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BenchLM
Data

Data as of November 12, 2026 · How the score is built

Decision readingGLM-5.3 scores 61 out of 100 and ranks #48 of 215. This profile shows 51 source-displayable benchmark rows; its strongest eligible category is Agentic at #14. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.

Email me when its price, rank or availability changes.

Capability

61/100

field median 49.4#48 of 215 ranked models

Public

#48of 215

Verified #32 of 81

Price

Self-hosted; infrastructure cost varies

input median $0.60No comparable first-party hosted token rate

Speed

86tok/s

field median 92 tok/sFirst token 25.62 s

Context

1Mtokens

field median 256,000Maximum output length is tracked separately

Decision snapshot

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.

Strongest published evidence

Agentic ranks #14. Particularly useful for coding agents, browser research, and computer-use workflows.

Validate before choosing

51 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.

Source-linked · 51 displayable benchmark rows

Is GLM-5.3 right for your job?

Its place on the LLM Selector’s job rankings, the same order the job pages publish. Describe your own job to see where it stands for that.

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Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #14 of 135Percentile 90thWeight 22%17 benchmarksMixed sources
66.7
CodingRank #33 of 162Percentile 80thWeight 20%16 benchmarksVerified
53.7
ReasoningRank #36 of 160Percentile 78thWeight 17%Not listedUnlisted sources
57.3
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeRank #48 of 180Percentile 74thWeight 12%2 benchmarksVerified
59.9
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%1 benchmarkVerified
Score pending
MathRank Not rankedWeight 5%2 benchmarksVerified
67.0

51 of 906 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

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.

  1. Agentic14/17 verified
  2. Coding16/16 verified
  3. ReasoningNot measured
  4. MultimodalNot measured
  5. Knowledge2/2 verified
  6. MultilingualNot measured
  7. Inst. Following1/1 verified
  8. Math2/2 verified
Verified sourceProvisionalNot measured

Capability shape

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-5.3 category percentile values

  • Agentic90th percentile
  • Coding80th percentile
  • Reasoning78th percentile
  • MultimodalNot eligible
  • Knowledge74th percentile
  • MultilingualNot eligible
  • Instruction followingNot eligible
  • MathNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#14/135
  2. Coding#33/162
  3. Reasoning#36/160
  4. MultimodalNot ranked
  5. Knowledge#48/180
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Every result says who ran the test. For this model, Z.AI ran 17 of 51. Coding opens by default. The tick marks the best source-verified result in the catalog; provisional leaders do not set the reference.

Overall rank
#48
of 215 on the leaderboard
Reported by Z.AI
17
Shown with that label
Other publishers
6
Another publisher’s document or table
Run by a benchmark
23
The benchmark’s own run
Independent runs
5
An outside evaluator’s run
Coding16 rows
Coding benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
CursorBench 4.042.6%

Best verified: Gemini 4 Carbon · 60.4%

Gap17.8 behindRun by CursorBench 4.0Counts10% ref. weight
FrontierSWE v230.2%

Best verified: Claude Fable 5.2 · 69.9%

Gap39.7 behindRun by FrontierSWE v2Counts8% ref. weight
LiveCodeBench (Vals)LiveCodeBench, Vals AI run80.5%

Best verified: Claude Fable 5.1 · 90.5%

Gap10 behindIndependent run: Vals AICounts8% ref. weight
DeepSWE66.9%

Best verified: Muse Wonder Full · 79.0%

Gap12.1 behindReported by Z.AIShown only
Terminal-Bench 2.188.2%

Best verified: SWE-2 · 92.8%

Gap4.6 behindReported by Z.AIShown only
terminalBench328.3%

Best verified: DeepSeek V4.1 Flash 1017 · 30%

Gap1.7 behindReported by Z.AIShown only
NL2Repo58%

Best verified: DeepSeek V4.1 Flash 1017 · 68.2%

Gap10.2 behindReported by Z.AIShown only
ProgramBenchProgramBench: Can Language Models Rebuild Programs From Scratch?19.0%

