RankingEffort benchmarks

GLM-5.1 · Non-reasoning effort benchmarks

Compare measured Low, Medium, High and other settings. Every number keeps its source.

Model

GLM-5.1

41 measured results · 4 reported effort labels · 31 benchmark/harness combinations. Model evidence and sources →

Capability score evidence

GLM-5.1 · Non-reasoning: 11.1 overall comparison score. Meets the overall evidence requirements.

Which sources carry the weight?

Artificial Analysis · 75.1% direct comparison weight · independent
Epoch AI · 24.9% direct comparison weight · independent

These shares describe the comparisons entering the fit, not fractions of the final score.

How sensitive is this score?

Remove one evaluator or family and refit the graph against the same overall panel. Values use the matching dataset audited 2026-09-12. These are sensitivity checks, not confidence intervals; ranking eligibility may also change.

Removed evidence	Recomputed score	Change
Artificial Analysis	8.7	-2.4
ARC Prize	13.2	+2.1
Datacurve	9.6	-1.6
LiveCodeBench	11.1	-0.1
Scale	12.2	+1.0
Terminal-Bench	11.1	-0.0
Cognition	11.6	+0.5
LiveBench	11.3	+0.2
SWE-rebench	11.4	+0.3
Epoch AI	11.8	+0.7
Vals AI	10.8	-0.3
Provider reports · Anthropic	11.1	+0.0
Provider reports · Moonshot AI	11.1	-0.0
Provider reports · Meta	11.1	-0.0
Provider reports · Mistral	11.1	+0.0
All provider reports	11.1	-0.0

Every benchmark family

Removed evidence	Recomputed score	Change
arc-agi	13.2	+2.0
critpt	13.1	+2.0
deepswe	9.6	-1.6
epoch-game-puzzles	9.8	-1.4
tau-bench	10.1	-1.1
mmmu-pro	11.8	+0.7
frontiermath	11.8	+0.6
gpqa	10.6	-0.6
aa-lcr	11.7	+0.5
frontiercode	11.6	+0.5
terminal-bench	10.7	-0.5
omniscience	10.7	-0.5
simpleqa	11.4	+0.3
ifbench	11.4	+0.3
mmlu-pro	11.4	+0.3
swe-rebench	11.4	+0.3
hle	10.9	-0.3
multi-swe-bench	11.0	-0.2
scicode	11.3	+0.2
livebench-instructions	11.3	+0.1
harvey	11.0	-0.1
livebench-language	11.3	+0.1
swe-atlas-qna	11.0	-0.1
apex-agents	11.2	+0.1
gdp-pdf	11.2	+0.1
livebench-coding	11.1	-0.1
gmmlu	11.2	+0.1
arc-agi-3	11.1	-0.1
livebench-reasoning	11.1	-0.1
vibe-code	11.1	-0.1
vals-finance-agent	11.1	-0.0
vals-legal-research	11.1	-0.0
swe-atlas-test-writing	11.1	-0.0
vals-code-migration	11.1	-0.0
analyst-agent	11.2	+0.0
briefcase	11.1	-0.0
livebench-data	11.2	+0.0
livebench-math	11.2	+0.0
livecodebench	11.1	-0.0
automationbench	11.1	-0.0
itbench	11.1	-0.0
math500	11.2	+0.0
swe-atlas-refactoring	11.1	-0.0
vals-excel-modeling	11.1	-0.0
enigma-eval	11.2	+0.0
aime	11.2	+0.0
enterprise-ops	11.2	+0.0
gdpval	11.1	-0.0
swe-bench-pro	11.1	+0.0

What evidence is missing from the fit?

Across all collected settings for this model: 95 observations, 27 contributing, 0 matched without graph weight and 68 excluded. Counts do not establish rank eligibility.

2 · Benchmark family or source protocol has not been reviewed
12 · Effort is not identified
26 · No reviewed effort for this catalog observation; any separately collected effort measurement is counted separately
27 · Supporting evidence outside the reviewed capability core
1 · Microsoft report Table 11 and section 4.1: MAI Terminal-Bench removes timeouts and uses a minimal ReAct harness; comparator values are cited from other model releases. A common evaluation protocol is not established.

