Thread

1. simonw · 1790621080 · focus · HN ↗

   Pelicans. Sonnet 5.5 has the same problem as Opus 5.5: on "max" thinking effort it burned through 128,000 thinking tokens (taking 15 minutes to do that) and ran out before it had produced the final SVG.

   <a href="https:&#x2F;&#x2F;tools.simonwillison.net&#x2F;markdown-svg-renderer?url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F1d85a9be7f3ecce26e7f1569161a0d01" rel="nofollow">https:&#x2F;&#x2F;tools.simonwillison.net&#x2F;markdown-svg-renderer?url=ht...

   Here&#x27;s how the thinking effort levels compare:

     low
     27 input, 1,623 output, thinking_tokens: 0
     1.6284
     Duration: 10138ms (10s)
     
     medium
     27 input, 1,796 output, thinking_tokens: 0
     1.7914 cents
     Duration: 11266ms (11s)
   
     high
     27 input, 2,334 output, thinking_tokens: 745
     2.3394 cents
     Duration: 17376ms (17s)
   
     xhigh
     27 input, 5,730 output, thinking_tokens: 2535
     5.7354 cents
     Duration: 41882ms (41s)
   
     max (failed to return response)
     27 input, 128,000 output, thinking_tokens: 128000
     $1.28
     Duration: 940617ms (15m 40s)
   

   Low and medium both used 0 thinking tokens.

   1. platinumrad · 1790623033 · focus · HN ↗

      The contrast between Anthropic, who seem to be training their models to output ever-increasing numbers of reasoning tokens, and Fireworks&#x27;s Ember-1, which was explicitly trained to preserve the quality of a model&#x27;s responses while cutting down on reasoning, is interesting. Claude Code also uses more many tokens per task per model than any other harness in benchmarks.

      1. [deleted] · 1790629231 · focus · HN ↗

         [deleted]

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