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The LLM benchmark aggregator.

GPT-5.4

OpenAI

Strongest in Mathematics (#10 of 140), weakest in Fairness (#217 of 300). Above par in 28 of 29 scopes. Among the models it meets almost everywhere, it finishes behind Claude Fable 5 and Claude Fable 5.1 and ahead of GPT-5.6 Terra and Gemini 3.1 Pro.

§ 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.

Reasoning59.6#14/178
Mathematics59.7#10/140
Science60.9#21/122
Reasoning58.9#25/140
Human preference64.2#28/342
Human preference64.2#28/342
Core abilities56.7#29/204
Data analysis59.9#11/57
Language56.2#21/57
Instruction following54#24/57
General intelligence56.8#48/204
Knowledge56.5#30/138
Academic knowledge57.8#27/123
Coding53.4#44/165
Code generation50.2#44/77
Agentic coding54.3#45/157
Professional53.8#45/168
Finance57.1#28/151
Legal52.1#62/151
Medical52.6#64/140
Safety55.3#47/337
Secure code63.7#23/274
Factual grounding57.5#31/101
Harm refusal56#48/300
Jailbreak resistance57.7#72/272
Toxicity avoidance55.1#103/272
Fairness44#217/300
Agents55.9#57/268
Knowledge work58.8#38/178

§ 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-5.4, left for the other.

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

§ 3 · Sources

Where the numbers come from

8 publications, 34 figures. Every one links to the page it was read from.

LMArena Text ↗1 measureread 2026-09-26
LMArena Text 1475
GDPval-AA ↗1 measureread 2026-09-26
GDPval-AA 1233
ARC-AGI-2 ↗1 measureread 2026-09-26
ARC-AGI-2 74%
LiveBench ↗8 measuresread 2026-09-26
LiveBench 78LiveBench · Reasoning 88.1LiveBench · Coding 77.5LiveBench · Agentic Coding 53.8LiveBench · Mathematics 94.1LiveBench · Data Analysis 79.3LiveBench · Language 82.6LiveBench · Instruction Following 70.2
Kagi LLM Benchmark ↗1 measureread 2026-09-26
Kagi LLM Benchmark 63.8%
Vals.ai ↗15 measuresread 2026-09-26
Vals · CaseLaw 63.77%Vals · LegalBench 86.04%Vals · Harvey Legal Agent Benchmark 0%Vals · CorpFin 65.27%Vals · TaxEval 73.96%Vals · MortgageTax 68.32%Vals · MedQA 96.09%Vals · MedCode 41.29%Vals · MedScribe 77.55%Vals · SWE-bench Verified 78.2% (Mini-SWE-agent)Vals · Vibe Code Bench 67.42% (OpenHands)Vals · Code Migration 34.98%Vals · GPQA Diamond 91.67%Vals · MMLU Pro 87.48%Vals · AIME 96.67%
Enkrypt AI Safety Leaderboard ↗6 measuresread 2026-09-26
Enkrypt · Jailbreak risk 6.3%Enkrypt · Harmful content risk 0%Enkrypt · CBRN risk 16.7%Enkrypt · Toxicity risk 2.7%Enkrypt · Bias risk 87.1%Enkrypt · Insecure code risk 5.8%
Vectara · Factual consistency 93%

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