Topic

Agentic Workloads

Multi-step autonomous tasks such as computer use, where accuracy, latency and cost trade off across models and hardware.

Current stories

buildConfirmed9 publishers

OpenAI's first Jalapeno numbers buy it leverage, not a procurement input

The 1.7x to 3.6x latency range is set by the baseline systems, not the chip, and the report's own publication date is unsettled. Read it as direction, not evidence.

Publishers:archive.thedeepview.comdev.tolatent.spacemezha.netruntimewire.comservethehome.comthe-decoder.comthenewstack.iotomshardware.com

Perspective Coverage

9 publishers

Builder: Builder 41%
Operator: Operator 31%
Investor: Investor 28%

Reality

Evidence52
Adoption14
Hype gap+38
Incentives82
Confidence68

buildOne report1 publisher

A 1.5x per-token price still bought a 25 percent cheaper correct answer in AWS's benchmark

AWS's open harness records $0.0021 per correct AIME answer for gpt-5.6-luna after an 80 percent Bedrock price cut. The figure depends on running luna with reasoning disabled while mini runs at its defaults.

Publishers:aws.amazon.com

Reality

Evidence58
Adoption: Insufficient
Hype gap+15
Incentives80
Confidence55

leadershipOne report1 publisher

Gemini 3.8 Flash buys three index points for a 45% rise in cost per task

Google held its token rates flat, but the measured cost of an Intelligence Index task rose from $0.40 to $0.58 while the score still trails Fable and Sol. Picking a model for agent work is now a pricing exercise.

Publishers:implicator.ai

Reality

Evidence64
Adoption24
Hype gap+14
Incentives46
Confidence61

buildOne report1 publisher

Thirty-nine retries fit inside the price gap between GLM-5.3-Flash and Opus 4.8

Zhipu's open-weight MoE ties Claude Opus 4.8 on one composite index at roughly a fortieth of its list rate. At that price gap, the practical question stops being which model is better. It becomes how many attempts your router can afford.

Publishers:dev.to

Reality

Evidence24
Adoption31
Hype gap+28
Incentives62
Confidence30

investOne report1 publisher

DeepSeek V4 doubled its OpenRouter token share, and the bill it displaced was ~130x bigger

OpenRouter says DeepSeek went from 9% to 18% of tokens in six months, with agent loops doing most of the work. Token share and spend share are not the same league table.

Publishers:openrouter.ai

Reality

Evidence52
Adoption74
Hype gap+18
Incentives70
Confidence50