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The LLM benchmark aggregator.

GPT-5.6 Terra

OpenAI

Strongest in Data analysis (#11 of 31), weakest in Toxicity avoidance (#212 of 268). Above par in 35 of 40 scopes. Among the models it meets almost everywhere, it finishes behind Claude Fable 5.1 and Claude Opus 5.5 and ahead of Claude Sonnet 4.5 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.

Coding57.7#20/179
Agentic coding58#19/173
Code generation61.7—/20✱
Reasoning57.3#28/195
Science60.3#24/122
Mathematics57.4#26/152
Reasoning56.1#42/147
Expert reasoning60.9—/0✱
Knowledge55.5#36/197
Document parsing55.5#33/83
Academic knowledge56.8#39/123
Factuality52.5—/0✱
Safety55.8#38/329
Secure code64.1#18/270
Fairness61.1#37/292
Harm refusal54.4#80/292
Jailbreak resistance55.1#114/268
Toxicity avoidance47.9#212/268
Professional54.4#39/194
IT operations46.3#15/33
Finance57.9#16/162
Cybersecurity56.4#18/47
Document design46.7#22/31
Legal56.4#30/173
Medical52.6#67/149
Human preference63.2#43/354
Human preference63.2#43/354
Agents54#71/298
Long-horizon work43.7#18/31
Web research51#20/35
Computer use54.4#25/68
Knowledge work61.1#41/208
Tool use57.8—/81✱
Workflow automation56.3—/0✱
Core abilities51.7#80/199
Data analysis57.6#11/31
Language56.4#19/62
Instruction following44#43/62
General intelligence50.3#84/178
Long context56—/0✱

§ 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-5.6 Terra, left for the other.

DeepSeek V3won 24 · lost 0
Gemma 4 31Bwon 25 · lost 1
GPT-4.1 nanowon 23 · lost 1
GPT-4.1 miniwon 23 · lost 1
Grok 4.3won 23 · lost 1
DeepSeek V4 Flashwon 24 · lost 2
DeepSeek V3.2won 22 · lost 2
GPT-6 Lunawon 27 · lost 4
Mistral Large 4won 24 · lost 4
Kimi K2.6won 21 · lost 4
GPT OSS 120Bwon 20 · lost 4
Qwen3.8 27Bwon 22 · lost 5
Inklingwon 26 · lost 6
Kimi K2.5won 20 · lost 5
GPT-5.6 Lunawon 23 · lost 6
MiniMax M3won 22 · lost 6
GLM-5.2won 20 · lost 6
Claude Sonnet 4won 19 · lost 6
Gemini 2.5 Prowon 18 · lost 6
GLM-5.3 Flashwon 25 · lost 9
Qwen3.7 Maxwon 18 · lost 7
DeepSeek V4 Prowon 20 · lost 8
Grok 4.5won 20 · lost 9
Claude Sonnet 5won 19 · lost 9
Claude Sonnet 4.6won 16 · lost 8
O3won 15 · lost 9
Qwen3.8 Maxwon 20 · lost 12
Claude Haiku 5.5won 16 · lost 10
Gemini 3.6 Flashwon 16 · lost 11
GPT-5 miniwon 14 · lost 11
Gemini 3.8 Flashwon 19 · lost 15
GPT-5won 13 · lost 11
MiMo V2.6 Prowon 15 · lost 13
Claude Sonnet 4.5won 13 · lost 12
DeepSeek V4.1 Flashwon 13 · lost 14
Gemini 3.5 Flashwon 13 · lost 14
GPT-5.2won 12 · lost 13
GPT-5.4won 12 · lost 15
Grok 4.7won 13 · lost 17
Grok 4.6won 13 · lost 19
Muse Spark 1.1won 10 · lost 15
GPT-6 Solwon 10 · lost 16
Claude Opus 4.7won 10 · lost 17
GPT-5.5won 10 · lost 18
Claude Opus 4.8won 9 · lost 18
Kimi K3won 7 · lost 19
GPT-5.6 Solwon 6 · lost 24
Claude Sonnet 5.5won 5 · lost 21
Claude Opus 5won 5 · lost 23
GPT-6.1 Solwon 4 · lost 22
GPT-6 Astrawon 4 · lost 23
Claude Opus 5.5won 3 · lost 29
Claude Fable 5.1won 1 · lost 27

§ 3 · Sources

Where the numbers come from

13 publications, 66 figures. Every one links to the page it was read from.

