AI Model Comparison

Claude Sonnet 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) vs GPT-6 Astra (max)

Compare Claude Sonnet 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) vs GPT-6 Astra (max) with benchmark results, speed, pricing, and practical workflow guidance.

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

  • Workloads that benefit from the stronger overall intelligence score
  • Longer responses where sustained output speed matters
  • Higher-volume workloads where blended token cost matters

Best For GPT-6 Astra (max)

  • Coding and agentic tasks where the benchmark edge matters
  • Latency-sensitive chat, support, and interactive product flows
  • Teams already standardized on OpenAI

Claude Sonnet 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) and GPT-6 Astra (max) are compared across intelligence, coding, math, speed, pricing, and benchmark coverage.

Claude Sonnet 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) and GPT-6 Astra (max) 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, Max Effort, Default Fallback) has the stronger overall benchmark position in Franklin AI's current dataset, with an intelligence index of 56.0 compared with 52.7 for GPT-6 Astra (max). That makes Claude Sonnet 5.5 (Adaptive Reasoning, Max 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 Anthropic Claude Sonnet 5.5 (Adaptive Reasoning, Max Effort, Default Fallback) OpenAI GPT-6 Astra (max)
Index Scores
Intelligence Index 56.0 52.7
Coding Index- 76.9
Math Index--
Benchmark Scores
GPQA- 96.1
SciCode 61.0 56.5
HLE 55.0 54.7
LCR 82.7 80.7

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, Max Effort, Default Fallback) when the work benefits from the stronger benchmark profile and the cost profile still fits the project. Choose GPT-6 Astra (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, Max Effort, Default Fallback) is the stronger benchmark pick today. GPT-6 Astra (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, Max Effort, Default Fallback) currently has the stronger overall benchmark profile, while GPT-6 Astra (max) may still be preferable depending on price, latency, coding strength, or ecosystem fit.

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