Head-to-head comparison
GLM-5.2 vs. GLM-5.1
Compare published benchmark results from matching versions and cohorts, API pricing, and technical specifications. Any documented test setup differences remain visible.
168
shared result series
18
distinct benchmarks
2026-08-16
latest retrieval
Change model selection
Specifications and cost
General specifications and standard API pricing
Undisclosed parameter counts and knowledge cutoffs stay marked as unavailable. The table shows direct standard token pricing, including documented context tiers. Batch, fast, priority, regional surcharges, tool calls, and cloud platform pricing are excluded.
| Attribute | GLM-5.2 | GLM-5.1 |
|---|---|---|
| Provider | Z.ai | Z.ai |
| Released | Jun 16, 2026 | Apr 7, 2026 |
| Availability | Open Source | Open Source |
| Model type | Open Weights | Open Weights |
| Parameters | 744B, 40B active | 744B, 40B active |
| Architecture | Mixture of Experts | Mixture of Experts |
| Context window | 1,000,000 tokens | 200,000 tokens |
| Knowledge cutoff | Not published | Not published |
| Technical sources | Model, Context | Model, Context |
| API pricing per 1M tokens | ||
| API input | $1.4 | $1.4 |
| API output | $4.4 | $4.4 |
| Cache read | $0.26 | $0.26 |
| Cache write (5 min.) | Not listed | Not listed |
| Cache write (1 hr.) | Not listed | Not listed |
| Price verified | 07/30/2026Source | 07/30/2026Source |
GLM-5.2GLM-5.1
API inputUSD per 1M tokens, base tier
GLM-5.2$1.4Tie
GLM-5.1$1.4Tie
API outputUSD per 1M tokens, base tier
GLM-5.2$4.4Tie
GLM-5.1$4.4Tie
Cache readUSD per 1M tokens, base tier
GLM-5.2$0.26Tie
GLM-5.1$0.26Tie
Performance
Shared benchmarks
Only published results with the same benchmark version, task, metric, and comparison cohort are paired. Different reasoning levels, agents, harnesses, or output limits appear directly in the table.
168 shared result series
GLM-5.2GLM-5.1
Arena Text, Style ControlOverall. Higher is better. Test setup not documented as identical
GLM-5.21,470.655 arena pointsHigher measured value
GLM-5.11,466.935 arena points
Code MigrationOverall. Higher is better. No documented setup difference
GLM-5.237.87 %Winner
GLM-5.125.768 %
CorpFin v2Overall. Higher is better. No documented setup difference
GLM-5.266.123 %Winner
GLM-5.164.452 %
Finance Agent (v2)Overall. Higher is better. No documented setup difference
GLM-5.249.699 %Winner
GLM-5.144.792 %
GPQA DiamondOverall. Higher is better. No documented setup difference
GLM-5.285.606 %Winner
GLM-5.184.518 %
Harvey's Legal Agent BenchmarkOverall · Task fully resolved. Higher is better. No documented setup difference
GLM-5.27.083 %Winner
GLM-5.10 %
Legal Research BenchOverall · All-pass. Higher is better. No documented setup difference
GLM-5.231.25 %Winner
GLM-5.127.885 %
LegalBenchOverall. Higher is better. No documented setup difference
GLM-5.284.073 %
GLM-5.184.394 %Winner
LiveCodeBench v6Overall. Higher is better. No documented setup difference
GLM-5.269.5 %
GLM-5.181.38 %Winner
MedCodeOverall. Higher is better. No documented setup difference
GLM-5.240.771 %
GLM-5.141.604 %Winner
MedScribeOverall. Higher is better. No documented setup difference
GLM-5.283.534 %Winner
GLM-5.172.27 %
MMLU-ProOverall. Higher is better. No documented setup difference
GLM-5.286.714 %
GLM-5.186.9 %Winner
| Benchmark and task | GLM-5.2 | GLM-5.1 | Comparability | Source |
|---|---|---|---|---|
| Arena Text, Style ControlOverall | 1,470.655 arena pointsHigher measured value 95% confidence interval: ±5.045The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | 1,466.935 arena points 95% confidence interval: ±4.197The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | style-control-v1style-controlled Bradley-Terry ratingHigher is betterCohort: lmarena-text-style-control:2026-08-12:overallTest setup not documented as identicalNo direct winnerGLM-5.2: Snapshot: glm-5.2-max, Reasoning: max, Harness: Arena text, style control GLM-5.1: Snapshot: glm-5.1, Harness: Arena text, style controlGLM-5.2: Style-controlled Arena rating from 26,867 anonymous pairwise votes.GLM-5.1: Style-controlled Arena rating from 39,063 anonymous pairwise votes. | ArenaType: Official benchmark leaderboardLeaderboard updated: 2026-08-12Import ID: lmarena-eeedf9209373a7f9b324bfb839e7b02fedacd1e5-1611ed64f3f1Retrieved 2026-08-16 |
