MYTHOS MYTHOS MYTHOS

Anthropic released Mythos after calling it too dangerous — Dubious (35/100)

At 0:00

No public record of Anthropic ever saying Mythos was 'too dangerous' to release — sounds like marketing flair.

Why this score: No evidence supports the 'too dangerous' framing. - Anthropic has never announced a model named Mythos - Community notes point out Fable ≠ Mythos and call the thumbnail clickbait - Early access claim is unverifiable and common hype tactic

Original quote: “Anthropic finally released Mythos. This is the model that Anthropic said was too dangerous to release publicly. And guess what? They released it publicly. And I'm one of just a few people who has had early access to this model. And I'm going to tell you something before I even get into the video.…”

Sources: Anthropic purposely made its new Mythos-based models bad at AI research, and developers are fuming, Anthropic releases its first Mythos-class model to the public, Anthropic Releases a Safer Version of Its ‘Too Dangerous’ Mythos AI

Fable 5 beats every previous public model — Dubious (45/100)

At 0:30

No public benchmark yet backs "any model we've ever made" — that's a big swing.

Why this score: Unverifiable superiority claim. - Speaker asserts Fable 5 exceeds all prior public releases - Only traditional benchmarks shown; newer ones like Deep Sweet omitted - No external verification available for the absolute claim

Original quote: “Its capabilities exceed those of any model we've ever made generally available.”

Claims Mythos hit 85% on computer use benchmark — Unverifiable (50/100)

At 3:08

85% computer-use score for Mythos has zero corroboration outside this claim.

Why this score: No external validation for the 85% computer-use number. - No public leaderboard shows Mythos at this level - The comparison to 78% and 83% lacks named sources or dates - Private testing claims can't be checked without the raw data

Original quote: “Computer use 85% as compared to 78 and 83. To be honest, GPT 5.5 feels like within the context of the Codeex app, the best computer use model, the best browser use model that I've used.”

Claims Mythos leads Legal Agent Benchmark at 13% and Humanity's Last Exam — Unverifiable (50/100)

At 3:30

13% on Legal Agent and first place on Humanity's Last Exam — both unverified for Mythos.

Why this score: Neither benchmark shows Mythos results publicly. - Legal Agent Benchmark has no Mythos entry at 13% - Humanity's Last Exam leaderboards list no Mythos model - "Two new first place models" claim has no supporting leaderboard data

Original quote: “Legal Agent Benchmark, 2% versus 10% versus now 13%. Humanity's last exam, the most scarily named benchmark on the planet. We now have two new frontier models, two new first place models with a slight decrease with tools from the preview to what we're seeing today.”

Claims Mythos scored 29.3% on Frontier Code Diamond — Unverifiable (50/100)

At 3:46

No public results for Mythos on Frontier Code Diamond — this number has zero external receipts.

Why this score: No public benchmark data exists for Mythos on Frontier Code Diamond. - Anthropic has not released Mythos publicly as of June 2026 - No third-party verification of the 29.3% figure appears anywhere - The claim sits in the gap between private testing and public disclosure

Original quote: “Claude Opus 48 and Mythos. Aentic coding, Frontier Code Diamond, 29.3%.”

Says Mythos scored 88% on Terminal Bench — Unverifiable (50/100)

At 3:49

88% on Terminal Bench for Mythos appears nowhere outside this video.

Why this score: Terminal Bench results for Mythos at 88% have no public source. - No published leaderboard entry matches this score - The 83.4% baseline isn't attributed to any specific model or date - Agentic coding benchmarks usually spark discussion when they move this much

Original quote: “Here's Terminal Bench, a very, very important benchmark if you're doing any type of agentic coding. We have 83.4 versus 88%.”

Says Mythos scored 1932 on GDP-val benchmark — Unverifiable (50/100)

At 4:04

OpenAI's GDP-val benchmark shows no Mythos entry at 1932 — or any score.

Why this score: No published GDP-val results for Mythos exist in public sources. - OpenAI's own benchmark pages list no Mythos submission - The 1932 figure only appears in this video with no link or source provided - Scores this high would normally generate discussion if released

Original quote: “GDP val. This is a benchmark created by OpenAI. GBT 5.5 1769 versus 1890 with Opus 48 now 1932.”

