BenchLeader

Gemini 4 Argon vs GPT-5.6 Sol

Verdict
  • Gemini 4 Argon (high) leads on quality: 70.0 vs 67.5.
  • Gemini 4 Argon (high) is stronger in agents & tools, coding, composite, human preference, knowledge, maths, reasoning.
  • GPT-5.6 Sol (max) is stronger in long context, instruction following, multimodal.
  • Gemini 4 Argon (high) is 2.0× cheaper ($4.00 vs $8.00 per 1M blended).
MetricGemini 4 Argon (high)GPT-5.6 Sol (max)
BenchLeader Index70.067.5
Agents & tools score67.566.5
Coding score72.165.8
Composite score89.275.4
Human preference score73.2–
Knowledge score75.765.8
Long context score65.067.3
Maths score71.971.5
Reasoning score82.072.7
Instruction following score–71.0
Multimodal score–66.7
Blended price $/M$4.00$8.00
Output speed–74 tok/s
Time to first answer–85.3 s
Context window1M1.1M
GPQA Diamond–93.5%
FrontierMath Tiers 1–3–89.1%
FrontierMath Tier 4–82.9%
OTIS Mock AIME–100.0%
SimpleQA Verified–69.7%
Terminal-Bench–37.3%
OSWorld-Verified 2.0–27.3%
SciCode61.8%57.1%
WeirdML–87.0%
ProofBench–83.0%
LMArena Text1525–
LMArena Hard Prompts1551–
LMArena Coding1562–
LMArena WebDev1678–
LMArena Agent9.3–
LiveBench–81.0%
LiveBench Reasoning–91.7%
LiveBench Coding–83.9%
LiveBench Agentic Coding–56.2%
LiveBench Mathematics–96.2%
LiveBench Data Analysis–79.8%
LiveBench Language–87.7%
LiveBench Instruction Following–71.8%
AA Intelligence Index v4.3.252.647.0
IFBench–72.7%
AA-LCR79.7%84.0%
MMMU-Pro–83.4%
AA-Omniscience42.422.0
Terminal-Bench Hard–65.9%
GPQA Diamond (AA)–94.1%
Humanity's Last Exam (AA)57.1%49.5%
SciCode (AA)61.8%57.1%
τ²-Bench Telecom (AA)–85.1%
LiveCodeBench–82.6%
MMLU-Pro–89.1%
IOI100.0%91.2%
LegalBench88.3%87.0%
CorpFin–64.4%
TaxEval–74.8%
Terminal-Bench 2.1 (Vals)–85.8%
SWE-bench (Vals)–96.2%
GPQA Diamond (Vals)–95.2%
Vals Index68.958.0
PRBench Finance–50.5%
PRBench Legal–50.5%
ARC-AGI-1–96.5%
ARC-AGI-2–92.5%
ARC-AGI-3–7.8%
CritPt27.1%32.3%
GDPval-AA v2.156.3%55.6%
τ³-Banking (AA)–44.3%
ITBench SRE (AA)–56.2%
Analyst Agent (AA)–47.5%
BioMysteryBench76.3%71.1%
Code Migration68.2%52.9%
CUA-bench4.8%8.3%
CyberBench77.9%76.3%
Excel Modeling Benchmark75.2%72.3%
Finance Agent v265.4%53.8%
Harvey's Legal Agent Benchmark19.6%2.5%
Legal Research Bench54.8%48.1%
MedCode58.8%44.0%
MedScribe87.4%85.2%
MMMU-Pro (Vals)–88.8%
MortgageTax–67.3%
MysteryMechanism45.5%33.3%
ProgramBench2.5%1.5%
Public Benefits Bench69.8%66.5%
SAGE53.6%52.6%
SkillsBench–54.1%
SREBench44.3%30.5%
Tax Agent Bench76.2%68.0%
Terminal-Bench 4.0 (Vals)57.6%37.9%
Terminal-Bench Science44.3%20.0%
Time Horizon Index: KSP–23.8%
Vals Multimodal Index–72.6%
Vibe Code Bench 1-100–20.0%
Vibe Code Bench v1.191.9%80.5%
Web Search Index–43.6%
LMArena Maths1528–
LMArena Creative Writing1519–
LMArena Instruction Following1529–
LMArena Multi-turn1553–
LMArena Longer Queries1545–
Chess Puzzles–55.0%
EBR-bench–44.8%
Mystery Game Puzzles–58.0%
BALROG–60.0%
PostTrainBench–36.2%
DeepSWE v1.1–72.7%
LMCA–58.4%
DTBench–95.5%
CursorBench–41.7%
ALE-Bench–2176.9
GDP.pdf–30.7%
FrontierSWE55.0%32.2%
BTF-3–13.7%
Terminal-Bench 4.0 (AA)57.1%39.9%
Terminal-Bench 2.1 (AA)–88.0%
AutomationBench77.5%–
GDP.pdf21.8%–
AA-Omniscience: accuracy49.9%59.4%
AA-Omniscience: non-hallucination84.9%7.8%
AA-Briefcase v1.11488–

Data as of 2026-10-11. Best configuration of each model; every score links to its source on the model pages.

Gemini 4 Argon vs GPT-5.6 Sol: questions

Is Gemini 4 Argon better than GPT-5.6 Sol?
Gemini 4 Argon (high) leads on quality: 70.0 vs 67.5. The BenchLeader Index combines every independent quality benchmark; Gemini 4 Argon (high) is ahead overall as of 2026-10-11, but check the category scores for your use.
Is Gemini 4 Argon better than GPT-5.6 Sol for coding?
Gemini 4 Argon scores higher in coding (72 vs 66 on the category index, where 50 is average).
Is Gemini 4 Argon better than GPT-5.6 Sol for agentic tasks?
Gemini 4 Argon scores higher in agentic tasks (68 vs 67 on the category index, where 50 is average).
Which is cheaper, Gemini 4 Argon or GPT-5.6 Sol?
Gemini 4 Argon is cheaper: $4.00 against $8.00 per million tokens, blended at three input tokens per output token.
Which has the larger context window?
GPT-5.6 Sol accepts more context: 1.1M against 1M tokens.