New AI Models (October 2026): GPT-6, Claude Opus 5.5, Gemini 4 Argon, Muse Wonder and the Security Risks of Each

New AI Models (October 2026): GPT-6, Claude Opus 5.5, Gemini 4 Argon, Muse Wonder and the Security Risks of Each

A major AI model now arrives every few days. In the six weeks since September 1, OpenAI shipped the GPT-6 family, Anthropic shipped three Claude 5.5 models, Google made Gemini 4 Argon available to everyone, Meta released Muse Wonder and then, today, the weights of Muse Spark Open, and NVIDIA, Mistral, xAI and DeepSeek each added their own. This guide covers every significant release in one place: what each model is, what it is good at, what it costs, how it ranks on independent testing, and what it changes for your security.

Every fact below is taken from the lab's own announcement page where one was available, and from Artificial Analysis for independent rankings. Vendor-reported numbers are labeled as such. This is the October 2026 edition of our monthly guide to new AI models, last updated October 15, 2026, and it is revised as new models ship.

The Short Answer: Which New AI Model Is Best?

There is no single best model, because the field has split along price, speed and how much you trust it with your data. Here is how the picks fall out of the numbers:

• Highest independent score: Claude Opus 5.5 at 58 on the Artificial Analysis Intelligence Index [18].
• Best intelligence for the money among the leaders: GPT-6.1 Sol scores 52 at about $0.72 per task, against $5.98 for Opus 5.5 at 58 [18]. Gemini 4 Argon scores 53 at about $1.99 per task [18].
• Cheapest models that are still useful: GPT-6 Luna, Claude Haiku 5.5, DeepSeek V4.1 Flash and Xiaomi's MiMo-V2.6-Flash all cost well under a dollar per task [18].
• Best open-weight models: MiMo-V2.6-Pro (46), GLM-5.3 (45) and Kimi K3 (44) lead the board. Among US releases, Meta's Muse Spark Open scores 39 [18].
• Most capable at cybersecurity: OpenAI says GPT-6 Astra reaches the "Critical" cyber threshold under its own framework [1]. Google says Gemini 4 Argon can find, validate and patch vulnerabilities on its own [10].

New AI Models at a Glance

*Artificial Analysis Intelligence Index v4.3 at each model's highest listed reasoning setting (xhigh or max for most). Higher is better. Scores shift as models are re-tested [18].

OpenAI: The GPT-6 Family

OpenAI's GPT-6 arrived in three steps in five weeks, and the family now has four models you can choose between [1][2][4].

GPT-6 Astra (September 3)

OpenAI calls Astra the most intelligent and aligned model it has built, and says it is state of the art on computer use, browsing, software engineering, cybersecurity, science and professional work [1]. Highlights from OpenAI's own page:

• Computer use. On Agents' Last Exam, a test of complex professional tasks in real software, OpenAI reports Astra at 59.3 percent, against 55.5 percent for Claude Opus 5. On OSWorld 2.0 it reports 72.6 percent in about 40 minutes per task, against 65.7 percent in about 75 minutes for GPT-5.6 Sol [1].
• Coding. 57.9 percent on Terminal-Bench 4.0, which OpenAI compares with 55.8 percent for Claude Fable 5.1 [1].
• Science. 64.6 percent on Terminal-Bench Science 0.1, against 52.6 percent for Fable 5.1, and 96.0 percent on GPQA Diamond [1].
• Price. $10 per million input tokens and $50 per million output tokens, with a fast mode at twice the speed and twice the price [1].
• Availability. ChatGPT Plus, Pro, Business and Enterprise, the OpenAI API, Microsoft Azure and AWS Bedrock. Enterprise administrators must switch it on, because access is off by default at launch [1].

Artificial Analysis found Astra ties Claude Fable 5.1 on its Intelligence Index at roughly 40 percent of the cost [19].

GPT-6 Sol and GPT-6 Luna (September 22)

OpenAI split the family into a full-size model and an efficient one [4]:

• GPT-6 Sol is the everyday reasoning model, at $2 per million input tokens and $10 per million output.
• GPT-6 Luna is built for speed and volume, at $0.10 per million input tokens and $0.50 per million output tokens. Luna also powers a Decisions API beta, which the changelog says is 10 times faster than the Responses API [4].