| Code MigrationOverall | 37.87 %Winner | 25.768 % | 1accuracyHigher is betterCohort: vals-code-migration:1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-code-migration-2026-08-16-107652dc6bfcRetrieved 2026-08-16 |
| CorpFin v2Overall | 66.123 %Winner | 64.452 % | 2accuracyHigher is betterCohort: vals-corp-fin-v2:2:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-12Import ID: vals-corp_fin_v2-2026-08-16-45bbf84f57abRetrieved 2026-08-16 |
| Finance Agent (v2)Overall | 49.699 %Winner | 44.792 % | 2accuracyHigher is betterCohort: vals-finance-agent-v2:2:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-14Import ID: vals-fabv2-2026-08-16-8853da43085dRetrieved 2026-08-16 |
| GPQA DiamondOverall | 85.606 %Winner | 84.518 % | 1accuracyHigher is betterCohort: vals-gpqa:1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-12Import ID: vals-gpqa-2026-08-16-7f8363eaa2dbRetrieved 2026-08-16 |
| Harvey's Legal Agent BenchmarkOverall · Task fully resolved | 7.083 %Winner | 0 % | 1task resolution rateHigher is betterCohort: vals-legal-agent-benchmark:1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-14Import ID: vals-hlab-2026-08-16-bae90566df2fRetrieved 2026-08-16 |
| Legal Research BenchOverall · All-pass | 31.25 %Winner | 27.885 % | 1all-pass rateHigher is betterCohort: vals-legal-research:1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-14Import ID: vals-legal_research-2026-08-16-af296c00ecaaRetrieved 2026-08-16 |
| LegalBenchOverall | 84.073 % | 84.394 %Winner | 1accuracyHigher is betterCohort: vals-legal-bench:1:overallNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-12Import ID: vals-legal_bench-2026-08-16-d5b3b1b2b5c8Retrieved 2026-08-16 |
| LiveCodeBench v6Overall | 69.5 % | 81.38 %Winner | 1accuracyHigher is betterCohort: vals-lcb:1:overallNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-lcb-2026-08-16-17b9c147080dRetrieved 2026-08-16 |
| MedCodeOverall | 40.771 % | 41.604 %Winner | 1accuracyHigher is betterCohort: vals-medcode:1:overallNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-12Import ID: vals-medcode-2026-08-16-524f7f474eacRetrieved 2026-08-16 |
| MedScribeOverall | 83.534 %Winner | 72.27 % | 1accuracyHigher is betterCohort: vals-medscribe:1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-12Import ID: vals-medscribe-2026-08-16-c385354a7e62Retrieved 2026-08-16 |
| MMLU-ProOverall | 86.714 % | 86.9 %Winner | 1accuracyHigher is betterCohort: vals-mmlu-pro:1:overallNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-12Import ID: vals-mmlu_pro-2026-08-16-e14d5bb3a34cRetrieved 2026-08-16 |
| ProgramBenchOverall | 0.5 %Winner | 0 % | 1accuracyHigher is betterCohort: vals-programbench:1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-programbench-2026-08-16-08d9727662ddRetrieved 2026-08-16 |
| SWE-bench VerifiedOverall | 82.8 %Winner | 76.4 % | 1accuracyHigher is betterCohort: vals-swebench:1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-12Import ID: vals-swebench-2026-08-16-c133071b8167Retrieved 2026-08-16 |
| TaxEval v2Overall | 73.344 %Winner | 71.194 % | 2accuracyHigher is betterCohort: vals-tax-eval-v2:2:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-12Import ID: vals-tax_eval_v2-2026-08-16-5bbd495dac53Retrieved 2026-08-16 |
| Terminal-Bench 2.1Overall | 67.79 %Winner | 56.929 % | 2.1accuracyHigher is betterCohort: vals-terminal-bench-2-1:2.1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-terminal-bench-2-1-2026-08-16-b3f38c18bea4Retrieved 2026-08-16 |
| Vals IndexCorpFin v2 | 66.317 %Winner | 65.268 % | 1.2benchmark scoreHigher is betterCohort: vals-vals-index:1.2:corp_fin_v2No documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-vals_index-2026-08-11-e79d08d59249Retrieved 2026-08-11 |
| Vibe Code Bench v1.1Overall | 63.961 %Winner | 31.456 % | 1.1accuracyHigher is betterCohort: vals-vibe-code-bench:1.1:overallNo documented setup differenceWinner: GLM-5.2GLM-5.2: Harness: OpenHands GLM-5.1: Harness: OpenHands | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-13Import ID: vals-vibe-code-2026-08-16-a73c9770bcc4Retrieved 2026-08-16 |
