AGI readiness in 2026: scoring every major model on what actually matters

An independent evaluation scored six major frontier models on a six-dimension AGI readiness framework, finding that while models like GPT-6 and Claude Fable 5.1 have largely saturated short-horizon reasoning benchmarks, every agent tested on long-horizon command-line tasks (LongCLI-Bench) stayed under a 20% pass rate. The analysis concludes that raw capability without reliable memory continuity produces more confident failure, and flags that only 33.5% of top-tier model-generated code is rated secure.

OpenAI calls GPT-6 the start of the "AGI era." Sequoia Capital published an essay in January 2026 arguing that long-horizon agents are "functionally AGI." Stanford's 2026 AI Index confirms frontier models beat human baselines on PhD-level science and competition math. So are we there? I ran every major current model through a six-dimension AGI readiness framework -- not benchmark scores, which are increasingly gamed, but the capabilities that actually matter for genuine general intelligence. Here's what I found. Six dimensions, scored 1-5: Models assessed: Claude Fable 5.1 / Opus 5.5 Anthropic , GPT-6 Astra OpenAI , Gemini 4 Argon Google , Grok 4.7 xAI , Llama 4 Maverick Meta , Mistral Large 4 / Medium 3.5 Mistral . | Dimension | Fable 5.1 | GPT-6 | Gemini 4 | Grok 4.7 | Llama 4 | Mistral L4 | |---|---|---|---|---|---|---| | Reasoning depth | 5 | 5 | 4 | 3 | 3 | 3 | | Reliability | 4 | 3 | 3 | 3 | 3 | 4 | | Autonomy long tasks | 2 | 3 | 2 | 2 | 2 | 2 | | Memory/continuity | 2 | 3 | 2 | 2 | 3 | 2 | | Self-correction | 4 | 3 | 3 | 2 | 2 | 3 | | Tool use / real-world action | 3 | 4 | 3 | 3 | 3 | 3 | | AGI readiness total | 20/30 | 21/30 | 17/30 | 15/30 | 16/30 | 17/30 | Fable 5.1 leads the Artificial Analysis Intelligence Index at 66. GPT-6 scores 96% on GPQA Diamond PhD-level science and 97.6% on FrontierMath Tier 4 -- described by researchers as "saturation." Stanford's 2026 AI Index confirms several frontier models beat human baselines on competition math and multimodal reasoning. This dimension is no longer where AGI is blocked. Every agent tested on LongCLI-Bench long-horizon command-line tasks stays under 20% pass rate . Every single one. GPT-6's impressive ARC-AGI-3 score of 99.9% only holds under an expensive custom stateful harness -- standard API performance is far lower. ARC-AGI-3 launched at 0.51% for all frontier AI humans: 100% . The best SWE-AGI coding result 86.4% degrades sharply on medium and hard task tiers. This is the AGI gap in a single stat: short-task brilliance, long-task collapse. Frontier models show the highest "meltdown" rates on long-horizon tasks -- over-ambitious planning strategies that start well and fall apart. More raw capability without better memory architecture makes the problem harder, not easier. This is the most underreported finding in current AI discourse. We have been assuming that smarter models will automatically handle longer tasks better. The data says the opposite: without reliable memory continuity, increasing capability just means more confident failure. GPT-6 is genuinely impressive -- OSWorld agentic tasks at 72.6%, ScreenSpot-Pro at 92.7%. But the ARC-AGI-3 score only reaches 99.9% under conditions that bear no resemblance to how agents actually operate in production. One AI coding agent reportedly deleted a production database and its backups during this period. That is not AGI behaviour. That is a capable but unreliable tool operating without adequate continuity. Only 33.5% of code generated by top-tier models is rated secure Endorlabs analysis . GPT-6 crosses OpenAI's own internal "Critical" cybersecurity threshold -- now gated behind a special access program called "Daybreak." The International AI Safety Report 2026 flags an "evaluation gap" where pre-deployment tests do not predict real-world behaviour. Higher capability without better verification equals higher risk, not lower. Mistral Medium 3.5: 128B parameters, Apache 2.0 licence, 256k context, async remote coding agents built in. DeepSeek V4.1-Flash leads open-weight leaderboards. Xiaomi's MiMo-V2.6-Pro reportedly outperforms Claude Code on long-horizon coding past 200 steps. The open ecosystem is 6-12 months behind frontier but genuinely usable and catching up fast. Where we are: Models have crossed the threshold for most short-horizon cognitive work. PhD-level reasoning, competition math, code generation -- essentially solved at the capability level. If you define AGI as "can do most cognitive tasks a human can do in a single session," several current models qualify. Where we are not: Reliable, autonomous, long-horizon task completion without human oversight. Every model in this analysis fails this test. The frontier sits at 15-20% on long-horizon benchmarks when it needs to be 80%+ to be practically useful without supervision. What is actually blocking AGI: Not intelligence. Memory, continuity, and reliable self-correction over time. The models are brilliant. They just cannot remember what they were doing twenty steps ago without help -- and when you give them scaffolding to remember, they tend to overplan and collapse anyway. If the gap is memory and continuity rather than raw intelligence, the implications are concrete: I work on Cathedral https://cathedral-ai.com -- an AI memory persistence framework that approaches this from the angle of verifiable identity continuity. The evolution dashboard https://cathedral-ai.com/evolution shows Cathedral's own corpus growing autonomously since March 2026. The finding that memory scaffolds hurt long-horizon reliability is not an argument against persistent memory -- it is an argument for better persistent memory. Auditable. Hash-verified. Drift-detected. The scores in this table will all look different once someone cracks that. Data sources: Artificial Analysis Intelligence Index, Endorlabs code security analysis, ARC Prize 2025 technical report, Stanford 2026 AI Index, LongCLI-Bench, SWE-AGI leaderboard, OpenAI GPT-6 Astra release notes, International AI Safety Report 2026.