GPT-6.1 Sol (September 29)

Only a week later, GPT-6.1 Sol replaced GPT-6 Sol. OpenAI says it nearly matches Astra on agentic coding, computer use and professional work at one-fifth of Astra's token price: $2 input, $10 output, and $0.10 for cached input [2]. OpenAI reports it matches Astra on DeepSWE v1.1 at roughly one-fifth the cost, and scores 2.2 percentage points above Claude Opus 5.5 on AutomationBench at medium effort for about a third of the cost [2]. Artificial Analysis scores it one point below Astra at under a quarter of the cost per task [19]. An Ultrafast mode was added October 8 [4].

GPT-6 and Intelligent UI in ChatGPT (October 7)

For the more than 1.2 billion people who use ChatGPT weekly, OpenAI shipped GPT-6 with a feature called Intelligent UI: instead of plain text, ChatGPT can answer with interactive diagrams, forms, charts, small tools and mini-apps built on the spot [3]. OpenAI also says GPT-6 can begin answering while it is still thinking, and that web-search answers start 44 percent sooner than on GPT-5.6 Instant [3]. GPT-6 Sol powers it for Plus, Pro, Business and Enterprise, and GPT-6 Luna powers it for Free and Go users [3].

Why it matters for security: OpenAI says Astra meets the "Critical" threshold for cybersecurity under its Preparedness Framework. OpenAI reports a perfect 100 percent on ExploitBench, says Astra found two previously unknown zero-day vulnerabilities during testing, and says that, run without safeguards, it could achieve code execution in hardened browsers [1]. The version that shipped refuses more advanced tasks such as writing proof-of-concept exploits, with a program called Daybreak to open that up to vetted defenders [1].

Anthropic: Claude Fable 5.1 and the Claude 5.5 Family

Anthropic released four models in about six weeks: Fable 5.1 on September 1, then Opus 5.5, Sonnet 5.5 and Haiku 5.5 [5][6][8].

Claude Opus 5.5 (September 22)

The first model in the 5.5 family. Anthropic says it performs at the level of Claude Fable 5.1 on most work at 40 percent lower run cost than Opus 5 [6]. The price is $4 per million input tokens and $20 per million output tokens. Anthropic-reported numbers include 66.4 percent on Terminal-Bench 4.0 and an 81.8 percent partial score on OSWorld 2.1, and testers describe finishing a 680,000-line code migration in under a day [6]. Anthropic says it is more resistant to prompt injection than Opus 5 and much less likely to take hard-to-reverse actions or act outside its boundaries [6]. Artificial Analysis currently ranks it first, at 58 [18].

Claude Sonnet 5.5 (September 28)

About 30 percent faster than Sonnet 5 and up to 30 percent cheaper for most work, at $2 input and $10 output per million tokens [7][20]. Artificial Analysis scores it 56, only two points behind Opus 5.5, but it used the most output tokens per task of any model it has measured [20].

Claude Haiku 5.5 (October 7)

Anthropic calls it its cheapest, fastest and most capable small model. It is priced at $0.10 per million input tokens and $0.50 per million output tokens for prompts up to 100,000 tokens, costs about 75 percent less than Haiku 4.5 on average, and can serve as a subagent alongside Opus 5.5 and Sonnet 5.5 [8]. It scores 43 at maximum effort on the Intelligence Index, up 26 points from the last Haiku a year ago [19].

Claude Fable 5.1 (September 1)

Anthropic's most capable general-availability model for long-running work, built to run for hours across many applications. Priced at $10 per million input tokens and $50 per million output tokens, with cache reads at $0.25 per million, 75 percent less than Fable 5 [5]. Two details matter to enterprise buyers: Anthropic requires 30-day data retention for safety monitoring by default, with zero data retention only for eligible enterprise customers, and queries that trip its cyber or biology safeguards are routed to a less capable model [5].

What About Claude Fable 5.5?

As of October 15, Anthropic has not announced a Fable 5.5. Its own newsroom lists Opus 5.5, Sonnet 5.5 and Haiku 5.5 as the Claude 5.5 releases and Fable 5.1 as the newest Fable [5]. Reports circulating October 13 and 14 say a Fable 5.5 may be coming, citing a model catalog entry and social media posts, but there is no model card, price or date from Anthropic. We will add it to this guide if and when Anthropic publishes it.

Why it matters for security: Anthropic says Opus 5.5 is comparable to its restricted Mythos 5.1 in biology and cybersecurity, so it ships with similar safeguards, and it is expanding its Cyber Verification Program for security practitioners [6]. On October 8 it also launched the Anthropic Cyber Mission, with a program for critical-infrastructure defenders and free scans of open-source projects, and said that finding vulnerabilities is easier than ever while verifying, prioritizing and fixing them is still hard [9]. If your security team plans to use a Claude model for defensive work, expect to need verified access.