| Arena Text, Style ControlBusiness, management and finance | 1,459.883 arena points 95% confidence interval: ±8.752The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | 1,464.98 arena pointsHigher measured value 95% confidence interval: ±7.499The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | style-control-v1style-controlled Bradley-Terry ratingHigher is betterCohort: lmarena-text-style-control:2026-08-12:industry_business_and_management_and_financial_operationsTest setup not documented as identicalNo direct winnerGLM-5.2: Snapshot: glm-5.2-max, Reasoning: max, Harness: Arena text, style control GLM-5.1: Snapshot: glm-5.1, Harness: Arena text, style controlGLM-5.2: Style-controlled Arena rating from 5,374 anonymous pairwise votes.GLM-5.1: Style-controlled Arena rating from 7,906 anonymous pairwise votes. | ArenaType: Official benchmark leaderboardLeaderboard updated: 2026-08-12Import ID: lmarena-eeedf9209373a7f9b324bfb839e7b02fedacd1e5-1611ed64f3f1Retrieved 2026-08-16 |
| Code MigrationCLI | 27.16 %Higher measured value | 18.833 % | 1accuracyHigher is betterCohort: vals-code-migration:1:cliTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-code-migration-2026-08-11-3a2d5ce718a3Retrieved 2026-08-11 |
| CorpFin v2Exact Pages | 63.636 %Winner | 61.772 % | 2accuracyHigher is betterCohort: vals-corp-fin-v2:2:exact_pagesNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-corp_fin_v2-2026-08-11-d9e58d191cd9Retrieved 2026-08-11 |
| Finance Agent (v2)Adjustments | 46.305 %Winner | 43.276 % | 2accuracyHigher is betterCohort: vals-finance-agent-v2:2:adjustmentsNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-fabv2-2026-08-11-3e5c93a1857cRetrieved 2026-08-11 |
| GPQA DiamondFew-Shot CoT | 83.333 % | 85.787 %Winner | 1accuracyHigher is betterCohort: vals-gpqa:1:diamond_few_shot_cotNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-gpqa-2026-08-11-d3325290a404Retrieved 2026-08-11 |
| Harvey's Legal Agent BenchmarkAntitrust Competition · Criteria passed | 87.319 %Higher measured value | 71.377 % | 1criteria pass rateHigher is betterCohort: vals-legal-agent-benchmark:1:criteria_pass_rate__antitrust_competitionTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-07Import ID: vals-hlab-2026-08-11-ea11b6e53414Retrieved 2026-08-11 |
| Legal Research BenchAdministrative / Regulatory · All-pass | 39.394 %Higher measured value | 31.818 % | 1all-pass rateHigher is betterCohort: vals-legal-research:1:administrative_regulatoryTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-legal_research-2026-08-11-326cf7aaca58Retrieved 2026-08-11 |
| LegalBenchConclusion Tasks | 88.587 % | 89.575 %Winner | 1accuracyHigher is betterCohort: vals-legal-bench:1:conclusion_tasksNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-legal_bench-2026-08-11-836e5e44618eRetrieved 2026-08-11 |
| LiveCodeBench v6Easy | 97.205 % | 98.754 %Winner | 1accuracyHigher is betterCohort: vals-lcb:1:easyNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-lcb-2026-08-11-0342bcc364e4Retrieved 2026-08-11 |
| MMLU-ProBiology | 94.003 %Winner | 93.724 % | 1accuracyHigher is betterCohort: vals-mmlu-pro:1:biologyNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-mmlu_pro-2026-08-11-b8e53828d746Retrieved 2026-08-11 |
| ProgramBenchAlmost Resolved | 9.5 %Higher measured value | 1 % | 1accuracyHigher is betterCohort: vals-programbench:1:almostTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 1, Output limit: 64,000 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-programbench-2026-08-11-b6f8959fbdfeRetrieved 2026-08-11 |
| SWE-bench Verified<15 min fix | 88.144 % | 88.66 %Higher measured value | 1accuracyHigher is betterCohort: vals-swebench:1:<15 min fixTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-08Import ID: vals-swebench-2026-08-11-a6212cd857d8Retrieved 2026-08-11 |
| TaxEval v2Correctness | 62.061 %Winner | 61.538 % | 2accuracyHigher is betterCohort: vals-tax-eval-v2:2:correctnessNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-tax_eval_v2-2026-08-11-d05a40bf446fRetrieved 2026-08-11 |
