Haiku 5.5
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- ChatRep switches chat and voice workloads to Terra 6.1
ChatRep founder u/chatrep said the company replaced Sol 6.1 and Sonnet 5.5 for chat with Terra 6.1 using low reasoning and balanced output, reporting better results and lower cost in its own testing. For voice, the founder reported that Terra 6.1 with reasoning disabled and concise output performed significantly better than Luna 6, although Luna 6 remained cheaper. The author described the comparison as non-scientific and limited to fairly simple RAG-style database interactions with low output. TTS and STT models were covered by separate tests and were not included in this comparison.
r/OpenAI · 🔥 0 - Reddit benchmark compares GPT-6.1 Sol and Terra on algorithmic problems
A Reddit benchmark post compared Sol 6.1, Terra 6.1, Opus 5.5, Haiku 5.5, Luna 5.6, and Luna 6.0 on four hard algorithmic problems, recording solve time, runtime, and memory usage. The results reported Sol 6.1 as the fastest on three problems, while Terra 6.1 at maximum effort led problem 2271H2; on 2273F, Terra's high effort solved faster than maximum effort, but maximum effort produced a faster, more memory-efficient solution. A later Reddit commenter described Terra's medium and high modes as frontier-level performance without providing independent measurements.
r/OpenAI · 🔥 0 - Reddit test estimates $20 coding capacity for Sonnet, Haiku, Opus and Codex
Reddit user /u/Sufficient-Storage87 ran the same eight-file WeasyPrint feature across Claude Pro and ChatGPT Plus, reading each tool's usage meter before and after two to four runs per setup. Based on the estimated share of each five-hour limit consumed per task, the user calculated about 33 Sonnet 5.5, 20 Haiku 5.5, 18 Codex Sol 6.1 and 6 Opus 5.5 tasks per window. The author attributes Haiku's lower result to more time spent revisiting the code and said weekly-limit consumption was roughly 1% for Opus and 0.5% for Sonnet and Codex.
r/ClaudeAI · 🔥 0 - YouMind's 'writing taste' benchmark puts Opus 5.5 first
YouMind published an internal benchmark it calls "文感力" (writing taste) that scores how closely a model's choices in a specific context match those of an experienced author, rather than whether it merely meets a brief. The method takes recent well-received articles by known authors, removes a paragraph, turns the reasons the original works into scoring criteria and builds wrong answers that fail individual criteria, then tests models on both picking the original paragraph and writing the removed one from scattered and decoy material. YouMind says Opus 5.5 ranked first, Gemini 4 Argon was the most reliable reader and Gemini 3.8 Flash offered the best value, and that Opus 5.5 and GPT-6 Luna, previously near-tied in its tests, differed by 42 points; it also reports every model wrote longer than the original (median 1.6x) and that 58% of rewrites broke at least one of the author's trade-offs even on articles the model had read correctly. These are vendor-reported internal results without independent replication.
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