Google: Gemini 4 Argon

Google announced Gemini 4 Argon on September 30, first to trusted cyber defenders. On October 13 it made the model available to everyone: developers and enterprises through the Gemini API, AI Studio and Android Studio, Google AI Ultra subscribers in Antigravity, and Google AI Ultra and Pro subscribers in the Gemini app and AI Mode in Search [10][11].

What Google reports:

• Coding and professional work. A new state of the art on DeepSWE v1.1 at 77.9 percent, the top spot on the Vals Index across finance, coding, legal and tax work, and 51.3 percent on AutomationBench, which Google says ranks first [10].
• Long outputs. An output limit of 1 million tokens, up from 64,000, so the model can work through a hard problem in one run [10].
• Vision. 91.7 percent on LVBench for long-video understanding [10].
• Price. $2 per million input tokens and $10 per million output tokens at launch, rising to $4 and $20 after the introductory period, with cached input at 95 percent off [10].

Independent testing: Artificial Analysis scores Argon at 53, equal to GPT-6 Astra, at about 60 percent of Astra's cost per task [18][19]. It also reports Argon has the lowest hallucination rate among models scoring 45 or higher on the index, at 15 percent [21].

Why it matters for security: Google says Argon can autonomously find, validate and patch critical vulnerabilities, and that it is releasing it without cyber guardrails to vetted defenders through the Fairwind Program, which is now expanding to hundreds more partners [10][11]. Google reports Argon cut attack success on the Gray Swan indirect prompt injection benchmark to 0.7 percent across 15 adaptive attempts, and says shortcut attempts by the model dropped to 2 percent on coding benchmarks [11]. Both are Google's own figures.

Meta: Muse Wonder and Muse Spark Open

Muse Wonder and Muse Wonder Full (October 12)

Meta's most capable models so far, built on the Muse Spark foundation and aimed at personal and professional agentic tasks across law, finance, health and coding [12]. Two features define them: Active Memory, which carries what matters across conversations, and Active Focus, which keeps working when a task gets hard, tries other approaches, recovers from errors and checks in before anything important [12].

On independent testing, Muse Wonder Full scores 56 on the Intelligence Index and Muse Wonder 52, up from 48 for Muse Spark 1.3 only six weeks earlier [20]. Muse Wonder is available through the Meta Model API and Muse Code at $4 per million input tokens and $20 per million output tokens, while Muse Wonder Full is available to Muse Code subscribers [12][21]. Artificial Analysis reports a hallucination rate of 23 percent for Muse Wonder Full, down from 33 percent [21].

Muse Spark Open (October 15)

Today Meta released the weights of Muse Spark Open, which it calls the most capable US open-weight model to date [13]. It is natively multimodal, with 816 billion total parameters (42 billion active) and a 1 million token context window, aimed at coding and long-horizon agentic work. The weights are on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models, with hosted API access from Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, and it runs locally on two Apple M5 Ultra Mac Studios [13]. It is released under the Muse Model License, which Meta describes as having limited commercial terms [13]; press coverage reports that large companies must agree separate terms with Meta before offering it as a hosted service [26]. Artificial Analysis scores it 39 [18].

Why it matters for security: Meta says it trained the Muse Wonder family to avoid sharing private information without permission and that a full report with additional safety evaluations "will be available soon," a statement it repeats for Muse Spark Open [12][13]. Until those reports exist, the safety claims have not been independently reviewed.

xAI: Grok 4.7

xAI, which now operates as SpaceXAI, released Grok 4.7 on September 21 as its most capable model for coding and knowledge work [14]. It uses a larger base model with a longer reinforcement-learning run and is priced at $2 per million input tokens and $6 per million output tokens, the same as Grok 4.6 [14]. It is available in Cursor and Grok Build, through the xAI API and through third-party coding tools and cloud platforms, with a fast variant at twice the speed and twice the price [14]. xAI reports 46.3 percent on CursorBench 4.0 (51.8 percent for Claude Fable 5.1 in the same table) and calls the model frontier on price-performance [14]. Artificial Analysis scores it 46 [18].

Why it matters for security: xAI says Grok 4.7 let only 3.3 percent of risky dual-use cyber prompts through on its own HackerBench v0.3, and that select cybersecurity partners get invite-only access to its red-team capabilities [14]. Both are vendor figures.

