Commenters focused on how DeepSeek v4.1 Flash 1017 compares on cost, speed, size and practicality. One commenter noted GPT 6.1 Sol (medium) was cheaper and faster per task than DeepSeek at max effort and argued that Artificial Analysis should report results at more realistic reasoning-effort settings, since using max effort is unrealistic for most users and can consume lots of time and tokens for only small gains. Another commenter highlighted that DeepSeek’s 536+16B (passive+active) configuration is surprisingly close in capability to the larger Mimo v2.6 Pro (978+42), praised its speed, and suggested targeted fine‑tuning to reduce verbosity and token use.
Participants also debated engineering trade‑offs and risks. One commenter celebrated rapid TPS and ongoing innovation (noting Nvidia’s Nemotron 120B as an example) and said having diverse design choices and open weights benefits users. Another warned that very high TPS with a “braindead” model is risky: giving such models browser access could quickly trigger bot‑detection systems. Opinion divides around whether teams should prioritize raw throughput and size efficiency or invest more in token efficiency, fine‑tuning and safer, less error‑prone behavior.
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