| Terminal-Bench 2.1Easy | 100 %Higher measured value | 83.333 % | 2.1accuracyHigher is betterCohort: vals-terminal-bench-2-1:2.1:easyTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 1, Output limit: 48,000 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-terminal-bench-2-1-2026-08-11-8f0ebd72a609Retrieved 2026-08-11 |
| Vals IndexFinance Agent v2 | 50.83 %Winner | 44.79 % | 1.2benchmark scoreHigher is betterCohort: vals-vals-index:1.2:finance_agentNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-vals_index-2026-08-11-e79d08d59249Retrieved 2026-08-11 |
| Arena Text, Style ControlChinese | 1,521.061 arena pointsHigher measured value 95% confidence interval: ±15.24The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | 1,519.866 arena points 95% confidence interval: ±13.198The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | style-control-v1style-controlled Bradley-Terry ratingHigher is betterCohort: lmarena-text-style-control:2026-08-12:chineseTest setup not documented as identicalNo direct winnerGLM-5.2: Snapshot: glm-5.2-max, Reasoning: max, Harness: Arena text, style control GLM-5.1: Snapshot: glm-5.1, Harness: Arena text, style controlGLM-5.2: Style-controlled Arena rating from 1,662 anonymous pairwise votes.GLM-5.1: Style-controlled Arena rating from 2,373 anonymous pairwise votes. | ArenaType: Official benchmark leaderboardLeaderboard updated: 2026-08-12Import ID: lmarena-eeedf9209373a7f9b324bfb839e7b02fedacd1e5-1611ed64f3f1Retrieved 2026-08-16 |
| Code MigrationCOBOL | 70 %Higher measured value | 46.571 % | 1accuracyHigher is betterCohort: vals-code-migration:1:cobolTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-code-migration-2026-08-11-3a2d5ce718a3Retrieved 2026-08-11 |
| CorpFin v2Max Fitting Context | 68.415 %Winner | 66.317 % | 2accuracyHigher is betterCohort: vals-corp-fin-v2:2:max_fitting_contextNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-corp_fin_v2-2026-08-11-d9e58d191cd9Retrieved 2026-08-11 |
| Finance Agent (v2)All-Pass | 38.006 %Winner | 32.68 % | 2accuracyHigher is betterCohort: vals-finance-agent-v2:2:all_passNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-fabv2-2026-08-11-3e5c93a1857cRetrieved 2026-08-11 |
| GPQA DiamondZero-Shot CoT | 87.879 %Winner | 83.249 % | 1accuracyHigher is betterCohort: vals-gpqa:1:diamond_zero_shot_cotNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-gpqa-2026-08-11-d3325290a404Retrieved 2026-08-11 |
| Harvey's Legal Agent BenchmarkAntitrust Competition · Task fully resolved | 0 %No direct winner | 0 %No direct winner | 1task resolution rateHigher is betterCohort: vals-legal-agent-benchmark:1:antitrust_competitionTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-07Import ID: vals-hlab-2026-08-11-ea11b6e53414Retrieved 2026-08-11 |
| Legal Research BenchAdministrative / Regulatory · Weighted partial credit | 85.95 %Higher measured value | 80.005 % | 1weighted partial-credit scoreHigher is betterCohort: vals-legal-research:1:weighted_pass__administrative_regulatoryTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-legal_research-2026-08-11-326cf7aaca58Retrieved 2026-08-11 |
| LegalBenchInterpretation Tasks | 82.17 %Winner | 81.709 % | 1accuracyHigher is betterCohort: vals-legal-bench:1:interpretation_tasksNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-legal_bench-2026-08-11-836e5e44618eRetrieved 2026-08-11 |
| LiveCodeBench v6Hard | 37.143 % | 57.429 %Winner | 1accuracyHigher is betterCohort: vals-lcb:1:hardNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-lcb-2026-08-11-0342bcc364e4Retrieved 2026-08-11 |
| MMLU-ProBusiness | 91.888 %Winner | 91.128 % | 1accuracyHigher is betterCohort: vals-mmlu-pro:1:businessNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-mmlu_pro-2026-08-11-b8e53828d746Retrieved 2026-08-11 |
| ProgramBenchFully Resolved | 0.5 %Higher measured value | 0 % | 1accuracyHigher is betterCohort: vals-programbench:1:strictTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 1, Output limit: 64,000 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-programbench-2026-08-11-b6f8959fbdfeRetrieved 2026-08-11 |
