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Kimi K3.1 Pro challenges Opus as Claude Code users revolt on SuperPower

AI CodingSunday, October 25, 2026· 2 videos

Briefing

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Kimi K3.1 Pro jumps tiers

Kimi K3.1 Pro emerged as a serious new contender in open-weight models, posting benchmark results that place it near Opus-class systems. Reported comparisons show roughly a 10-point gain over earlier Kimi, GLM, and DeepSeek generations. In aggregate charts, it was described as beating several mainstream rivals while trailing only the strongest Opus variants in some categories. The result is a notable reshuffling at the high end of deployable models for coding-heavy use.

Kimi closes on Opus coding

The model's strongest showing came on coding and agentic evaluations, where it repeatedly outperformed Astra and DeepSeek. On Terminal Bench, SWE Marathon, and OS World, Kimi K3.1 Pro was positioned close to Fable and Opus in several comparisons. Those tests matter because they emphasize real task completion, code execution, and operating-system style control rather than pure chat fluency. That profile makes the release especially relevant for tool builders and engineering teams.

OpenRouter pricing undercuts Opus

Pricing was a central part of the pitch. On OpenRouter, the cited rate for Kimi K3.1 Pro was $2 per million input tokens and $10 per million output tokens, roughly half the cost of Opus in the comparison referenced. That combination of benchmark strength and lower inference pricing could broaden adoption among cost-sensitive developers. Subscription tiers were also cited, including $15 Plus and $31 Pro options.

Open-weight economics get sharper

The larger significance is strategic rather than purely technical. If an open-weight model can approach premium closed systems on coding and agent tasks at materially lower cost, it changes procurement decisions across startups and enterprises. Teams that previously paid for top-tier proprietary models may now test whether a cheaper, more controllable stack is good enough. That intensifies pressure on premium vendors to justify both performance gaps and pricing.

Claude Code backlash hits SuperPower

A separate flashpoint centered on Claude Code, where advanced users sharply criticized SuperPower as bloated workflow overhead. The complaint is not that structured prompting never helps, but that modern frontier models often handle the same tasks directly with less ceremony. Critics argue the framework inserts extra steps, more tokens, and slower iteration while offering limited quality gains. In that reading, the product optimizes for process theater more than developer throughput.

BMAD faces the same critique

BMAD drew similar criticism, with detractors arguing that it packages a large amount of instruction scaffolding rather than new capability. Reported figures cited 31 skills, 274 Markdown files, and more than 12,000 lines of prompt material. Opponents say that much context can inflate token bills and distract models from the immediate coding objective. The core editorial question is whether elaborate prompt frameworks still make sense as base models improve.

Big adoption, disputed utility

The backlash is notable because both projects show substantial reach. SuperPower was associated with roughly 300,000 GitHub stars and about 1 million installations in the Claude Code ecosystem, while BMAD was cited at around 54,000 stars. Those numbers suggest powerful distribution and branding, but they do not establish superior outcomes. Critics say social proof has outrun evidence of measurable productivity gains.

AI coding splits on methodology

Taken together, the two storylines point to a broader divide in AI-assisted software development. On one side, stronger base models such as Kimi K3.1 Pro are reducing the need for heavy prompt choreography by handling harder coding tasks natively. On the other, framework-driven systems like SuperPower and BMAD still promise consistency, structure, and repeatability. The market is increasingly testing whether raw model capability now beats elaborate prompt architecture on speed, cost, and code quality.

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