@ArtificialAnlys That 4 point gap to GPT-6.1 Sol is the whole pitch. OpenAI says Terra finishes tasks over 3x faster than Sol, and for an agent clicking through apps all day, speed beats 4 index points.
원본OpenAI가 비용 효율성의 한계를 넓히며 GPT-6.1 테라를 출시했습니다. GPT-5.6 이후 최초의 테라급 모델이며, 비용은 3분의 1도 안 되면서 더 지능적입니다 토큰당 가격은 이전 모델의 $2/$12 대비 백만 입력/출력 토큰당 $1/$5로 GPT-5.6 테라의 절반 이하입니다. 캐시 쓰기 비용도 절반으로 줄어 백만 토큰당 $1.25입니다. 캐시된 입력은 백만 토큰당 $0.20에서 4분의 1 수준인 $0.05입니다. 주요 포인트: ➤ 지능 대 비용 측면의 파레토 최적
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@ArtificialAnlys im just going to keep using 6.1 sol bro this makes 0 sense
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@ArtificialAnlys The 50 point hallucination drop to 38% is a substantial shift for GPT-6.1 Terra.
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@ArtificialAnlys Seeing Terra's efficiency makes me reconsider budgetary allocations in a way that feels almost philosophical.
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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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