| SWE-bench Verified>4 hours | 66.667 %Higher measured value | 33.333 % | 1accuracyHigher is betterCohort: vals-swebench:1:>4 hoursTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-08Import ID: vals-swebench-2026-08-11-a6212cd857d8Retrieved 2026-08-11 |
| TaxEval v2Stepwise Reasoning | 84.628 %Winner | 80.851 % | 2accuracyHigher is betterCohort: vals-tax-eval-v2:2:stepwise_reasoningNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-tax_eval_v2-2026-08-11-d05a40bf446fRetrieved 2026-08-11 |
| Terminal-Bench 2.1Hard | 51.111 %Higher measured value | 37.778 % | 2.1accuracyHigher is betterCohort: vals-terminal-bench-2-1:2.1:hardTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 1, Output limit: 48,000 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-terminal-bench-2-1-2026-08-11-8f0ebd72a609Retrieved 2026-08-11 |
| Vals IndexSWE-bench | 83.333 %Winner | 72.549 % | 1.2benchmark scoreHigher is betterCohort: vals-vals-index:1.2:swebenchNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-vals_index-2026-08-11-e79d08d59249Retrieved 2026-08-11 |
| Arena Text, Style ControlCoding | 1,506.101 arena points 95% confidence interval: ±7.714The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | 1,515.299 arena pointsHigher measured value 95% confidence interval: ±6.649The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | style-control-v1style-controlled Bradley-Terry ratingHigher is betterCohort: lmarena-text-style-control:2026-08-12:codingTest setup not documented as identicalNo direct winnerGLM-5.2: Snapshot: glm-5.2-max, Reasoning: max, Harness: Arena text, style control GLM-5.1: Snapshot: glm-5.1, Harness: Arena text, style controlGLM-5.2: Style-controlled Arena rating from 7,692 anonymous pairwise votes.GLM-5.1: Style-controlled Arena rating from 11,013 anonymous pairwise votes. | ArenaType: Official benchmark leaderboardLeaderboard updated: 2026-08-12Import ID: lmarena-eeedf9209373a7f9b324bfb839e7b02fedacd1e5-1611ed64f3f1Retrieved 2026-08-16 |
| Code MigrationCode Quality | 3.981 / 5Higher measured value | 3.544 / 5 | 1mean reviewer code-quality ratingHigher is betterCohort: vals-code-migration:1:code_qualityTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-code-migration-2026-08-11-3a2d5ce718a3Retrieved 2026-08-11 |
| CorpFin v2Shared Max Context | 66.317 %Winner | 65.268 % | 2accuracyHigher is betterCohort: vals-corp-fin-v2:2:shared_max_contextNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-corp_fin_v2-2026-08-11-d9e58d191cd9Retrieved 2026-08-11 |
| Finance Agent (v2)Comparables | 44.78 %Winner | 41.557 % | 2accuracyHigher is betterCohort: vals-finance-agent-v2:2:comparablesNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-fabv2-2026-08-11-3e5c93a1857cRetrieved 2026-08-11 |
| Harvey's Legal Agent BenchmarkArbitration International Dispute Resolution · Criteria passed | 91.756 %Higher measured value | 53.405 % | 1criteria pass rateHigher is betterCohort: vals-legal-agent-benchmark:1:criteria_pass_rate__arbitration_international_dispute_resolutionTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-07Import ID: vals-hlab-2026-08-11-ea11b6e53414Retrieved 2026-08-11 |
| Legal Research BenchBusiness & Commercial · All-pass | 27.907 %Higher measured value | 23.256 % | 1all-pass rateHigher is betterCohort: vals-legal-research:1:business_commercialTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-legal_research-2026-08-11-326cf7aaca58Retrieved 2026-08-11 |
| LegalBenchIssue Tasks | 89.506 %Winner | 89.02 % | 1accuracyHigher is betterCohort: vals-legal-bench:1:issue_tasksNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-legal_bench-2026-08-11-836e5e44618eRetrieved 2026-08-11 |
| LiveCodeBench v6Medium | 74.151 % | 87.958 %Winner | 1accuracyHigher is betterCohort: vals-lcb:1:mediumNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-lcb-2026-08-11-0342bcc364e4Retrieved 2026-08-11 |
| MMLU-ProChemistry | 91.608 % | 91.844 %Winner | 1accuracyHigher is betterCohort: vals-mmlu-pro:1:chemistryNo documented setup differenceWinner: GLM-5.1GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-mmlu_pro-2026-08-11-b8e53828d746Retrieved 2026-08-11 |
