Enterprises are handing coding agents real engineering work and choosing the model on benchmarks that basically just count passing unit tests. Those benchmarks do not see the failure every engineering team has met, code that runs and misses the point, and they say nothing about what a passed job costs. Signal65 PINNACLE now has a Coding Agent test, published today beside the PINNACLE Model Score. ➡️ A model is dropped into a codebase it has never seen, with a business brief, 74K tokens of documentation and 7,200 seconds, and asked to build the software ➡️ Grading is all code. The program it builds is run on 200 unseen samples, every answer is recomputed, and the design is checked across samples, so a generator that never varies fails the job ➡️ Two classes of work, open-ended Frontier Class jobs and fully scoped Local Class jobs, weighted equally into one PINNACLE Coding Agent Score ➡️ The best enterprise model is not necessarily the best coding model, and the rankings part ways at the top of the field ➡️ One passed open-ended job costs from $1.35 to $73.76 across the hosted frontier, failed tries included, with open-weights models priced on a rented @NVIDIA B300 node 63 configurations, 3,690 Frontier Class and 3,660 Local Class attempts, developed in close partnership with @Kamiwaza. Read the article and the results:

🚨🚨 Google is back at the top of the agentic PINNACLE leaderboard! 🚨🚨 Gemini 4 Argon leads Signal65 PINNACLE ( at its default effort, and at Google's standard $4/$20 rate a correct task costs $0.87, below the $1.08 and $1.51 of the two models it passed. ➡️ 13% fewer weighted errors than Claude Sonnet 5.5 at max effort and 35% fewer than GPT-6 Astra at max in our testing, with 100% of the multi-step enterprise jobs finished and 98.9% on the 128K retrieval corpus ➡️ 14 median rounds per job and 10,549 output tokens per correct answer, against 29 and 63,020 for Sonnet 5.5 max, which is why a job costs less on Argon at twice Sonnet's input rate ➡️ The top of the board has moved up 53% since September 5th and the price of a correct task on it is 43% lower We priced @GoogleAI Argon at the standard rate on purpose. Google's $2/$10 promotion runs through November 30th and would read 43 cents; the number that maintains through December is the one on the board.

📰Meta has a flagship agent model again. 📰 Muse Wonder 1.0 enters the top ten of the Signal65 PINNACLE board ( above Claude Opus 5.5 at both efforts, GPT-6 Sol max and Grok 4.6 in our testing, a 1.5x step over Muse Spark 1.3, and the one thing holding it off the cost frontier is the price - something @Meta can change. ➡️ 33% fewer weighted errors than Spark, in 19 median rounds against 32, with 49% fewer output tokens per correct answer ➡️ 99.6% of the multi-step enterprise jobs finished, 97.6% on the 128K retrieval corpus, fabrication 0.0%, level with the best on the board ➡️ $1.70 per correct task at Meta's $4/$20 list rate, 1.9x Spark and 5.6x GPT-6.1 Sol max, which scores higher and holds the frontier step above it Intelligence is the hard part and Meta seems to have it; the rate card is the adjustable part. At Spark's $1.25/$4.25 card the same work would cost 49 cents and sit on the frontier. Every score and every price of a correct task is live at

An @AMD Instinct MI355X node does more agentic work per dollar as the serving stack behind it improves, and the same node now serves 1.44x the MiniMax-M3 throughput it did when Signal65 PINNACLE first published it, in our testing. ➡️ AITER unified attention and piecewise graph capture on the same vLLM 0.23.1 build added 11% peak throughput and took the MI355X node from 52 to 56 agents sustained at the service floor ➡️ At 8 agents, per-agent decode went from 18.3 to 43.4 tok/s, 2.4x, on the same build ➡️ vLLM 0.30.1 with the @AMD MiniMax-M3 configuration added another 30%, 563 to 731 tok/s ➡️ 31% lower cost per output token on hardware a buyer already owns On the version-matched build the MI355X now sustains more agents at the PINNACLE floor than the NVIDIA B300. AMD engineering has been a strong partner in this work, and more MI355X results on the current vLLM cycle are coming. @AnushElangovan @roaner @AIatAMD