Inspect every observation and exclusion →

Capability profile

One configuration, seven capabilities

Comparison score · 0–100

AGLM-5.1 · Non-reasoning2 of 7 capabilities supported

Gaps are unknown, not zero. Hollow points are preliminary.

Agentic
A39.5Preliminary
Hard reasoning
A11.5Supported
Coding
A22.4Preliminary
Human pref
Unknown
Knowledge
A13.0Supported
Multimodal
Unknown
Long context
A10.3Preliminary

Each profile belongs to the named configuration. Capability scores use a shared panel for all axes, distinct from the overall ranking panel; they are not accuracy percentages or direct head-to-head wins.

Compare this configuration →Explore the model evidence profile →

These are published benchmark scores. Supported effort estimates also appear on the Capability leaderboard. Results from different harnesses occupy different rows. Missing cells stay unknown; no scores are copied between effort levels.

Benchmark / source	Max	Reasoning	Non-reasoning	Unspecified
AA-Briefcase EloArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details AA-Briefcase is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better · Evaluation results measured independently by Artificial Analysis Unit: elo. Source collected 2026-09-12.	Not reported	964.01 Elo Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
AA-LCR v1.1Artificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	73.67% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	53.33% Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
AutomationBench-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of task objectives completed with no guardrail violations · Higher is better · Benchmark developed by Zapier · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	20.31% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
CritPtArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by Argonne and UIUC, with contributions from 60+ researchers globally · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	4.57% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	0% Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
GDP.pdf: All-passArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of attempts where every atomic criterion passed · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	8.4% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
GDPval-AA v2 LeaderboardArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Elo rating for performance on real-world work tasks · Anchored to a human baseline of 1,000 · Higher is better · Evaluation results measured independently by Artificial Analysis Unit: elo. Source collected 2026-09-12.	Not reported	1,180.01 Elo Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
GPQA DiamondArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	86.77% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	83.94% Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
Humanity's Last ExamArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	30.07% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	27.94% Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
IFBenchArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	76.26% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	51.97% Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
ITBench-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Combined public and private Site Reliability Engineering (SRE) tasks · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	40.25% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
AA-Omniscience IndexArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis Unit: index. Source collected 2026-09-12.	Not reported	0.85 Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	-22.43 Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
AA-Omniscience AccuracyArtificial Analysis · 6000 questions; 1 attempt; correct-answer share; GPT-5.6 Luna medium grader Benchmark details AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	23.7% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	25.18% Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
SciCodeArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	44.79% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
𝜏²-Bench TelecomArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	97.66% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	97.08% Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
𝜏³-BankingArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by Sierra Research · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	13.61% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
Terminal-Bench HardArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	43.18% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	35.61% Reported settings GLM-5.1 (Non-reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1-non-reasoning	Not reported