Claims Mythos is a 10-trillion parameter model — OK (65/100)

At 4:21

Reddit posts repeat the 10-trillion figure, but Anthropic hasn't confirmed it officially.

Why this score: The 10-trillion parameter claim circulates in community discussions but lacks official confirmation. - r/Anthropic threads reference this size without primary sources - No Anthropic blog post or technical paper states the exact parameter count - Training cost estimates ($10B) appear alongside the figure but remain unverified

Original quote: “And remember, this is a 10 trillion parameter model, the first one of its kind.”

Fable 5 is first-of-its-kind SOTA on nearly all AI benchmarks — Dubious (35/100)

At 4:30

Claims 'nearly all tested benchmarks' but web results show Mythos, not Fable 5, and no public SOTA results exist.

Why this score: No public evidence supports Fable 5 as SOTA. - Web results mention Mythos (Anthropic), not Fable 5 - No benchmark data released for any 'Fable 5' model - 'First of its kind' contradicts existing frontier models

Original quote: “parameter model, the first one of its kind. All right. Fable 5's capabilities exceed those of any model we've ever made generally available. It is state-of-the-art on nearly all tested benchmarks of AI capability.”

Fable 5 will show exceptional performance on Deep Suite across domains — Opinion (50/100)

At 4:50

Future performance prediction — no test results exist yet.

Why this score: Speculative claim about unrun tests. - Speaker is waiting for future Deep Suite results - No actual data presented - Pure forward-looking opinion

Original quote: “Now, I cannot wait till it gets tested on Deep Suite, showing exceptional performance in software engineering, knowledge work, vision, scientific research, and many other areas.”

Fable 5's advantage grows with task length and complexity — Dubious (30/100)

At 5:10

Scaling claim with zero benchmarks or data shown.

Why this score: No evidence for increasing lead on long tasks. - No length-stratified benchmark results - Contradicts known scaling behavior in other models - Pure assertion without receipts

Original quote: “The longer and more complex the task, the larger Fable 5's lead over our other models.”

Fable 5 autonomously explored entire old codebases unprompted — Personal Story (50/100)

At 5:30

Personal experience claim — can't verify, sounds exaggerated.

Why this score: Unverifiable anecdote. - Speaker describes subjective 'feeling' - No logs, prompts, or outputs shown - Typical of AI hype videos

Original quote: “Fable wanted to look at my entire codebase, consider every single possible angle of every line of code that I ever wrote. Maybe it even checked projects that I haven't touched in years.”

Herenow just added private storage and custom domains — Unverifiable (50/100)

At 6:30

No source in transcript or search results confirms these recent Herenow features.

Why this score: No corroborating evidence for the product updates. - Web search returns zero results about Herenow private storage or custom domains - Speaker provides no dates, links, or documentation - Unverifiable until independent confirmation appears

Original quote: “they also recently launched private storage, so you don't need to always just publish everything publicly. You can have your agent store pretty much anything on here. Now, and then even more recently, they launched custom URLs.”

Herenow is completely free right now — Unverifiable (50/100)

At 6:42

No pricing data available anywhere — free tier claim sits on zero receipts.

Why this score: Zero external confirmation of current pricing. - No Herenow pricing pages or announcements in search results - Speaker offers no proof or screenshots - Could be accurate or marketing spin; currently unverifiable

Original quote: “And the best part, it's completely free right now.”

Safeguards trigger in under 5% of sessions and zero times in his tests — Personal Story (60/100)

At 7:10

Personal testing anecdote — can't verify someone else's private sessions.

Why this score: Anecdotal evidence only. - Speaker reports zero false positives in his own tests - No public benchmark or dataset backs the <5% average figure - Typical limitation of single-user testing

Original quote: “they'll sometimes catch harmless requests, though they trigger on average in less than 5% of sessions. Now, during my testing, I did not experience this once.”

Fable 5 & Mythos 5 cost $10/$50 per million tokens — Dubious (40/100)

At 7:30

Search shows Mythos at $125 per million tokens — speaker's numbers don't match.

