mihawkxxx

AI enthusiast. Builder. I make stuff with new models and post the results.

[mihawkxxx @mihawkxxxxx]

@emanueledpt ragebait

[mihawkxxx @mihawkxxxxx]

@ArtificialAnlys bro your benchmark is shit

[mihawkxxx @mihawkxxxxx]

@TheZvi Scam benchmark

[mihawkxxx @mihawkxxxxx]

@Da7_Tech bro delete it

[mihawkxxx @mihawkxxxxx]

@theo Scam benchmark

[mihawkxxx @mihawkxxxxx]

ABSOLUTE CINEMA

[Theo - t3.gg @theo]

GPT-6 Astra is world class at Blender and 3 dimensional reasoning. This was a 1-shot game it created, all running in browser.

[mihawkxxx @mihawkxxxxx]

Scam benchmark paid by Antrophic

[Artificial Analysis @ArtificialAnlys]

GPT-6 Astra makes significant gains in the Artificial Analysis Coding Agent Index, scoring equal to Fable 5 at lower cost. In the Intelligence Index, it uses fewer tokens than GPT-5.6 Sol for similar performance, but this is outweighed by higher prices

Pricing is 2.5x GPT-5.6 Sol’s current prices across the board, up from $4/$20 to $10/$50 per million input/output tokens, with the same 90% discount for cache reads and 25% premium for cache writes.

We see distinct stories across our two flagship Indices. In the Artificial Analysis Coding Agent Index, GPT-6 Astra equals Fable 5 at less than half the cost, driven by significant token efficiency gains. In the Artificial Analysis Intelligence Index, GPT-6 Astra is more token efficient than its predecessor for similar performance, but this is offset by the price increase.

Artificial Analysis Coding Agent Index - key takeaways:

➤ Rivals top models: In Codex, GPT-6 Astra scores 67 in the Index - approximately equal to Claude Opus 5 and Fable 5 in Claude Code, and Muse Spark 1.3 in Muse Code. Fable 5.1 in Claude Code leads the Index with a score of 70.

➤ 70% more token efficient than GPT-5.6 Sol: GPT-6 Astra sees a substantial improvement in token efficiency, using one third of the tokens compared to GPT-5.6 Sol (max) in the Codex harness, and one fifth of the tokens of Claude Opus 5 (xhigh). Various effort levels of the model occupy the Pareto frontier of token efficiency.

➤ Leads Coding Agent Index cost efficiency frontier: At max effort, GPT-6 Astra costs about the same as GPT-5.6 Sol (max) while scoring 2 points higher on the Index. Per task, the model is less than half the cost of Claude Fable 5, for the same score.

Artificial Analysis Intelligence Index - key takeaways:

➤ Sits beside GPT-5.6 Sol in Intelligence: GPT-6 Astra scores equal to GPT-5.6 Sol in the Index at 61. This is 5 points lower than Claude Fable 5.1 (max with fallback). The model also trails Meta’s newly released Muse Spark 1.3 (max).

➤ ~10% fewer output tokens, offset by price increase: GPT-6 Astra defines a new Pareto frontier for Intelligence Index vs Output Tokens per Task - with a ~10% reduction in token use at max effort compared to GPT-5.6 Sol. However, due to the 2.5x increase in price, the model is 75% more expensive per task than its predecessor at max effort.

➤ Hallucinates half as much as GPT-5.6 Sol: GPT-6 Astra sees a large jump in AA-Omniscience, our knowledge and hallucination benchmark. This is driven by a significant decrease in hallucination rate from 92% to 51% at max effort. Unlike some models, this improvement does not come at the cost of accuracy - Astra increased accuracy by 4 points at the same time.

➤ ~80 point gain in AA-Briefcase Elo: GPT-6 Astra improves ~80 points in AA-Briefcase, our frontier long-horizon knowledge work evaluation. Models are tested on multi-week projects, with many linked tasks and thousands of source files. Astra sees a significant increase in both rubric scores and Analytical Quality Elo in AA-Briefcase compared to its predecessor. In the other direction, we observe a reduction in Presentation Quality Elo, where GPT-5.6 Sol (max) still leads all models.

➤ Mixed progress on other evaluations: The model sees a 6 point gain in Humanity’s Last Exam, a long-standing evaluation with emphasis on mathematics, science, and humanities. This is offset by a drop of ~80 Elo points in GDPval-AA v2 - a benchmark we adapted from OpenAI’s dataset measuring economically valuable tasks across 44 occupations. We also observe 2-3 point regressions on other evaluations across a mix of capabilities, including reductions in τ³-Banking (customer support), SciCode (Python problems in a scientific domain), and AA-LCR (long context reasoning over large documents).

Congratulations @OpenAI and @sama on the launch!

[mihawkxxx @mihawkxxxxx]

@ArtificialAnlys Scam benchmark

[mihawkxxx @mihawkxxxxx]

@synthwavedd Scam benchmark paid

[mihawkxxx @mihawkxxxxx]

@DanDr1s FUCK

[mihawkxxx @mihawkxxxxx]

@Angaisb_ I hope this fake information

[mihawkxxx @mihawkxxxxx]

@Angaisb_ sad

[mihawkxxx @mihawkxxxxx]

@synthwavedd Shut up and give them time

[mihawkxxx @mihawkxxxxx]

@thsottiaux let’s go

[mihawkxxx @mihawkxxxxx]

today?

[Tibo @thsottiaux]

I think the best way to describe OpenAI's culture is as a mega startup. It's hard to believe unless you see it from the inside. Extreme ownership, care and pace.

[mihawkxxx @mihawkxxxxx]

@synthwavedd yes

[mihawkxxx @mihawkxxxxx]

@wecraveai Fucking insane

[mihawkxxx @mihawkxxxxx]

I hope so

[Dr Singularity @Dr_Singularity]

A lot of signs are pointing to today being a very big day for AI.

[mihawkxxx @mihawkxxxxx]

@Da7_Tech bro likes ragebaiting

[mihawkxxx @mihawkxxxxx]

They really cooked Gemini. Muse Spark 1.3 is a really good model and has the lowest price for that level of intelligence. I tested Gemini and Muse Spark, and Muse Spark is much better

[Alexandr Wang @alexandr_wang]

i really hate to say it, but…

gemini who? 🏎️💨