Terminal-Bench v2.1Artificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by the Laude Institute and open-source contributors · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	61.8% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
Terminal-Bench v4.0Artificial Analysis · mini-SWE-agent v2.4.6; 66 tasks; pass@1 averaged over 3 repeats; 500 steps; task timeout up to 8 hours Benchmark details Benchmark developed by the Laude Institute, Stanford, and open-source contributors · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	Not reported	2.02% Reported settings GLM-5.1 (Reasoning); Model source: https://artificialanalysis.ai/models/glm-5-1	Not reported	Not reported
Terminal-Bench 2.1Terminal-Bench · Claude Code Benchmark details Original public board. Configuration and task coverage may differ; inspect source. Unit: percent. Source collected 2026-09-12.	58.65% Reported settings GLM-5.1; Reported effort: max; 445 trials; recorded 2026-03-27	Not reported	Not reported	Not reported
Epoch · Chess Puzzles · v1.1.6Epoch AI · Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match Benchmark details Epoch AI internal evaluation. Mean accuracy under the published setting; exact task version and scorer stay separate. Related tasks share a family budget. Data by Epoch AI, CC BY. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	19% Reported settings Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=glm-5.1; run=kmHbjBrwGgEKNr65JrXcGX; mean accuracy; https://epoch.ai/data/benchmarks.csv
Epoch · FrontierMath Tiers 1–3 · v2.0.0Epoch AI · Epoch AI Inspect; task 2.0.0; scorer verification_code Benchmark details Epoch AI internal evaluation. Mean accuracy under the published setting; exact task version and scorer stay separate. Related tasks share a family budget. Data by Epoch AI, CC BY. FrontierMath was developed with OpenAI funding and unequal access to part of the question set; this result is from Epoch's evaluation. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	24.91% Reported settings Epoch AI Inspect; task 2.0.0; scorer verification_code; model=glm-5.1_none; run=4hmmfwG9tgZJvKhL78nKzL; mean accuracy; https://epoch.ai/data/benchmarks.csv	36.84% Reported settings Epoch AI Inspect; task 2.0.0; scorer verification_code; model=glm-5.1; run=Mhwme9odWJMzMjoYkW8r6B; mean accuracy; https://epoch.ai/data/benchmarks.csv
Epoch · GPQA diamond · v1.0.11Epoch AI · Epoch AI Inspect; task 1.0.11; scorer choice Benchmark details Epoch AI internal evaluation. Mean accuracy under the published setting; exact task version and scorer stay separate. Related tasks share a family budget. Data by Epoch AI, CC BY. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	89.9% Reported settings Epoch AI Inspect; task 1.0.11; scorer choice; model=glm-5.1; run=CBbeMKRRSeRsXnvSfn7Png; mean accuracy; https://epoch.ai/data/benchmarks.csv
Epoch · OTIS Mock AIME 2024-2025 · v1.1.6Epoch AI · Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match Benchmark details Epoch AI internal evaluation. Mean accuracy under the published setting; exact task version and scorer stay separate. Related tasks share a family budget. Data by Epoch AI, CC BY. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	93.33% Reported settings Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=glm-5.1; run=b7Lw9ExxAhVY3hdExoh4MP; mean accuracy; https://epoch.ai/data/benchmarks.csv
Epoch · SimpleQA Verified · v1.2.0Epoch AI · Epoch AI Inspect; task 1.2.0; scorer simpleqa_scorer Benchmark details Epoch AI internal evaluation. Mean accuracy under the published setting; exact task version and scorer stay separate. Related tasks share a family budget. Data by Epoch AI, CC BY. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	34% Reported settings Epoch AI Inspect; task 1.2.0; scorer simpleqa_scorer; model=glm-5.1; run=R2fMFAtRXah864hMifdQGV; mean accuracy; https://epoch.ai/data/benchmarks.csv
Vals AI · Finance Agent v2Vals AI · Vals FAB v2 shared six-tool harness; two-hour task limit Benchmark details Overall weighted rubric accuracy, averaged over three runs on 450 tasks. Dealbreaker failures and timeouts score zero. Related categories and all-pass scores are not additional votes. Three-model judge panel; private test set. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	44.79% Reported settings {"checkpoint":"glm-5.1","evaluatorModel":"zai/glm-5.1","sourceUpdatedOn":"2026-09-10","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":null,"top_p":null,"max_output_tokens":null,"verbosity":null,"provider":"Zhipu AI","harness":null}