LMArena Text ↗1 measureread 2026-11-10
LMArena Text 1468
GDPval-AA ↗1 measureread 2026-11-10
GDPval-AA 1461
AA-Briefcase ↗1 measureread 2026-11-10
AA-Briefcase 1333
Terminal-Bench ↗1 measureread 2026-11-10
Terminal-Bench 21.5% (Codex)
ARC-AGI-2 ↗1 measureread 2026-11-10
ARC-AGI-2 83.9%
LiveBench ↗8 measuresread 2026-11-10
LiveBench 67.2LiveBench · Agentic 25.9LiveBench · Reasoning 90.6LiveBench · Coding 84.2LiveBench · Browser Use 70.3LiveBench · Data Analysis 79.3LiveBench · Research 79.7LiveBench · Design 70.9
LiveBench 1.0 ↗8 measuresread 2026-11-10
LiveBench 1.0 77.9LiveBench 1.0 · Reasoning 90.6LiveBench 1.0 · Coding 78.2LiveBench 1.0 · Agentic Coding 54.9LiveBench 1.0 · Mathematics 94.9LiveBench 1.0 · Data Analysis 79.3LiveBench 1.0 · Language 82.9LiveBench 1.0 · Instruction Following 64.6
Kagi LLM Benchmark ↗1 measureread 2026-11-10
Kagi LLM Benchmark 51.3%
SimpleBench ↗1 measureread 2026-11-10
SimpleBench 48.9%
Vals.ai ↗16 measuresread 2026-11-10
Vals · Legal Research Bench 41.35%Vals · LegalBench 85.11%Vals · Finance Agent 54.44%Vals · CorpFin 65.31%Vals · TaxEval 76.17%Vals · MortgageTax 67.33%Vals · MedCode 43.41%Vals · MedScribe 82.87%Vals · CyberBench 72.08%Vals · SRE Bench 9.54%Vals · SWE-bench Verified 95.4% (Mini-SWE-agent)Vals · Vibe Code Bench 74.59% (OpenHands)Vals · Code Migration 47.8%Vals · GPQA Diamond 90.91%Vals · MMLU Pro 86.66%Vals · ProofBench 74%
ParseBench ↗6 measuresread 2026-11-10
ParseBench · Overall 67.06%ParseBench · Tables 86.74%ParseBench · Charts 67.86%ParseBench · Content faithfulness 86.77%ParseBench · Semantic formatting 68.47%ParseBench · Visual grounding 25.45%
Enkrypt AI Safety Leaderboard ↗6 measuresread 2026-11-10
Enkrypt · Jailbreak risk 8.5%Enkrypt · Harmful content risk 2.2%Enkrypt · CBRN risk 20%Enkrypt · Toxicity risk 7.6%Enkrypt · Bias risk 60.7%Enkrypt · Insecure code risk 4.4%
GDPVal-AA 1503tau3-Banking 28.7%Toolathlon Verified 64.8%Automation Bench Public 28%Apex-Agents (pass@1) 25.4%MCPMark 74%WildClawBench 60%Terminal-Bench 2.1 75.7%SciCode 50.1%Humanity's Last Exam (without tools) 38.5%GPQA Diamond 89.6%CritPt 22.9%AA-LCR 73.3%AA-Omniscience Accuracy 45%AA-Omniscience Non-Hallucination 10%

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PublicAI Index badge for GPT-5.6 Terra[![PublicAI Index](https://publicai.io/model-index/badge?model=gpt-5-6-terra)](https://publicai.io/model-index/m/gpt-5-6-terra)

✱ Placed by a one-off publication, not a board that re-ran the model. ✱✱ marks a figure the model’s own publisher printed, which counts for less again. Scores are 0–100 on the PublicAI Index scale, 50 = the average of the models each source lists. Scores belong to their publishers.

Snapshot 2026-11-10 · the full index · JSON API