Jev AI Benchmarks cases, page 2

@crislenta reports a benchmark of 500 real-time agents in parallel in a 3D environment: about 500ms average latency and 35 API calls per second, with what they call a naive implementation and zero optimizations.

Jev is actually insane. We benchmarked it, and the results completely change the game for us: > 500 real-time agents > running in parallel > in a 3D environment…

Quoted @CompleteSkeptic

@anshnanda posts a video comparing Jev with GPT-6 Astra, Fable 5.1, and Deepseek v4.1 Flash on four tasks: Doom, a year of Bitcoin trading, 1,500 emails at 200ms each, and 857 support tickets versus Intercom Fin. They say the Jev bill for the video was $0.49.

By now you've probably seen Jev on your timeline. Yes, it's an insanely fast & cheap model. But how does it compare against GPT-6 Astra, Fable 5.1, and Deepseek…

@nutlope had Jev play chess against GLM 5.3. GLM won by checkmate in 29 moves. They report about 0.3s and under $0.0001 per Jev move, about 5.8s and $0.008 per GLM move, and 24 cents for the game. The takeaway they state is to use Jev for fast classification and an LLM when reasoning or lookahead is needed.

Jev vs GLM 5.3 at chess! Results: ◾ GLM 5.3 won by checkmate in 29 moves ◾ Jev: ~0.3s and <$0.0001 per move ◾ GLM 5.3: ~5.8s and ~$0.008 per move ◾ The whole ga…

@SUOHA_AI compares Jev and DeepSeek on matching 15 brands to PR hooks in a live news stream and tagging intent. They say Jev finished all 428 items in 28 seconds while DeepSeek V4.1-flash had done 6 of 428. They attribute the gap to Jev returning a decision in one pass instead of writing text, and point to an Elvis Sun GitHub demo.

完全睡不着了....这个视频让你完全看懂JEV的恐怖能力 我实际演示了一下，用 JEV 和 DeepSeek 做同一个任务：从当天的海量实时新闻流里，为 15 个品牌快速匹配有没有适合借势的公关热点，并打上结构化意图分类标签 结果是：28 秒内，Jev 狂刷完了整整 428 条数据，并完成了全部的目标分解与分类；而同一…

Quoted @SUOHA_AI

• github.com

@libukai compared Jev on OpenRouter with Gemini Flash Lite on classifying whether People’s Daily articles mention Hubei, using 500 positive and 500 negative examples. They report about 0.35s per article versus 3s, disagreement on about 15% mostly from person names, and about $1 for roughly 5,000 items and 30M tokens.

这两天关于 Jev 这个模型的热度非常之高，甚至可以说是近半年我重点关注了英推以来，看到的原创内容密度最高的一次。 正好我过去一年一直在跑一个小项目，其中有一个环节就是要对每天出版的《人民日报》上的新闻做一个分类，判断每一篇文章中是否包含有湖北相关的元素。 一年跑下来，积累了接近 2 万 4 千条数据，其中判定包含的有…

@ashutoshftw had Jev, Claude Haiku 4.5, and Gemini 3.5 Flash-Lite play Tetris with the same 200 pieces and the same legal moves. They report Jev at 9200 points, 75 lines, 300ms per move, and 0 errors, ahead of Claude (8900, 1.52s, $0.48) and Gemini (9000, 1.13s, $0.02).

Had jev, claude haiku 4.5, and gemini 3.5 flash-lite play tetris. Same 200 pieces, same legal moves, real time. Jev: 9200 pts, 75 lines, 300ms/move, 0 errors Cl…

@injaneity says TypeSafe Jev is 5x faster and 1000x cheaper than Astra at playing 2048, and that it clearly does not match Astra or Fable at reasoning.

@typesafeai jev is 5x faster and 1000x cheaper than astra at playing 2048! though it (clearly) does not excel at reasoning compared to state of the art models l…

Quoted @injaneity