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GPT-5.4

OpenAI

Strongest in Reasoning (#15 of 195, on 3 of its 5 boards), weakest in Fairness (#212 of 292). Above par in 27 of 28 scopes. Among the models it meets almost everywhere, it finishes behind Claude Fable 5 and Claude Fable 5.1 and ahead of Gemini 3.8 Flash and MiMo V2.6 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.

Reasoning59.1#15/195
Mathematics59.2#15/152
Science60.9#21/122
Reasoning56.9#40/147
Professional55#30/194
Legal57.2#24/173
Finance57.1#29/162
Medical52.3#73/149
Human preference63.9#30/354
Human preference63.9#30/354
Core abilities54.9#46/199
Language56#23/62
Instruction following53.9#27/62
General intelligence55.4#48/178
Safety55.3#47/329
Secure code63.4#24/270
Factual grounding57.5#32/104
Harm refusal56.1#48/292
Jailbreak resistance57.8#70/268
Toxicity avoidance55.1#102/268
Fairness44#212/292
Coding53#60/179
Agentic coding53.1#57/173
Knowledge53#64/197
Academic knowledge57.8#27/123
Document parsing52.3#42/83
Agents53.5#74/298
Knowledge work57#57/208

§ 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.

Mistral Medium 3.5won 17 · lost 0
Inkling Smallwon 18 · lost 0
GLM-4.7won 18 · lost 0
Mistral Large 3won 20 · lost 0
Gemini 3.5 Flash-Litewon 21 · lost 0
Command Awon 23 · lost 0
Grok 4.3won 23 · lost 0
GPT-4.1 nanowon 23 · lost 1
GPT-4.1 miniwon 23 · lost 1
Gemini 2.0 Flashwon 21 · lost 1
Grok 4.1 Fastwon 21 · lost 1
GLM-5.1won 21 · lost 1
GPT-5.4 miniwon 20 · lost 1
Llama 3.3 70B Instructwon 19 · lost 1
Ling 3.0 Flashwon 17 · lost 1
Gemini 1.5 Prowon 17 · lost 1
MiniMax M2.5won 17 · lost 1
Grok 4 Fastwon 17 · lost 1
Magistral Small 1.2won 16 · lost 1
Command R+won 16 · lost 1
DeepSeek V3won 23 · lost 2
GLM-4.6won 22 · lost 2
Llama 4 Scout Instructwon 21 · lost 2
GPT-5.4 nanowon 21 · lost 2
Grok 3won 21 · lost 2
Gemma 4 31Bwon 20 · lost 2
GPT OSS 20Bwon 19 · lost 2
MiMo V2.5 Prowon 19 · lost 2
Gemini 3.1 Flash-Litewon 18 · lost 2
GLM-4.5won 17 · lost 2
Gemini 2.5 Flash-Litewon 16 · lost 2
Qwen3.6 Pluswon 16 · lost 2
DeepSeek V3.2won 22 · lost 3
Kimi K2.5won 21 · lost 3
GPT-4o miniwon 20 · lost 3
Kimi K2 Thinkingwon 17 · lost 3
Grok 3 Miniwon 16 · lost 3
GPT OSS 120Bwon 21 · lost 4
Kimi K2.6won 21 · lost 4
GLM-5won 15 · lost 3
Gemini 2.5 Flashwon 20 · lost 4
Gemini 2.5 Prowon 20 · lost 4
Mistral Large 4won 19 · lost 4
Qwen3.8 27Bwon 17 · lost 4
MiniMax M3won 21 · lost 5
DeepSeek V4 Flashwon 21 · lost 5
MiMo V2.6 Flashwon 16 · lost 4
GPT-6 Lunawon 20 · lost 5
Qwen3.7 Maxwon 20 · lost 5
Claude Haiku 4.5won 19 · lost 5
Kimi K2won 19 · lost 5
Qwen3 235B A22B Instructwon 19 · lost 5
GLM-5.2won 18 · lost 5
GPT-4owon 18 · lost 5
Grok 4won 18 · lost 5
Gemini 3 Flashwon 13 · lost 4
Claude 3.5 Haikuwon 15 · lost 5
DeepSeek V4 Prowon 20 · lost 7
GLM-5.3 Flashwon 20 · lost 7
Claude Haiku 5.5won 14 · lost 5
Inklingwon 19 · lost 7
GPT-5 nanowon 16 · lost 6
MiniMax M2.7won 16 · lost 6
Claude Sonnet 4won 18 · lost 7
Claude Opus 4.5won 15 · lost 6
DeepSeek R1won 17 · lost 7
Claude Sonnet 4.6won 17 · lost 7
GPT-5.1won 16 · lost 7
Claude 3.5 Sonnetwon 13 · lost 6
O3 Miniwon 15 · lost 7
Claude Sonnet 4.5won 17 · lost 8
Claude 3.7 Sonnetwon 14 · lost 7
O4 Miniwon 16 · lost 8
GPT-4.1won 16 · lost 8
GPT-5.6 Lunawon 17 · lost 10
GPT-5 miniwon 16 · lost 10
Claude Opus 4won 14 · lost 9
O3won 14 · lost 9
O1won 12 · lost 8
GLM-5.3won 12 · lost 8
GPT-5won 15 · lost 10
GPT-5.2won 15 · lost 11
Claude Sonnet 5won 15 · lost 11
DeepSeek V4.1 Flashwon 12 · lost 9
GPT-5.6 Terrawon 15 · lost 12
Gemini 3.6 Flashwon 14 · lost 12
Qwen3.8 Maxwon 14 · lost 12
MiMo V2.6 Prowon 11 · lost 10
Gemini 3.8 Flashwon 14 · lost 13
Gemini 3.1 Prowon 11 · lost 11
Claude Opus 4.6won 12 · lost 12
Grok 4.5won 13 · lost 13
GPT-6 Solwon 12 · lost 13
Gemini 3.5 Flashwon 11 · lost 14
Grok 4.7won 10 · lost 13
Gemini 3 Prowon 9 · lost 12
Muse Spark 1.1won 10 · lost 14
Grok 4.6won 10 · lost 16
GPT-6 Astrawon 7 · lost 14
Gemini 3.7 Flashwon 6 · lost 14
Claude Opus 4.7won 8 · lost 20
Claude Sonnet 5.5won 5 · lost 14
GPT-5.6 Solwon 7 · lost 21
Muse Spark 1.2won 4 · lost 14
GPT-5.5won 6 · lost 22
Kimi K3won 4 · lost 16
Claude Opus 5.5won 5 · lost 21
Claude Opus 4.8won 5 · lost 22
Claude Opus 5won 4 · lost 21
GPT-6.1 Solwon 3 · lost 16
Gemini 4 Argonwon 2 · lost 15
Claude Fable 5.1won 0 · lost 21
Claude Fable 5won 0 · lost 21