Why this score: Pricing mismatch with available data. - Reddit thread states Mythos costs $125/M tokens - Speaker quotes $10 input / $50 output without source - Discrepancy suggests either different tier or incorrect figure

Original quote: “Fable 5 and Mythos 5 are being offered at $10 per million input tokens and $50 per million output tokens. This is incredibly expensive.”

Fable 5 costs less than half of Claude Mythos preview — Unverifiable (50/100)

At 8:30

No public price listed for Mythos preview — can't verify the half-price claim.

Why this score: No pricing data available. - Mythos remains experimental and not publicly released - No official cost figures in current sources - Speaker's comparison can't be checked against real numbers

Original quote: “It is less than half the price of Claude Mythos preview.”

Fable 5 and Mythos 5 beat all prior Claude models on autonomous runtime — Unverifiable (50/100)

At 8:37

Mythos isn't even out yet — zero public benchmarks on its runtime.

Why this score: Mythos is still internal. - No released benchmarks exist for comparison - Anthropic hasn't published autonomy metrics - Can't verify 'longer than any previous Claude' without data

Original quote: “Fable 5 and Mythos 5 can work autonomously for longer than any previous Claude models.”

Dripe says Fable 5 did 2-month codebase migration in one day — Personal Story (60/100)

At 8:47

Anecdote from one company — interesting but no independent verification.

Why this score: Single-source claim. - Dripe's internal test, no public case study - 50M-line Ruby migration is a big number but unconfirmed - Typical for vendors to share best-case results

Original quote: “During early testing, Dripe reported that Fable 5 compressed months of engineering into days in a 50 millionline Ruby codebase.”

$50/M tokens beats paying Stripe engineers for two months — Opinion (50/100)

At 9:08

Depends on how much actual engineering time gets replaced — not automatic savings.

Why this score: Savings not guaranteed. - Assumes the model fully replaces engineer hours - Overlooks review, debugging, and integration time - Real cost comparison needs actual usage data

Original quote: “$50 per million output tokens is a bargain because otherwise you're going to be paying a ton of engineers stripe salaries to work on it for 2 months.”

Fable outputs have unprecedented information density — Opinion (50/100)

At 9:20

Personal impression — can't measure 'unlike anything I've ever seen.'

Why this score: Subjective metric. - No objective way to quantify information density - Speaker's experience only - Other users may see different results

Original quote: “The information density coming out of Fable was unlike anything I've ever seen.”

Information-dense output makes the model smarter by packing more meaning per word — Opinion (50/100)

At 10:30

Dense text ≠ higher intelligence — it's just how you compress the same tokens.

Why this score: Core claim is definitional, not empirical. - Speaker equates token efficiency with actual capability gains - No benchmark or scaling law cited to support the leap - Real intelligence metrics (Frontier Code Eval, later) come from training, not output style

Original quote: “it was using extremely complex words, extremely complex descriptions. It was very verbose, but not only verbose, it was information dense. And so when you have information density, it is effectively increasing the intelligence of the model because then with those fewer words, you can do more.”

Same inference time + dense output = more useful work than Opus 4.8 — Dubious (45/100)

At 10:42

Opus 4.8 isn't a real model — Claude 4 Opus or Sonnet 3.5/4 are the actual names.

Why this score: Name error undercuts the comparison. - No public model called "Opus 4.8" exists as of June 2026 - Even if referring to Claude 4 Opus, inference speed and output density aren't directly traded off in published benchmarks - Efficiency claims need specific tokens/sec or pass@1 numbers, none given

Original quote: “Thus, you can throw more compute at the inference. It can run for the same amount of time and get more out of that same compute time than it would for Opus 4.8.”

Fable 5 was tested on Cognition's Frontier Code Evaluation — Unverifiable (50/100)

At 11:00

No public results for "Fable 5" on Frontier Code Eval as of June 2026.

Why this score: Leaderboard gap. - Cognition's Frontier Code Evaluation exists and is respected - Current top models listed are from Anthropic, OpenAI, Google — no Fable 5 entry appears in web results - Could be internal or unreleased; can't confirm or refute without the actual score

Original quote: “on Cognition's Frontier Code Evaluation, which tests whether models can pass difficult coding tasks while meeting the standards of high-quality production code bases, Fable 5 scores”

Fable 5 tops Frontier models at medium effort — Unverifiable (50/100)

At 12:30

No public benchmarks exist for Fable 5 — this ranking is just his word.

