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MiniMax M3.1 Flash ships at $0.15/$0.60 per million, weights Oct 23

MiniMax released MiniMax M3.1 Flash at $0.15 per million input tokens and $0.60 per million output tokens, claiming the model runs at about a third of M3's cost and 3x its speed while scoring 73.7% on DeepSWE v1.1 with the same 1M context window. MiniMax said open weights for M3.1 Flash will be published on Oct 23, letting teams verify the vendor's numbers themselves. The cheaper Flash tier targets coding agents that need long context at lower cost.

read2 min views3 publishedOct 20, 2026

MiniMax claims it runs at about a third of M3's cost and 3x its speed while scoring 73.7% on DeepSWE v1.1, so coding agents get a cheaper Flash tier with the same 1M context. Open weights on Oct 23 let teams check the vendor's numbers themselves. Read: MiniMax claims it runs at about a third of M3's cost and 3x its speed while scoring 73.7% on DeepSWE v1.1, so coding agents get a cheaper Flash tier with the same 1M context. Open weights on Oct 23 let teams check the vendor's numbers themselves. Watch: Dashboards stay connected to data platforms and CRMs, so a report Claude builds keeps updating instead of going stale as a one-off chart. Motion writes animations as editable code rather than video, and Docs, Slides and Design leave beta on every plan, Free included. Read: Cost per review had doubled before the fix. Reviewer code changes plus model and effort tuning brought it down and cut median review time from over four minutes to 1m32s, showing that agent spend came from how the system was built, not only from model prices. Try: Apps that traced MCP requests sent server URLs, tokens and headers into OpenTelemetry, so those credentials stay in historical traces after the upgrade. A second advisory let the Vercel AI and AG-UI adapters run tools with client-chosen arguments. Read: Notion claims its Auto model routing reaches near-frontier results at about 90% lower cost. Business and Enterprise seats also move to a Standard/Select credit system with admin controls, which changes how teams budget and govern agent work inside Notion. Read: Trainium previously relied on XLA lazy-tensor workflows. The new backend supports eager mode and torch.compile, so Hugging Face models move over with a device setting change, widening accelerator choices for PyTorch training and inference. Read: A single all-knowing data-pipeline agent began hallucinating in production. Moving domain reasoning to 14 specialist agents behind a router that only sequences work and runs reliability checks fixed what bigger context and better prompts had not.

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