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@@ -14,7 +14,7 @@ configs:
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  # πŸš— Sara Open-Model Benchmark
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  > **We run a production AI sales agent. We pay a premium for GPT-5.2 to power it. So we
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- > tested 18 models on our *own* agent** β€” and **16 of them β€” open and closed β€” are statistically tied with GPT-5.2 on quality** (Tier 1), and one of them, **gpt-oss-20B**, costs **~1/60th** as much. We're paying
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  > a premium for a lead we don't have.
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  Everyone is shipping AI agents. Almost nobody knows which model to actually put *inside*
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  ## πŸ“Š What we found
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- - **Quality is a statistical tie.** 16 models β€” open and closed β€” land in one
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  top tier (Tier 1); a two-proportion z-test can't separate GPT-5.2 from the open models above
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  it (DeepSeek-V4-Flash leads by point estimate at 90.3%). At n=62 the raw
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  ranking is noise β€” **the tie *is* the finding.** And the prompt was tuned *for* GPT-5.2, so
 
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  # πŸš— Sara Open-Model Benchmark
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  > **We run a production AI sales agent. We pay a premium for GPT-5.2 to power it. So we
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+ > tested 19 models on our *own* agent** β€” and **17 of them β€” open and closed β€” are statistically tied with GPT-5.2 on quality** (Tier 1), and one of them, **gpt-oss-20B**, costs **~1/60th** as much. We're paying
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  > a premium for a lead we don't have.
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  Everyone is shipping AI agents. Almost nobody knows which model to actually put *inside*
 
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  ## πŸ“Š What we found
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+ - **Quality is a statistical tie.** 17 models β€” open and closed β€” land in one
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  top tier (Tier 1); a two-proportion z-test can't separate GPT-5.2 from the open models above
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  it (DeepSeek-V4-Flash leads by point estimate at 90.3%). At n=62 the raw
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  ranking is noise β€” **the tie *is* the finding.** And the prompt was tuned *for* GPT-5.2, so