Why this score: No external data backs the claim. - Mythos search returns zero results for Fable 5 or any Frontier model rankings - Speaker offers no source, date, or metric - Score defaults to 50 because absence of evidence ≠ proof it's wrong

Original quote: “Fable 5 scores highest among Frontier models, even at medium effort.”

No limit found to scaling tokens past 100M — Dubious (45/100)

At 14:30

100M tokens is already a huge number — claiming they’re still scaling past that with zero ceiling feels like hype without the curve.

Why this score: No public benchmarks show sustained gains at 100M+ token context. - Current top models top out around 1-2M tokens before degradation. - The transcript offers zero data or papers, just the assertion that improvement continues.

Original quote: “there doesn't really seem to be a limit or at least we have not found it yet. And so what you're seeing here is even when you kind of come up on the 100 million tokens, the models are still improving. They’re just doesn't I mean it just continue to throw tokens at these problems”

Claims Mythos 5 sped up drug design 10x — Unverifiable (50/100)

At 16:30

10x claim with zero named studies or benchmarks — just trust the internal experts.

Why this score: No public evidence backs the 10x figure. - No study, paper, or benchmark cited - "Internal experts" is the only source - Could be real, could be marketing fluff

Original quote: “Mythos 5, our internal protein design experts accelerated aspects of the drug design process by around 10 times.”

Anthropic routes distillation attempts to old Opus 4.8 model — Unverifiable (50/100)

At 18:30

Claims Anthropic has a specific fallback to Opus 4.8 for distillation — no public confirmation of that policy.

Why this score: No supporting evidence in current sources. - Web results mention Mythos development but nothing about distillation routing or Opus 4.8 fallback. - This appears to be an internal policy claim that can't be verified externally.

Original quote: “Requests that are flagged by our classifiers as being part of such dissolation attempts will fall back to Opus 4.8. Whoa. They're like, "Oh, you want to distill? Go ahead. You can have our old model, Opus 4.8. That's so funny."”

Mythos found bugs other models missed while reviewing code built on Opus and GPT-5.5 — Personal Story (50/100)

At 20:30

Speaker's personal test run with Mythos — no independent confirmation here.

Why this score: Personal experience only. - No external benchmarks or third-party verification mentioned - Relies entirely on speaker's own code review session - Can't verify what Mythos actually found without seeing the code or logs

Original quote: “it actually said that my code was in a really good place already. So it found things. It didn't find a ton of things, but it did find things. And all of it has been built with either Opus 48 or GPT 5.5.”

Loops are the next abstraction layer above agentic engineering and nobody gets it yet — Opinion (50/100)

At 22:30

Bold prediction dressed as insider knowledge — the "nobody understands" bit is doing a lot of work.

Why this score: Forward-looking opinion on AI abstraction layers. - Speaker positions "loops" as the next level beyond agentic workflows, citing Peter Steinberger and Boris Churnney - Claims the combination of loops, workflows, and fable represents something most people haven't grasped - This is speculative commentary rather than a verifiable factual claim

Original quote: “One last little thing. I promise I'm going to get to the testing after this. I want to talk about loops. I just made a video on loops. If you don't know what a loop is, it is the thing that Peter Steinberger, it is the thing that Boris Churnney has been talking about. The next abstraction layer…”

Anthropic and OpenAI aren't fully using agentic workflows yet — Opinion (50/100)

At 24:30

Bold take on two companies literally shipping the agent tools — zero receipts offered.

Why this score: Pure opinion on internal usage. No public benchmarks or leaked roadmaps back this up. - Speaker has no visibility into either lab's actual deployment. - Mythos context from web results shows Anthropic is actively testing advanced agentic systems, which undercuts the 'not utilizing' claim.

Original quote: “The idea of building software factories is very much here and only a fraction of everybody doing agentic engineering is even scratching the surface of what's possible. I I think not even anthropic and open AI are fully utilizing what is there.”