Vals AI · Code Migration v1Vals AI · Vals Code Migration shared agent harness Benchmark details Hidden behavior-test pass rate, equally weighted across 40 repositories (30 CLI and 10 COBOL-to-Java). Anti-cheat failures score zero. Code-quality ratings and language splits are not additional votes. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	25.77% Reported settings {"checkpoint":"glm-5.1","evaluatorModel":"zai/glm-5.1","sourceUpdatedOn":"2026-09-10","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":1,"top_p":0.95,"max_output_tokens":131072,"verbosity":null,"provider":"Zhipu AI","harness":null}
Vals AI · Legal Research Bench v1Vals AI · Vals Legal Research shared five-tool harness; three-hour task limit Benchmark details All-pass accuracy on 208 private legal research tasks. Every expert rubric check must pass; GPT-5.4 judges responses. Partial-credit scores and practice-area splits are not additional votes. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	27.89% Reported settings {"checkpoint":"glm-5.1","evaluatorModel":"zai/glm-5.1","sourceUpdatedOn":"2026-09-10","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":1,"top_p":0.95,"max_output_tokens":131072,"verbosity":null,"provider":"Zhipu AI","harness":null}
Vals AI · MMLU-Pro v1Vals AI · Vals MMLU-Pro five-shot chain-of-thought; regex answer grading Benchmark details Mean accuracy across 14 subjects under Vals five-shot prompting. Subject splits share one family and are not additional votes. Vals labels this benchmark archived; it is historical corroboration, not ongoing frontier evaluation. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	86.9% Reported settings {"checkpoint":"glm-5.1","evaluatorModel":"zai/glm-5.1","sourceUpdatedOn":"2026-09-01","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":null,"top_p":null,"max_output_tokens":null,"verbosity":null,"provider":"Zhipu AI","harness":null}
Vals AI · Terminal-Bench 2.1Vals AI · Vals Terminus 2 harness; pass@1 on 89 tasks Benchmark details Overall task pass rate under the shared Terminus 2 configuration. All task tests must pass. Difficulty splits share one family. Fable 5 and Opus 5 mixed-checkpoint fallback runs are excluded. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	56.93% Reported settings {"checkpoint":"glm-5.1","evaluatorModel":"zai/glm-5.1","sourceUpdatedOn":"2026-09-10","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":1,"top_p":0.95,"max_output_tokens":131072,"verbosity":null,"provider":"Zhipu AI","harness":null}
Vals AI · Vibe Code Bench v1.1Vals AI · Vals modified OpenHands harness; five hours or 1,000 turns per app Benchmark details Mean app test pass rate on the canonical v1.1 test set. Browser Use executes workflow tests; each test requires at least 90% of its substeps. Automated judging can introduce error. Alternate agents are excluded. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	31.46% Reported settings {"checkpoint":"glm-5.1","evaluatorModel":"zai/glm-5.1","sourceUpdatedOn":"2026-09-10","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":1,"top_p":0.95,"max_output_tokens":131072,"verbosity":null,"provider":"Zhipu AI","harness":"OpenHands"}
Vals AI · Harvey Legal Agent Benchmark v1Vals AI · Vals Harvey held-out legal file-work protocol; internet disabled Benchmark details Mean task resolution across two judges (GPT-5.5 Medium and Sonnet 4.6), requiring every criterion to pass. Criteria pass rates and practice-area splits do not add votes. Shared Harvey family; Fable 5 fallback run excluded. Unit: percent. Source collected 2026-09-12.	Not reported	Not reported	Not reported	0% Reported settings {"checkpoint":"glm-5.1","evaluatorModel":"zai/glm-5.1","sourceUpdatedOn":"2026-09-10","reasoning_effort":null,"compute_effort":null,"reasoning":null,"temperature":null,"top_p":null,"max_output_tokens":null,"verbosity":null,"provider":"Zhipu AI","harness":null}

What these comparisons mean

Each source reports its own effort labels. Matching labels do not establish equal compute budgets or identical fallback behavior. Open Reported settings to inspect the exact model name, agent and task coverage. Multiple results at one level remain visible rather than selecting the best. Source-reported None or Non-reasoning labels are not treated as evidence that an API supports those settings.

These measurements use their own dated source collection. The leaderboard fits identified configurations jointly; it contains no pooled model ratings. Explicit thinking budgets are separate configurations. Read the methodology.

Download all effort benchmark results · 7,424 results across 240 models · Collected 2026-09-12

GLM-5.1 · Non-reasoning effort benchmarks

GLM-5.1

Capability score evidence

One configuration, seven capabilities

Agentic

Hard reasoning

Coding

Human pref

Knowledge

Multimodal

Long context