Capability
62.9/100
field median 57.3
#41 of 214 ranked models
Model profile · Z.AI
Data as of July 31, 2026 · How the score is built
Knowledge ranks #9. Particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.
18 published rows leave some tracked benchmark slots empty. Independent runtime speed has not been measured.
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Capability
62.9/100
field median 57.3
#41 of 214 ranked models
Price
$1.40input / $4.40 output
input median $1
blended $2.90
Speed
Not measured
field median 108 tok/s
Time to first token not measured
Context
1Mtokens
field median 256,000
Reported for this model; direct source link not stored
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
GLM-5.2 category percentile values
The dashed outline is median of 6 nearest peers.
Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Horizontal: blended price per million tokens, log scale · Vertical: public score
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersScores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #21 of 128Percentile 84thWeight 22%4 benchmarksVerified | 56.8 | #21 of 128 | 84th | 22% | 4 benchmarks | Verified |
| CodingRank #12 of 129Percentile 91stWeight 20%5 benchmarksVerified | 64.4 | #12 of 129 | 91st | 20% | 5 benchmarks | Verified |
| ReasoningWeight 17%1 benchmarkVerified | Score pending | Not ranked | Not available | 17% | 1 benchmark | Verified |
| KnowledgeRank #9 of 55Percentile 85thWeight 12%4 benchmarksVerified | 82.4 | #9 of 55 | 85th | 12% | 4 benchmarks | Verified |
| MathRank Not rankedWeight 5%4 benchmarksVerified | 81.4 | Not ranked | Not available | 5% | 4 benchmarks | Verified |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score62.1% | Versus best verified row Best verified: Claude Mythos 5 · 80.3% | Gap18.2 behind | WeightWeighted 10% | Provider exact |
| NL2Repo | Score48.9% | Versus best verified row Best verified: DeepSeek V4 Flash (Max) · 54.2% | Gap5.3 behind | WeightDisplay only | Provider exact |
| Terminal-Bench 2.0 | Score81.0% | Versus best verified row Best verified: GPT-5.6 Sol · 91.9% | Gap10.9 behind | WeightDisplay only | Provider exact |
| ProgramBenchProgramBench: Can Language Models Rebuild Programs From Scratch? | Score63.7% | Versus best verified row Best verified: Claude Opus 5 · 93.0% | Gap29.3 behind | WeightDisplay only | Provider exact |
| cursorBench32 | Score55.0% | Versus best verified row Best verified: Claude Fable 5 · 70.5% | Gap15.5 behind | WeightDisplay only | Benchmark exact |
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.0 | Score81% | Versus best verified row Best verified: GPT-5.6 Sol · 91.9% | Gap10.9 behind | WeightWeighted 38% | Provider exact |
| MCP Atlas | Score76.8% | Versus best verified row Best verified: Muse Spark 1.1 · 88.1% | Gap11.3 behind | WeightDisplay only | Provider exact |
| Toolathlon | Score48.2% | Versus best verified row Best verified: Muse Spark 1.1 · 75.6% | Gap27.4 behind | WeightDisplay only | Provider exact |
| ResearchClawBench | Score20.7% | Versus best verified row Best verified: Claude Opus 4.8 · 21.1% | Gap0.4 behind | WeightDisplay only | Benchmark exact |
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| CritPtCritical Physics Tasks | Score20.9% | Versus best verified row Best verified: GLM-5.2 · 20.9% | GapBest verified | WeightDisplay only | Provider exact |
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| HLEHumanity's Last Exam | Score54.7% | Versus best verified row Best verified: Claude Opus 5 · 64.7% | Gap10 behind | WeightWeighted 45% | Provider exact |
| GPQAGraduate-Level Google-Proof Q&A | Score91.2% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap4.3 behind | WeightWeighted 7% | Provider exact |
| GPQA-DGPQA Diamond | Score91.2% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap4.3 behind | WeightDisplay only | Provider exact |
| HLE w/o toolsHumanity's Last Exam without tools | Score40.5% | Versus best verified row Best verified: Claude Mythos 5 · 59% | Gap18.5 behind | WeightDisplay only | Provider exact |
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME26AIME 2026 | Score99.2% | Versus best verified row Best verified: GLM-5.2 · 99.2% | GapBest verified | WeightWeighted 25% | Provider exact |
| HMMT Feb 2026Harvard-MIT Mathematics Tournament February 2026 | Score92.5% | Versus best verified row Best verified: Qwen3.7 Max · 97.1% | Gap4.6 behind | WeightWeighted 25% | Provider exact |
| HMMT Nov 2025Harvard-MIT Mathematics Tournament November 2025 | Score94.4% | Versus best verified row Best verified: Qwen3.6 Plus · 94.6% | Gap0.2 behind | WeightDisplay only | Provider exact |
| MMAnswerBench | Score91.0% | Versus best verified row Best verified: GLM-5.2 · 91.0% | GapBest verified | WeightDisplay only | Provider exact |
The sequence follows explicit supersedes links. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Mar 1, 2026
GLM-5Score 65.2 · $1 / $3.2
Apr 7, 2026
GLM-5.1Score 66.8 · $1.4 / $4.4
Jun 16, 2026 · you are here
GLM-5.2Score 62.9 · $1.4 / $4.4
flagship · 5.2
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
GLM-5.2 ranks #41 of 214 on the public leaderboard with a score of 62.94/100. It does not yet have enough sourced coverage for a verified position.
GLM-5.2 is a open weight model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Official exact-value snapshot from Z.AI's GLM-5.2 launch materials and Hugging Face model card. BenchLM maps directly comparable rows into existing keys; DeepSWE, FrontierSWE, PostTrainBench, SWE-Marathon, and other unsupported long-horizon rows remain excluded or external-only until stable local keys exist.
GLM-5.2 sits in the GLM-5 family with GLM-5, GLM-5.1, GLM-5 (Reasoning), GLM-5-Turbo, GLM-5V-Turbo. Its explicit predecessor is GLM-5.1. 18 of 376 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Knowledge at #9, while its lowest eligible position is Agentic at #21. particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.
Z.AI · Model release
GLM-5.2 ranks #41 out of 214 models on the public BenchAlign leaderboard, with a score of 62.94/100. Its evidence status is Estimated, and this profile shows 18 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
GLM-5.2 ranks #9 out of 55 eligible models for knowledge and understanding, with a public category score of 82.4/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.
GLM-5.2 ranks #12 out of 129 eligible models for coding and programming, with a public category score of 64.4/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
GLM-5.2 has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
GLM-5.2 has source-displayable benchmark coverage for reasoning and logic, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
GLM-5.2 ranks #21 out of 128 eligible models for agentic tool use and computer tasks, with a public category score of 56.8/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
GLM-5.2 is an open-weight model from Z.AI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
GLM-5.2 belongs to the GLM-5 family. Related tracked variants include GLM-5, GLM-5.1, GLM-5 (Reasoning), plus 2 more. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
No. GLM-5.2 currently has 42 source-displayable rows across 376 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
GLM-5.2 has a reported context window of 1M in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.
Related resources
Last updated July 31, 2026. Runtime fields remain blank until a sourced snapshot exists.
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