返信 21

• @shubshub11 返信 ♥ 0

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

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• @notanotherbench 返信 ♥ 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 返信 ♥ 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 返信 ♥ 0

  @ArtificialAnlys Good chart, thanks

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• @0xcg2100 返信 ♥ 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 返信 ♥ 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 返信 ♥ 0

  @ArtificialAnlys bruh?

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• @RichDevLab 返信 ♥ 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 返信 ♥ 0

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

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• @filibluster 返信 ♥ 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 返信 ♥ 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 返信 ♥ 0

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

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• @jonoringer 返信 ♥ 0

  @ArtificialAnlys Opus 5.5 still seems like the best

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• @shuizhuyu 返信 ♥ 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 返信 ♥ 0

  @ArtificialAnlys 更强还不到三分之一成本，这波确实狠

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• @bylupu 返信 ♥ 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 返信 ♥ 1

  @ArtificialAnlys Virei ateu de benchmark

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• @GitTested 返信 ♥ 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 返信 ♥ 0

  @ArtificialAnlys 🤯🤯

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• @ikun_ai_lab 返信 ♥ 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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• @Relve_official 返信 ♥ 0

  @ArtificialAnlys Imagine your AI finishing the entire task while you're still reading the instructions 😅

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