@ArtificialAnlys Seeing Terra's efficiency makes me reconsider budgetary allocations in a way that feels almost philosophical.
OriginalOpenAI hat GPT-6.1 Terra veröffentlicht und damit die Grenzen der Kosteneffizienz verschoben. Es ist das erste Modell der Terra-Klasse seit GPT-5.6 und intelligenter bei weniger als einem Drittel der Kosten. Der Preis pro Token beträgt die Hälfte oder weniger von GPT-5.6 Terra – bei 1 $ / 5 $ pro Million Eingabe-/Ausgabetoken im Vergleich zu 2 $ / 12 $ beim Vorgängermodell. Das Schreiben in den Cache hat sich ebenfalls halbiert und liegt nun bei 1,25 $ pro Million Token. Zwischengespeicherte Eingaben betragen ein Viertel bei 0,05 $ im Vergleich zu 0,20 $ pro Million Token. Wichtigste Erkenntnisse: ➤ Pareto-optimal für Intelligenz vs. Kosten
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Kommentare 22
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@ArtificialAnlys where does it land on the coding agent index ?
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@ArtificialAnlys Better than grok 4.7 lol
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@ArtificialAnlys
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@ArtificialAnlys Not gonna lie, Seems like a pathetic launch based on these numbers
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@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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@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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@ArtificialAnlys Good chart, thanks
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@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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@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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@ArtificialAnlys bruh?
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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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@ArtificialAnlys This is the same Sonnet fumble. Dinner and more expensive than Sol. No place for it.
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@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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@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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@ArtificialAnlys umm https://x.com/garyfung/status/2114849687396286696
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@ArtificialAnlys Opus 5.5 still seems like the best
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@ArtificialAnlys Cool, OpenAI's really been pushing on cost efficiency these days. Looking forward to seeing its actual performance.
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@ArtificialAnlys 更强还不到三分之一成本，这波确实狠
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@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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@ArtificialAnlys Virei ateu de benchmark
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@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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스크롤하면 더 불러옵니다