AI Model Comparison

GPT-6.1 Sol (max) vs Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)

Compare GPT-6.1 Sol (max) vs Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) with benchmark results, speed, pricing, and practical workflow guidance.

Best For GPT-6.1 Sol (max)

  • Teams already standardized on OpenAI
  • Use cases where its strongest benchmark rows map to the workload
  • Readers who want the best fit after checking the full table

Best For Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)

  • Workloads that benefit from the stronger overall intelligence score
  • Latency-sensitive chat, support, and interactive product flows
  • Longer responses where sustained output speed matters

GPT-6.1 Sol (max) and Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) are compared across intelligence, coding, math, speed, pricing, and benchmark coverage.

GPT-6.1 Sol (max) and Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) serve similar evaluation needs, but the useful difference is not the brand name. It is how the benchmark profile, latency, and token pricing line up with the work a reader actually wants to run.

What the benchmarks show

Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) has the stronger overall benchmark position in Franklin AI's current dataset, with an intelligence index of 51.9 compared with 51.8 for GPT-6.1 Sol (max). That makes Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) the clearer default when a workflow depends on the highest available reasoning score. The rest of the table still matters, because coding, math, and individual benchmark rows can point to a different choice for narrow use cases.

Benchmark table

Side-by-side scores, speed, and pricing for the selected models.

Metric OpenAI GPT-6.1 Sol (max) Anthropic Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback)
Index Scores
Intelligence Index 51.8 51.9
Coding Index--
Math Index--
Benchmark Scores
SciCode 54.2 57.3
HLE 52.9 50.0
LCR 83.0 79.7

Speed and cost

Pricing and responsiveness can change the decision even when one model leads on the headline index. A model with a lower blended token cost may be easier to use at scale, while a model with faster first-token response can feel better in interactive products. The benchmark table below keeps those tradeoffs visible instead of reducing the comparison to one score.

Which model fits which workflow

Choose Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) when the work benefits from the stronger benchmark profile and the cost profile still fits the project. Choose GPT-6.1 Sol (max) when its provider ecosystem, latency, or pricing better matches the way the model will be used. The best choice is the one whose advantage appears in the rows that map to the actual workload.

Decision takeaway

For most readers, Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) is the stronger benchmark pick today. GPT-6.1 Sol (max) remains worth considering when budget, speed, provider preference, or a specific benchmark row matters more than the overall intelligence index.

Verdict

Claude Sonnet 5.5 (Adaptive Reasoning, Xhigh Effort, Default Fallback) currently has the stronger overall benchmark profile, while GPT-6.1 Sol (max) may still be preferable depending on price, latency, coding strength, or ecosystem fit.

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