BenchLeader

Gemini 4 Argon vs GPT-5.5

Verdict
  • Gemini 4 Argon (high) leads on quality: 70.0 vs 65.9.
  • Gemini 4 Argon (high) is stronger in agents & tools, coding, composite, human preference, knowledge, maths, reasoning.
  • GPT-5.5 (xhigh) is stronger in long context, instruction following, multimodal.
  • Gemini 4 Argon (high) is 2.8× cheaper ($4.00 vs $11.25 per 1M blended).
MetricGemini 4 Argon (high)GPT-5.5 (xhigh)
BenchLeader Index70.065.9
Agents & tools score67.561.5
Coding score72.161.4
Composite score89.269.2
Human preference score73.2–
Knowledge score75.765.5
Long context score65.067.5
Maths score71.969.9
Reasoning score82.075.8
Instruction following score–73.8
Multimodal score–63.1
Blended price $/M$4.00$11.25
Output speed–88 tok/s
Time to first answer–39.0 s
Context window1M1.1M
FrontierMath Tiers 1–3–85.3%
FrontierMath Tier 4–72.5%
SimpleQA Verified–63.0%
OSWorld-Verified 2.0–13.0%
SciCode61.8%56.1%
WeirdML–84.9%
ProofBench–50.0%
GSO-Bench–40.2%
LMArena Text1525–
LMArena Hard Prompts1551–
LMArena Coding1562–
LMArena WebDev16781513
LMArena Agent9.34.4
LiveBench–80.2%
LiveBench Reasoning–89.7%
LiveBench Coding–82.2%
LiveBench Agentic Coding–54.0%
LiveBench Mathematics–95.9%
LiveBench Data Analysis–81.6%
LiveBench Language–87.4%
LiveBench Instruction Following–70.7%
AA Intelligence Index v4.3.252.638.4
IFBench–75.8%
AA-LCR79.7%84.3%
MMMU-Pro–79.9%
AA-Omniscience42.420.5
Terminal-Bench Hard–60.6%
GPQA Diamond (AA)–93.5%
Humanity's Last Exam (AA)57.1%45.8%
SciCode (AA)61.8%55.8%
τ²-Bench Telecom (AA)–93.9%
LiveCodeBench–85.3%
MMLU-Pro–88.1%
IOI100.0%–
LegalBench88.3%86.5%
CorpFin–68.4%
TaxEval–75.0%
SWE-bench (Vals)–82.6%
GPQA Diamond (Vals)–93.2%
Vals Index68.9–
AIME 2026–100.0%
HMMT February 2026–98.5%
MathArena Apex–80.2%
MCP Atlas–75.3%
ARC-AGI-1–95.0%
ARC-AGI-2–85.0%
CritPt27.1%27.1%
GDPval-AA v2.156.3%42.7%
τ³-Banking (AA)–39.0%
ITBench SRE (AA)–45.8%
Analyst Agent (AA)–50.0%
APEX-Agents (AA)–37.7%
BioMysteryBench76.3%–
CaseLaw v2–66.2%
Code Migration68.2%45.2%
CUA-bench4.8%–
CyberBench77.9%–
Excel Modeling Benchmark75.2%64.5%
Finance Agent v265.4%51.8%
Harvey's Legal Agent Benchmark19.6%3.8%
Legal Research Bench54.8%40.4%
MedCode58.8%49.1%
MedScribe87.4%86.9%
MMMU-Pro (Vals)–88.3%
MortgageTax–68.8%
MysteryMechanism45.5%–
ProgramBench2.5%0.5%
Public Benefits Bench69.8%60.9%
SAGE53.6%51.5%
SkillsBench–62.2%
SREBench44.3%3.8%
Tax Agent Bench76.2%60.5%
Terminal-Bench 4.0 (Vals)57.6%–
Terminal-Bench Science44.3%–
Time Horizon Index: KSP–8.3%
Vals Multimodal Index–68.1%
Vibe Code Bench v1.191.9%69.8%
FORTRESS–16.3%
SWE Atlas: Codebase QnA–45.4%
SWE Atlas: Refactoring–44.8%
SWE Atlas: Test Writing–42.6%
LMArena Maths1528–
LMArena Creative Writing1519–
LMArena Instruction Following1529–
LMArena Multi-turn1553–
LMArena Longer Queries1545–
EBR-bench–34.3%
Mystery Game Puzzles–56.0%
PostTrainBench–27.2%
DeepSWE v1.1–67.0%
LMCA–54.3%
DTBench–96.0%
ALE-Bench–1943.0
GDP.pdf–26.0%
FrontierSWE55.0%–
Terminal-Bench 4.0 (AA)57.1%14.7%
Terminal-Bench 2.1 (AA)–84.3%
AutomationBench77.5%–
GDP.pdf21.8%–
AA-Omniscience: accuracy49.9%58.0%
AA-Omniscience: non-hallucination84.9%11.0%
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-5.5: questions

Is Gemini 4 Argon better than GPT-5.5?
Gemini 4 Argon (high) leads on quality: 70.0 vs 65.9. 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-5.5 for coding?
Gemini 4 Argon scores higher in coding (72 vs 61 on the category index, where 50 is average).
Is Gemini 4 Argon better than GPT-5.5 for agentic tasks?
Gemini 4 Argon scores higher in agentic tasks (68 vs 62 on the category index, where 50 is average).
Which is cheaper, Gemini 4 Argon or GPT-5.5?
Gemini 4 Argon is cheaper: $4.00 against $11.25 per million tokens, blended at three input tokens per output token.
Which has the larger context window?
GPT-5.5 accepts more context: 1.1M against 1M tokens.