Open-Weight Models: The Fast-Moving Tier

Open-weight models are the ones you can download and run yourself. This wave is notable because the US labs have joined in.

Two things to note. First, the gap between the best closed model (58) and the best open-weight model (46) is about 12 points, about the same as at the start of September [18][23]. Second, the highest-scoring open-weight models come from Chinese labs, which matters for procurement and policy reviews in some organizations, whatever their technical merit.

Mistral says Large 4 was red-teamed with cybersecurity leaders and state authorities before release and reports 93.3 percent on Lakera's B3 benchmark for resistance to indirect prompt injection, a vendor-reported figure [16]. NVIDIA's model card for Nemotron 3.5 Super VL says use-case-specific testing is required before deployment [15].

What Is Coming Next

Only items the labs themselves have said:

• Gemini 4 family: Google says Argon "is just the beginning" and that the rest of the Gemini 4 family is coming [11].
• Mistral Large 4 open weights by the end of October [16].
• Muse Wonder in the Muse app, rolling out first to subscribers [12].
• Meta's full safety evaluation reports for Muse Wonder and Muse Spark Open [12][13].
• Claude Fable 5.5. Rumored, not announced (see above).

How to Choose a Model: A Practical Guide

A caution on benchmarks: each lab publishes numbers on its own settings, and at this level Anthropic itself says benchmark margins have become a less reliable guide to real-world differences [6]. Run your own tasks on your own data before you commit.

What These Models Change for Security

The models above are impressive. They are also the reason security teams are having a harder year. These are the changes that matter.

1. Vulnerability Discovery Is Getting Faster Than Remediation

OpenAI's Astra, Google's Argon and Anthropic's Opus 5.5 are all described by their makers as strong at finding and exploiting software flaws, and all three ship with restrictions or verification programs because of it [1][6][10]. Anthropic's own statement is the one to remember: finding vulnerabilities is easier than ever, but verifying, prioritizing and fixing them is still hard [9]. The bottleneck has moved to triage and patching. A vulnerability program that can scan, rank and drive fixes fast matters more now than it did a year ago; see why your vulnerability management program can't score a prompt injection finding.

2. Autonomous Agents Are Becoming the Default

Fable 5.1 runs unattended for hours [5], Astra operates computers [1], and Muse Wonder keeps working through errors [12]. The joint CISA guide Careful Adoption of Agentic AI Services names four core risks of agentic AI: an expanded attack surface, privilege creep, behavioral misalignment and obscure event records [24]. Notably, the vendors are building monitors for their own models: OpenAI says Astra's written reasoning is harder to monitor than its predecessor's and is deploying production misalignment monitoring [1], and Google runs a monitor that blocks unintended workarounds [11]. If the labs consider that necessary, so should you. See MCP server security and AI agent authorization.

3. Prompt Injection Is Better, Not Solved

Google reports 0.7 percent attack success across 15 adaptive attempts on Gray Swan's benchmark [11], Anthropic says Opus 5.5 is more resistant than Opus 5 [6], and Mistral reports 93.3 percent on Lakera's B3 [16]. These are different tests run by the vendors themselves, so they are not comparable. OWASP still ranks prompt injection as the number one risk for LLM applications [25]. The test that counts is your own.

4. Data Handling Varies Widely

The retention terms differ by model. Anthropic requires 30-day retention for Fable by default [5]. OpenAI says Astra supports zero data retention for eligible API customers and is testing private safety processing [1]. Open-weight models like Muse Spark Open reach you through four clouds, seven API providers and local hardware, each with different contracts [13]. Read the data terms for the exact model and channel, not the vendor in general.

5. Cheap Models Make Shadow AI Easy

Luna at ten cents per million input tokens and Haiku 5.5 at the same price [4][8] put capable AI inside every free-tier app and internal script. When capable models are cheap and everywhere, "wait for approval" stops working as a control. The control that works is an approved alternative that is just as convenient, plus visibility into what is actually in use.

6. Self-Hosting Moves the Security Work to You

Running Muse Spark Open, Nemotron or Mistral Large 4 yourself means you own patching, hardening, weight integrity (official sources, verified checksums, pinned versions), license compliance and monitoring [13][15][16]. A self-hosted model is a server like any other, and it belongs in your vulnerability management and SIEM coverage.

