BenchLeader

Gemini 3.8 Flash vs GPT-6 Sol

Verdict
  • GPT-6 Sol (max) leads on quality: 67.7 vs 64.3.
  • Gemini 3.8 Flash (medium) is stronger in agents & tools, coding, knowledge, long context, multimodal.
  • GPT-6 Sol (max) is stronger in composite, reasoning.
  • Gemini 3.8 Flash (medium) is 2.7× cheaper ($1.50 vs $4.00 per 1M blended).
  • GPT-6 Sol (max) streams 1.6× faster (126 vs 77 tokens per second).
MetricGemini 3.8 Flash (medium)GPT-6 Sol (max)
BenchLeader Index64.367.7
Agents & tools score74.8
Coding score60.1
Composite score76.986.2
Knowledge score76.175.4
Long context score67.967.7
Multimodal score68.167.1
Reasoning score63.495.0
Blended price $/M$1.50$4.00
Output speed77 tok/s126 tok/s
Time to first answer2.4 s107.2 s
Context window1.0M1.1M
SciCode54.4%
FrontierCode41.2%
AA Intelligence Index39.847.5
AA-LCR84.0%83.7%
MMMU-Pro84.2%83.3%
AA-Omniscience28.627.1
GPQA Diamond (AA)93.5%
Humanity's Last Exam (AA)42.1%47.9%
SciCode (AA)55.1%57.6%
CritPt12.3%30.9%
GDPval (AA)45.4%49.4%
τ²-Bench Banking (AA)45.8%
DeepSWE71.0%
LMCA52.9%
DTBench94.9%
CursorBench37.3%
GDP.pdf23.4%
Terminal-Bench 4.0 (AA)19.7%43.9%
Terminal-Bench 2.1 (AA)83.9%
AutomationBench61.6%
GDP.pdf24.8%
MLCR16.1%
AA-Omniscience: accuracy53.0%54.5%
AA-Omniscience: non-hallucination48.1%39.9%
AA-Briefcase1483

Data as of 2026-09-23. Best configuration of each model; every score links to its source on the model pages.

Gemini 3.8 Flash vs GPT-6 Sol: questions

Is Gemini 3.8 Flash better than GPT-6 Sol?
GPT-6 Sol (max) leads on quality: 67.7 vs 64.3. The BenchLeader Index combines every independent quality benchmark; GPT-6 Sol (max) is ahead overall as of 2026-09-23, but check the category scores for your use.
Which is cheaper, Gemini 3.8 Flash or GPT-6 Sol?
Gemini 3.8 Flash is cheaper: $1.50 against $4.00 per million tokens, blended at three input tokens per output token.
Which is faster, Gemini 3.8 Flash or GPT-6 Sol?
GPT-6 Sol streams faster: 126 against 77 output tokens per second.
Which has the larger context window?
GPT-6 Sol accepts more context: 1.1M against 1.0M tokens.