| ProgramBenchRaw Pass Rate | 62.605 %Higher measured value | 50.879 % | 1accuracyHigher is betterCohort: vals-programbench:1:partialTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 1, Output limit: 64,000 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-programbench-2026-08-11-b6f8959fbdfeRetrieved 2026-08-11 |
| SWE-bench Verified1-4 hours | 66.667 %Higher measured value | 45.238 % | 1accuracyHigher is betterCohort: vals-swebench:1:1-4 hoursTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-08Import ID: vals-swebench-2026-08-11-a6212cd857d8Retrieved 2026-08-11 |
| Terminal-Bench 2.1Medium | 74.545 %Higher measured value | 65.455 % | 2.1accuracyHigher is betterCohort: vals-terminal-bench-2-1:2.1:mediumTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 1, Output limit: 48,000 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-terminal-bench-2-1-2026-08-11-8f0ebd72a609Retrieved 2026-08-11 |
| Vals IndexTerminal-Bench 2.1 | 67.79 %Higher measured value | 56.929 % | 1.2benchmark scoreHigher is betterCohort: vals-vals-index:1.2:terminal_bench_2_1Test setup not documented as identicalNo direct winnerGLM-5.2: Top P: 1, Output limit: 48,000 tokens GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-09Import ID: vals-vals_index-2026-08-11-e79d08d59249Retrieved 2026-08-11 |
| Arena Text, Style ControlCreative writing | 1,449.311 arena pointsHigher measured value 95% confidence interval: ±9.401The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | 1,448.824 arena points 95% confidence interval: ±8.067The Arena score measures human preference rather than a fixed capability test. The confidence interval and model configuration are part of the result. | style-control-v1style-controlled Bradley-Terry ratingHigher is betterCohort: lmarena-text-style-control:2026-08-12:creative_writingTest setup not documented as identicalNo direct winnerGLM-5.2: Snapshot: glm-5.2-max, Reasoning: max, Harness: Arena text, style control GLM-5.1: Snapshot: glm-5.1, Harness: Arena text, style controlGLM-5.2: Style-controlled Arena rating from 4,900 anonymous pairwise votes.GLM-5.1: Style-controlled Arena rating from 6,969 anonymous pairwise votes. | ArenaType: Official benchmark leaderboardLeaderboard updated: 2026-08-12Import ID: lmarena-eeedf9209373a7f9b324bfb839e7b02fedacd1e5-1611ed64f3f1Retrieved 2026-08-16 |
| Finance Agent (v2)Disclosure Analysis | 61.588 %Winner | 52.713 % | 2accuracyHigher is betterCohort: vals-finance-agent-v2:2:disclosure_analysisNo documented setup differenceWinner: GLM-5.2GLM-5.2: No further settings disclosed GLM-5.1: No further settings disclosed | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-06Import ID: vals-fabv2-2026-08-11-3e5c93a1857cRetrieved 2026-08-11 |
| Harvey's Legal Agent BenchmarkArbitration International Dispute Resolution · Task fully resolved | 0 %No direct winner | 0 %No direct winner | 1task resolution rateHigher is betterCohort: vals-legal-agent-benchmark:1:arbitration_international_dispute_resolutionTest setup not documented as identicalNo direct winnerGLM-5.2: Reasoning: max, Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens GLM-5.1: Temperature: 1, Top P: 0.95, Output limit: 131,072 tokens | Vals AIType: Independent evaluationLeaderboard updated: 2026-08-07Import ID: vals-hlab-2026-08-11-ea11b6e53414Retrieved 2026-08-11 |
The table shows 64 diversified result series from 168 shared results. This prevents one benchmark with many subtasks from crowding out every other test.
How to read this comparison
Winning one benchmark is not an overall verdict
Your workload, budget, and required context length matter most. A coding benchmark says little about visual understanding or agent performance.
Prices use official API rates per 1M tokens. Web subscriptions and cloud platform surcharges can differ.
Every result links to its measurement source and retrieval date. Labels distinguish vendor reports, official benchmark leaderboards, and independent evaluations. Disputed or archived results are excluded.
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