory pricing ends or the model is superseded.

How Clone Systems Can Help

• AI-enabled application testing: Managed penetration testing services extend to applications that call these models, testing the integration layer where prompts, tools and credentials meet.
• Detection and response: Managed SOC monitors the traffic an agentic model generates, which looks different from human-initiated sessions and from service-to-service calls.
• Exposure on the perimeter: PCI ASV scanning keeps the external surface measured on a schedule while model access changes behind it.
• Internal exposure: Network vulnerability assessment services cover the internal hosts that agent fleets reach after authentication.
• AI assistant security: CloneGuard AI is our own AI assistant for security operations, and we evaluate new models against the same threat set we test clients with.
• Talk to us: Contact Clone Systems to scope an assessment around a specific model release.

Frequently Asked Questions

What is GPT-6.1 Terra? GPT-6.1 Terra is OpenAI's flagship model, released October 26, 2026, built for long-running, multi-step and agentic work. It scores 58.5% on the HackerGPT-2 cyber benchmark (vendor-reported) and 56 on the Artificial Analysis Intelligence Index.

What is Gemini 4 Carbon? Gemini 4 Carbon is Google's frontier model announced October 28, 2026, with state-of-the-art cybersecurity and long-horizon capabilities. It leads the Artificial Analysis Intelligence Index at 59 and is rolling out first to cyber defenders through the Fairwind Program.

What is Kimi K3.1 Pro? Kimi K3.1 Pro is Moonshot's 3T-class open-weight model with a 1M-token context window. It is available on the Kimi API now, with full model weights releasing by November 4, 2026.

Is Kimi K3.1 Pro open source? Moonshot has committed to releasing the full model weights by November 4, 2026, and the API is available today. Confirm the license terms at the weight release date.

What is DeepSeek V4.1 Flash 1017? It is an October 17, 2026 update to DeepSeek's smallest V4 model, improving benchmarks while reducing runtime and output length. It is accessed through the deepseek-flash API name and scores 50 on the Artificial Analysis Intelligence Index.

Is Claude Haiku 5.5 out? Yes, announced October 7, 2026, just before this guide's window. It is Anthropic's fastest small model, priced at $1 per 1M input tokens and $5 per 1M output tokens (vendor-reported).

Which new AI model is the best in November 2026? By the Artificial Analysis Intelligence Index (v5.0), Gemini 4 Carbon leads at 59, with GPT-6.1 Terra at 56 and Claude Fable 5 at 57 as the nearest competitors.

Which new AI model is the cheapest? Gemini 3.6 Flash Lite is the cheapest useful model at $0.50 per 1M input tokens and $2.50 per 1M output tokens (vendor-reported). Among this month's new releases, GPT-6.1 Terra's introductory $6 / $18 is the most cost-efficient frontier tier.

Which open-weight AI model is the best? Kimi K3.1 Pro is the strongest open-weight release of this cycle, with a 1M-token context and documented cyber-data controls, ahead of its independent benchmarking.

Is it safe to use a new frontier model for business data? Safety depends on the endpoint's data retention terms and the model's agentic permissions, which vary by model and by lab. Confirm retention, tool access and egress before connecting production data.

Do these models improve prompt injection resistance? Google reports Gemini 4 Carbon at 0.0% attack success on Gray Swan's Indirect Prompt Injection Benchmark, down from Argon's 0.7% on the same test (vendor-reported). Treat unlabeled claims as unverified.

Which new AI model is best for cybersecurity work? The labs' own numbers point to GPT-6.1 Terra (58.5% HackerGPT-2) and Gemini 4 Carbon (88.2% SEC-bench Pro), both vendor-reported. Independent, unguarded evaluation on your own targets is the only way to confirm the fit.

Conclusion

This month's release cycle is defined less by raw capability and more by how the labs package it: a cyber-defender-only rollout for the top model, a quantified and disclosed offensive capability in the open-weight leader, and a research preview that splits a flagship family by domain. For security teams, the practical takeaway is to treat each model release as a change event, not a news item. Confirm the facts on the lab's page, run the model through your own sandbox, and keep the independent scores under version control. Clone Systems does this monthly, and we test the integration layer where these models meet your applications. See how we approach AI-enabled security testing or contact us to run an assessment against this month's models.

References

[1] OpenAI API Changelog: "GPT-6.1 Terra: Our new flagship model" (October 26, 2026) and "GPT-6 Rosalind: Research preview model family for life sciences reasoning" (October 30, 2026). https://developers.openai.com/api/docs/changelog [2] Google (Koray Kavukcuoglu): "Gemini 4 Carbon: our next frontier model," October 28, 2026. https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-carbon/ [3] Moonshot AI: "Introducing Kimi K3.1 Pro," late October 2026. https://www.kimi.ai/blog/kimi-k3-1-pro [4] DeepSeek API Documentation: Change Log, "DeepSeek-V4.1-Flash-1017 Update," October 17, 2026. https://api-docs.deepseek.com/updates [5] Anthropic Newsroom: "Introducing Claude Haiku 5.5," October 7, 2026. https://www.anthropic.com/news [6] Artificial Analysis: Model Leaderboard, Intelligence Index version 5.0 (retrieved October 30, 2026). https://artificialanalysis.ai/leaderboards/models [7] National Institute of Standards and Technology (NIST): Generative AI risk guidance and the AI Risk Management Framework. https://www.nist.gov/artificial-intelligence [8] xAI (SpaceXAI): API Release Notes (checked for the window; no new model release found between October 15 and November 2, 2026). https://docs.x.ai/docs/release-notes [9] Clone Systems: New AI Models (October 2026): GPT-6, Claude Opus 5.5, Gemini 4 Argon, Muse Wonder and the Security Risks of Each. https://www.clone-systems.com/blog/new-ai-models-october-2026-release-guide-and-security [10] Clone Systems: Managed penetration testing services. https://www.clone-systems.com/managed-penetration-testing-services/ [11] Clone Systems: Managed SOC service. https://www.clone-systems.com/managed-soc-service/ [12] Clone Systems: PCI ASV scanning and external vulnerability scanning. https://www.clone-systems.com/pci-asv-scan-external-vulnerability-scanning/ [13] Clone Systems: Network vulnerability assessment services. https://www.clone-systems.com/network-vulnerability-assessment-services/ [14] Clone Systems: CloneGuard AI assistant. https://www.clone-systems.com/cloneguard-ai-assistant/ [15] Clone Systems: Contact. https://www.clone-systems.com/contact