A 30-Day Checklist for Any New Model

1. Freeze and name. Publish an interim policy naming the specific models and the single approved channel.
2. Find shadow use. Check cloud bills, API key registries and proxy or DNS logs for AI endpoints nobody approved.
3. Set data rules. Define the data classes that never enter a prompt (cardholder data, credentials, source code, customer records) and enforce them with controls.
4. Read the data terms. Retention, training use and logging for that exact model and channel.
5. Scope every connection. Least-privilege credentials for exactly the actions the model needs, and human approval for high-impact actions.
6. Test for prompt injection. Put hostile instructions into the documents, tickets and web pages the model reads, and see whether it acts.
7. Watch what it does. Send prompts, outputs and tool calls to your SIEM so a managed SOC or your own team can triage anomalies.
8. Test the surface. Penetration test the AI feature and its integrations like any web application.
9. Re-check PCI scope. If cardholder data can reach a model, see are AI agents in PCI DSS scope and confirm your scan covers the current scope.
10. Document the decision. Approve, restrict or block each model, with an owner and a review date.

How Clone Systems Can Help

• Penetration testing for AI-enabled applications: prompt injection, tool-call abuse and authorization testing against the features you build on these models. Managed penetration testing services.
• Managed SOC and SIEM: visibility into AI tool use and anomalous agent activity, triaged around the clock. Managed SOC service.
• PCI compliance and ASV scanning: keeping your scope current when AI endpoints could touch cardholder data. PCI ASV scanning.
• Vulnerability scanning: the new public endpoints and self-hosted model servers these releases add to your estate. Network vulnerability assessment services.
• CloneGuard AI: a security-aware assistant your team can use inside an approved channel. CloneGuard AI assistant.

If a new model has already landed in your environment and you are not sure where it touches, talk to our team about a scoped review.

Frequently Asked Questions

What new AI models have been released recently? Since September 1: OpenAI's GPT-6 Astra, GPT-6 Sol, GPT-6 Luna and GPT-6.1 Sol; Anthropic's Claude Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5; Google's Gemini 4 Argon; Meta's Muse Wonder, Muse Wonder Full and Muse Spark Open; xAI's Grok 4.7; Mistral Large 4; NVIDIA's Nemotron 3.5 Super VL and Bolt; and DeepSeek V4.1 Flash.

What is GPT-6? GPT-6 is OpenAI's current model family. Astra is the most capable, Sol is the everyday model (now GPT-6.1 Sol), and Luna is the fast, low-cost model. ChatGPT uses GPT-6 Sol for paid plans and GPT-6 Luna for Free and Go users [3][4].

What is Gemini 4 Argon? Gemini 4 Argon is Google's newest frontier model, announced September 30 and available to everyone since October 13. It has a 1 million token output limit and is strong at coding, professional work, vision and cybersecurity defense [10][11].

Is Claude Fable 5.5 out yet? No. As of October 15, Anthropic has not announced a Fable 5.5. The newest Fable is Fable 5.1 and the newest Claude models are Opus 5.5, Sonnet 5.5 and Haiku 5.5 [5].

What is the most intelligent AI model right now? On Artificial Analysis's independent index, Claude Opus 5.5 at maximum effort leads at 58, followed by Claude Sonnet 5.5 and Meta's Muse Wonder Full at 56 [18].

What is the cheapest good AI model? GPT-6 Luna, Claude Haiku 5.5 and DeepSeek V4.1 Flash cost cents per task. At about $0.21 per task Haiku 5.5 scores 43, and Xiaomi's MiMo-V2.6-Pro scores 46 at about $0.13 [18].

What is the best open-weight AI model? By Artificial Analysis's index, MiMo-V2.6-Pro (46), GLM-5.3 (45) and Kimi K3 (44). The best US option is Meta's Muse Spark Open at 39 [18].

Is Muse Spark Open open source? Meta released the weights under its own Muse Model License with limited commercial terms [13]. That is not a standard open-source license. Read the license before building a product on it.

Which AI model is best for coding? It depends on the benchmark and harness. On Artificial Analysis's Coding Agent Index, Muse Wonder Full in Muse Code and Claude Code with Sonnet 5.5 both score 68, and Opus 5.5 scores 66 [21]. Google reports a state-of-the-art 77.9 percent for Argon on DeepSWE v1.1 [10]. Test on your own codebase.

Are the new AI models safe for business use? No model is inherently safe or unsafe. Safety depends on what data reaches the model, what it can touch and what you can see. Choose by data class, read the retention terms, scope access, log activity and test before production use.

Can employees use these models at work? Only the ones you have approved under written data rules. Unapproved use of a cheap, capable model is where most exposure starts.

Do new AI features need a penetration test? Yes, before wider rollout. Prompt injection, tool-call abuse and excessive agency do not show up in a standard vulnerability scan.

