Request a pilot
‹ PublicAI Index

The LLM benchmark aggregator.

GPT-5.5

OpenAI

Strongest in Data analysis (#2 of 57), weakest in Toxicity avoidance (#217 of 272). Above par in 28 of 30 scopes. Among the models it meets almost everywhere, it finishes behind Claude Fable 5.1 and Claude Fable 5 and ahead of Gemini 3.8 Flash and Muse Spark 1.1.

§ 1 · Profile

What it is good at

Bars run from 50 — the average of the models each source lists — so right of the line is above par. The middle column is the gap to whoever leads that scope.

Core abilities63.7#4/204
Data analysis63.8#2/57
General intelligence67.2#5/204
Language65.3#7/57
Instruction following54.9#23/57
Reasoning61.6#7/178
Mathematics59.8#9/140
Science61.9#10/122
Reasoning62.4#12/140
Human preference64.8#18/342
Human preference64.8#18/342
Coding57.6#19/165
Code generation59.6#9/77
Agentic coding57#28/157
Knowledge57.2#24/138
Academic knowledge58.6#21/123
Professional56.3#25/168
IT operations42.2#7/11
Finance58.5#13/151
Medical58.5#18/140
Legal57.1#26/151
Agents57.7#46/268
Knowledge work60#35/178
Safety55.2#51/337
Secure code63.9#19/274
Fairness60.5#40/300
Factual grounding51.7#45/101
Harm refusal54.4#81/300
Jailbreak resistance54.2#127/272
Toxicity avoidance47.6#217/272

§ 2 · Head to head

What it beats, and what beats it

The same models turn up scope after scope. Counted once: where both were placed, who finished higher. Bars run right for GPT-5.5, left for the other.

Grok 4 Fastwon 18 · lost 0
GLM-5won 18 · lost 0
Grok Build 0.1won 19 · lost 0
Mistral Large 3won 20 · lost 0
Qwen3.6 Pluswon 22 · lost 0
Gemini 3.5 Flash Litewon 22 · lost 0
Grok 4.3won 28 · lost 0
Kimi K2.6won 28 · lost 1
DeepSeek V3won 25 · lost 1
Gemini 2.5 Flashwon 24 · lost 1
Kimi K2.5won 24 · lost 1
GPT-4.1 Nanowon 23 · lost 1
GPT-4.1 Miniwon 23 · lost 1
Command Awon 22 · lost 1
GPT-5.4 Miniwon 22 · lost 1
Mimo V2.5 Prowon 22 · lost 1
Grok 4.1 Fastwon 21 · lost 1
GLM-5.1won 21 · lost 1
GPT OSS 20Bwon 20 · lost 1
Grok 3won 20 · lost 1
Kimi K2 Thinkingwon 19 · lost 1
Kimi K2.7 Codewon 18 · lost 1
GLM-4.5won 18 · lost 1
Gemini 3.1 Flash Litewon 18 · lost 1
Ling 3.0 Flashwon 17 · lost 1
Gemini 2.5 Flash Litewon 17 · lost 1
DeepSeek V4 Flashwon 26 · lost 2
MiniMax M3won 26 · lost 2
Claude Sonnet 4.6won 25 · lost 2
Gemini 2.5 Prowon 24 · lost 2
DeepSeek R1won 23 · lost 2
DeepSeek V3.2won 23 · lost 2
GPT-4o Miniwon 22 · lost 2
GPT-5.4 Nanowon 22 · lost 2
Kimi K2won 22 · lost 2
Qwen3 235B A22B Instructwon 22 · lost 2
GLM-4.6won 22 · lost 2
Gemini 2.0 Flashwon 20 · lost 2
Gemma 4 31Bwon 20 · lost 2
Mistral Smallwon 18 · lost 2
GLM-5.2won 26 · lost 3
Gemini 1.5 Prowon 16 · lost 2
GPT OSS 120Bwon 22 · lost 3
GPT-6 Lunawon 22 · lost 3
Llama 4 Scout Instructwon 20 · lost 3
Grok 4won 20 · lost 3
Claude Opus 4.5won 20 · lost 3
Qwen3.8 27Bwon 19 · lost 3
GPT-5.6 Lunawon 24 · lost 4
Qwen3.7 Maxwon 24 · lost 4
Claude Sonnet 4won 22 · lost 4
Claude Haiku 4.5won 21 · lost 4
Mistral Largewon 15 · lost 3
GPT-5.4won 24 · lost 5
O3 Miniwon 19 · lost 4
GLM-5.3 Flashwon 23 · lost 5
Gemini 3.5 Flashwon 23 · lost 5
GPT-4owon 18 · lost 4
DeepSeek V4.1 Flashwon 18 · lost 4
Claude 3.7 Sonnetwon 17 · lost 4
O1won 16 · lost 4
Inklingwon 22 · lost 6
GPT-6 Solwon 20 · lost 6
Gemini 3 Prowon 16 · lost 5
O4 Miniwon 19 · lost 6
Claude Sonnet 4.5won 19 · lost 6
DeepSeek V4 Prowon 22 · lost 7
Grok 4.5won 22 · lost 7
Claude 3.5 Haikuwon 15 · lost 5
Claude Opus 4won 18 · lost 6
Gemini 3.6 Flashwon 21 · lost 7
GPT-5 Nanowon 16 · lost 6
GLM-5.3won 16 · lost 6
GPT-5 Miniwon 18 · lost 7
MiniMax M2.7won 15 · lost 6
GPT-5.2won 20 · lost 8
Claude Sonnet 5won 20 · lost 8
GPT-5.6 Terrawon 20 · lost 8
GPT-4.1won 17 · lost 7
O3won 17 · lost 7
Gemini 3.1 Prowon 17 · lost 7
Claude 3.5 Sonnetwon 14 · lost 6
Claude Opus 4.6won 17 · lost 8
Qwen3.8 Maxwon 19 · lost 9
GPT-5.1won 15 · lost 8
GPT-5won 16 · lost 9
Mimo V2.6 Prowon 11 · lost 8
Gemini 3.7 Flashwon 13 · lost 10
Claude Opus 4.7won 16 · lost 13
Muse Spark 1.1won 15 · lost 13
Gemini 3.8 Flashwon 15 · lost 13
Kimi K3won 11 · lost 11
Grok 4.7won 12 · lost 13
Grok 4.6won 13 · lost 15
Claude Opus 4.8won 13 · lost 15
GPT-5.6 Solwon 13 · lost 17
Muse Spark 1.2won 9 · lost 12
Muse Spark 1.3won 7 · lost 11
GPT-6 Astrawon 7 · lost 15
Claude Opus 5won 7 · lost 22
Claude Opus 5.5won 6 · lost 20
Claude Fable 5won 3 · lost 19
Claude Fable 5.1won 1 · lost 22