§ 3 · Sources

Where the numbers come from

9 publications, 39 figures. Every one links to the page it was read from.

LMArena Text ↗1 measureread 2026-11-10
LMArena Text 1475
GDPval-AA ↗1 measureread 2026-11-10
GDPval-AA 1256
ARC-AGI-2 ↗1 measureread 2026-11-10
ARC-AGI-2 74%
LiveBench 1.0 ↗8 measuresread 2026-11-10
LiveBench 1.0 78LiveBench 1.0 · Reasoning 88.1LiveBench 1.0 · Coding 77.5LiveBench 1.0 · Agentic Coding 53.8LiveBench 1.0 · Mathematics 94.1LiveBench 1.0 · Data Analysis 79.3LiveBench 1.0 · Language 82.6LiveBench 1.0 · Instruction Following 70.2
Kagi LLM Benchmark ↗1 measureread 2026-11-10
Kagi LLM Benchmark 63.8%
Vals.ai ↗14 measuresread 2026-11-10
Vals · CaseLaw 63.77%Vals · LegalBench 86.04%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%
ParseBench ↗6 measuresread 2026-11-10
ParseBench · Overall 61.72%ParseBench · Tables 83.64%ParseBench · Charts 65.57%ParseBench · Content faithfulness 86.78%ParseBench · Semantic formatting 55.67%ParseBench · Visual grounding 16.95%
Enkrypt AI Safety Leaderboard ↗6 measuresread 2026-11-10
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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