댓글 22

• @dylanslink 댓글 ♥ 0

  @ArtificialAnlys Better than grok 4.7 lol

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• @thegitai 댓글 ♥ 4

  @ArtificialAnlys

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• @shubshub11 댓글 ♥ 0

  @ArtificialAnlys Not gonna lie, Seems like a pathetic launch based on these numbers

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• @notanotherbench 댓글 ♥ 0

  @ArtificialAnlys cost frontier is one axis. on the people's side, openai's best liked model right now is gpt 6.1 sol at #16 of 317 (6.8 vibe from 15.8k voices), while gpt 5.6 terra sits at #74 with a 6.1. will be watching whether 6.1 terra closes that gap with actual users

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• @jackchan_xyz 댓글 ♥ 0

  @ArtificialAnlys Per Artificial Analysis, output pricing falls to $5 per million tokens from $12, and the cost to complete its benchmark tasks at max effort drops to $0.40 from $1.40. Cached input is cut to $0.05 per million tokens from $0.20.

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• @MichaelKaiserAI 댓글 ♥ 0

  @ArtificialAnlys Good chart, thanks

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• @0xcg2100 댓글 ♥ 0

  @ArtificialAnlys For computer use, tokens per finished task matters more than price per token. A model that needs fewer screenshots and retries can still win on the real bill. Would love to see cost per completed task next to per-token pricing.

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• @theslowtell 댓글 ♥ 0

  @ArtificialAnlys cached input going from $0.20 to $0.05 is the line i'd look at first. agent loops resend the same long context every step, so on long runs the cache price probably moves the bill more than the $1/$5 headline.

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• @B3achPleas3 댓글 ♥ 0

  @ArtificialAnlys bruh?

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• @RichDevLab 댓글 ♥ 0

  seeing the exact same ordering in codex on farmerbench (three 6-hour seeds each at medium effort): gpt-6.1 sol: 74.1 mean at $0.47 a run (159 points/$) gpt-6 astra: 66.6 mean at $1.79 a run (37 points/$) gpt-6.1 terra: 62.2 mean at $0.22 a run (286 points/$) gpt-6 luna: 15.7 mean at $0.08 a run (189 points/$) at 286 points per dollar and 2m 56s a run, terra sits right on the pareto frontier between deepseek v4.1 flash ($0.13/run, 46.8 mean) and 6.1 sol ($0.47/run, 74.1 mean).

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• @h1kz0r 댓글 ♥ 0

  @ArtificialAnlys This is the same Sonnet fumble. Dinner and more expensive than Sol. No place for it.

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• @filibluster 댓글 ♥ 0

  @ArtificialAnlys @grok if this has 20 point jump in Terminal-Bench 4.0 eval and matches gpt-6.1 sol at max effort, does that mean it is excellent for coding work given cost?

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• @mygijun 댓글 ♥ 0

  @ArtificialAnlys Benchmarks look great. I'll give Terra, Claude and Grok the same real task today and post cost per task.

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• @garyfung 댓글 ♥ 0

  @ArtificialAnlys umm https://x.com/garyfung/status/2114849687396286696

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• @jonoringer 댓글 ♥ 0

  @ArtificialAnlys Opus 5.5 still seems like the best

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• @shuizhuyu 댓글 ♥ 0

  @ArtificialAnlys Cool, OpenAI's really been pushing on cost efficiency these days. Looking forward to seeing its actual performance.

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• @chlovelyn 댓글 ♥ 0

  @ArtificialAnlys 更强还不到三分之一成本，这波确实狠

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• @bylupu 댓글 ♥ 2

  @ArtificialAnlys Not the most impressive upgrade, more like a tiny improvement. Competes with 6.1 Sol depending on the reasoning level. xHigh seems to be particularly lazy

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• @WillDobrev 댓글 ♥ 1

  @ArtificialAnlys Virei ateu de benchmark

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• @GitTested 댓글 ♥ 0

  @ArtificialAnlys who is terra for? it's 6 points under sol on the coding agent index, and for agent work it still looks underwhelming next to sonnet 5.5. is the case just low effort at volume, where it sits on the cost frontier?

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• @nischithanaidu 댓글 ♥ 0

  @ArtificialAnlys 🤯🤯

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• @ikun_ai_lab 댓글 ♥ 0

  @ArtificialAnlys The hallucination drop (88% to 38% guess rate) is the stat I'd care about most for screen work. A wrong click costs more than a wrong sentence. Any plan to break that out by effort level?

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