Will this guide be updated? Yes. It is updated as labs ship new models, and the date at the top tells you how current it is.

Conclusion

The pace of releases is the story. Every model on this page was new this quarter, several are weeks old, and the leaders keep trading places. The organizations that stay ahead are not the ones that adopt fastest. They are the ones that can say, for every model in the environment, where it runs, what data it sees, what it can touch and who owns it. Use the checklist above, and if you want a second pair of hands on testing and monitoring, contact Clone Systems.

References

[1] OpenAI, GPT-6 Astra: A new generation of intelligence, September 3, 2026. https://openai.com/index/gpt-6-astra/ [2] OpenAI, Introducing GPT-6.1 Sol, September 29, 2026. https://openai.com/index/introducing-gpt-6-1-sol/ [3] OpenAI, GPT-6 and Intelligent UI for everyone, October 7, 2026. https://openai.com/index/gpt-6-for-everyone/ [4] OpenAI, API Changelog, OpenAI Developers, 2026. https://developers.openai.com/api/docs/changelog [5] Anthropic, Claude Fable, 2026. https://www.anthropic.com/claude/fable ; Anthropic Newsroom: https://www.anthropic.com/news [6] Anthropic, Introducing Claude Opus 5.5, September 22, 2026. https://www.anthropic.com/claude-opus-5-5 [7] Anthropic, Introducing Claude Sonnet 5.5, September 28, 2026. https://www.anthropic.com/claude-sonnet-5-5 [8] Anthropic, Introducing Claude Haiku 5.5, October 7, 2026. https://www.anthropic.com/claude-haiku-5-5 [9] Anthropic, Introducing the Anthropic Cyber Mission, October 8, 2026. https://www.anthropic.com/news/anthropic-cyber-mission ; Expanding the Cyber Verification Program, October 6, 2026. https://www.anthropic.com/news/cyber-verification-program [10] Google, Gemini 4 Argon: our next era of frontier intelligence, September 30, 2026. https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/ [11] Google, Gemini 4 Argon: Built to solve your hardest problems, October 13, 2026. https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-4-argon/ [12] Meta, Introducing Muse Wonder and Muse Wonder Full, October 12, 2026. https://about.fb.com/news/2026/10/introducing-muse-wonder-models/ [13] Meta, Introducing Muse Spark Open, October 15, 2026. https://research.meta.ai/blog/muse-spark-open [14] xAI (SpaceXAI), Introducing Grok 4.7, September 21, 2026. https://x.ai/news/grok-4-7 [15] NVIDIA, NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 model card, Hugging Face, October 15, 2026. https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 [16] Mistral, Introducing Mistral Large 4, October 6, 2026. https://mistral.ai/news/mistral-large-4/ [17] DeepSeek, API Docs Change Log, 2026. https://api-docs.deepseek.com/updates [18] Artificial Analysis, LLM Leaderboard, October 15, 2026. https://artificialanalysis.ai/leaderboards/models [19] Artificial Analysis, Changelog and articles, September to October 2026. https://artificialanalysis.ai/changelog [20] Artificial Analysis, Claude Sonnet 5.5 reaches #2 on the Artificial Analysis Intelligence Index, September 28, 2026. https://artificialanalysis.ai/articles/claude-sonnet-5-5 [21] Artificial Analysis, Muse Wonder and Muse Wonder Full, October 12, 2026. https://artificialanalysis.ai/articles/muse-wonder [22] Artificial Analysis, NVIDIA Releases Nemotron 3.5 Super VL and Nemotron 3.5 Bolt 74B A9B, October 15, 2026. https://artificialanalysis.ai/articles/nemotron-3-5-super-and-bolt [23] Artificial Analysis, Announcing the Artificial Analysis Intelligence Index v4.3, September 7, 2026. https://artificialanalysis.ai/articles/artificial-analysis-intelligence-index-v4-3 [24] CISA and international partners, Careful Adoption of Agentic Artificial Intelligence (AI) Services, May 1, 2026. https://www.cisa.gov/news-events/news/cisa-us-and-international-partners-release-guide-secure-adoption-agentic-ai [25] OWASP GenAI Security Project, OWASP Top 10 for LLM Applications 2025. https://genai.owasp.org/llm-top-10/ [26] The New Stack, Meta ships Muse Spark Open, its answer to China's best open-weight models (secondary source for the license summary), October 2026. https://thenewstack.io/meta-muse-spark-open/