§ 3 · Sources

Where the numbers come from

10 publications, 37 figures. Every one links to the page it was read from.

LMArena Text ↗1 measureread 2026-09-26
LMArena Text 1481
GDPval-AA ↗1 measureread 2026-09-26
GDPval-AA 1336
AA-Briefcase ↗1 measureread 2026-09-26
AA-Briefcase 1137
ARC-AGI-2 ↗1 measureread 2026-09-26
ARC-AGI-2 85%
LiveBench ↗8 measuresread 2026-09-26
LiveBench 80.2LiveBench · Reasoning 89.7LiveBench · Coding 82.1LiveBench · Agentic Coding 54LiveBench · Mathematics 95.9LiveBench · Data Analysis 81.6LiveBench · Language 87.4LiveBench · Instruction Following 70.7
Kagi LLM Benchmark ↗1 measureread 2026-09-26
Kagi LLM Benchmark 88.8%
SimpleBench ↗1 measureread 2026-09-26
SimpleBench 69%
Vals.ai ↗16 measuresread 2026-09-26
Vals · Legal Research Bench 40.38%Vals · CaseLaw 66.24%Vals · LegalBench 86.52%Vals · Harvey Legal Agent Benchmark 3.75%Vals · Finance Agent 51.76%Vals · CorpFin 68.42%Vals · TaxEval 74.98%Vals · MortgageTax 68.76%Vals · MedCode 49.1%Vals · MedScribe 86.87%Vals · SRE Bench 3.82%Vals · SWE-bench Verified 82.6% (Mini-SWE-agent)Vals · Vibe Code Bench 69.85% (OpenHands)Vals · Code Migration 45.16%Vals · GPQA Diamond 93.18%Vals · MMLU Pro 88.14%
Enkrypt AI Safety Leaderboard ↗6 measuresread 2026-09-26
Enkrypt · Jailbreak risk 9.2%Enkrypt · Harmful content risk 0.6%Enkrypt · CBRN risk 20.7%Enkrypt · Toxicity risk 8%Enkrypt · Bias risk 61.5%Enkrypt · Insecure code risk 5.3%
Vectara · Factual consistency 90.7%

Badge

PublicAI Index badge for GPT-5.5[![PublicAI Index](https://publicai.io/model-index/badge?model=gpt-5-5)](https://publicai.io/model-index/m/gpt-5-5)