Best verified: Claude Opus 5 · 93.0%

Gap74 behindReported by Z.AIShown only
FrontierSWE78.1%

Best verified: Kimi K3 · 81.2%

Gap3.1 behindReported by Z.AIShown only
sweMarathon42.5%

Best verified: Step 5 Preview · 72.7%

Gap30.2 behindReported by Z.AIShown only
PostTrainBench (lab runs)PostTrainBench lab-run results with no stated version39.8%

Best verified: GLM-5.3 · 39.8%

GapHighest listedReported by Z.AIShown only
VulcanBench v378.3%

Best verified: Grok 4.5 · 89.9%

Gap11.6 behindRun by VulcanBench v3Shown only
OpenHarmony BenchOpenHarmony Bench v1.060.8%

Best verified: Qwen3.8 Max · 60.8%

GapBest verifiedRun by OpenHarmony BenchShown only
SWE-bench (Vals)SWE-bench, Vals AI run95.4%

Best verified: Claude Opus 5 · 97.0%

Gap1.6 behindIndependent run: Vals AIShown only
Bug Hunt Bench19.0 fixes

Best verified: Claude Fable 5.2 · 58.0 fixes

Gap39 behindRun by Bug Hunt BenchShown only
PostTrainBenchPostTrainBench v1.234.5%

Best verified: Claude Opus 5.5 · 43.8%

Gap9.3 behindRun by PostTrainBenchShown only
Agentic17 rows
Agentic benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
Terminal-Bench 4.141.20%

Best verified: Claude Opus 5.5 · 65.80%

Gap24.6 behindRun by Terminal-Bench 4.1Counts9% ref. weight
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run71.5%

Best verified: Claude Opus 5.5 · 87.6%

Gap16.1 behindIndependent run: Vals AICounts4% ref. weight
Terminal-Bench 2.188.2%

Best verified: SWE-2 · 92.8%

Gap4.6 behindReported by Z.AIShown only
AutomationBench48.2%

Best verified: MiniMax M3.1 Flash · 62.2%

Gap14 behindReported by Z.AIShown only
Toolathlon-Verified73.0%

Best verified: Claude Opus 5 · 80.6%

Gap7.6 behindReported by Z.AIShown only
Agents' Last Exam28.5%

Best verified: GPT-6.1 Terra · 60.0%

Gap31.5 behindReported by Z.AIShown only
HLE w/ toolsHumanity's Last Exam with tools62.5%

Best verified: Kimi K3.1 Pro · 70.0%

Gap7.5 behindReported by Z.AIShown only
FinchFinch (FinWorkBench): Finance & Accounting across Spreadsheet-Centric Enterprise Workflows65.1%

Best verified: Mistral Large 4 · 73.3%

Gap8.2 behindReported by a third partyShown only
b3 Agent Securityb3: Backbone Breaker Benchmark93.0%

Best verified: Mistral Large 4 · 97.1%

Gap4.1 behindReported by a third partyShown only
Cyber Misuse RefusalRefusal of harmful cyber requests (JailbreakBench, StrongREJECT, AgentHarm)87.0%

Best verified: Mistral Large 4 · 96.6%

Gap9.6 behindReported by a third partyShown only
terminalBench328.3%

Best verified: DeepSeek V4.1 Flash 1017 · 30%

Gap1.7 behindReported by Z.AIShown only
CyberGym84.5%

Best verified: MiMo-V2.6-Flash · 95.1%

Gap10.6 behindReported by Z.AIShown only
ExploitGym15.0%

Best verified: Gemini 4 Carbon · 53.5%

Gap38.5 behindReported by Z.AIShown only
Hermes IndexHermes Index: agentic capability and cost per task inside Hermes Agent39.2%

Best verified: Claude Opus 5.5 · 63.3%

Gap24.1 behindRun by Hermes IndexShown only
Hermes BenchHermes Bench: 150 agent tasks graded on the files and state the agent leaves behind71.8%

Best verified: Claude Opus 5.5 · 76.7%

Gap4.9 behindRun by Hermes BenchShown only
MazeBenchMazeBench: visual spatial reasoning in a 3D open world0%

Best verified: GPT-6 Astra · 23%

Gap23 behindRun by MazeBenchShown only
CWE-benchCWE-bench v242.0%

Best verified: Claude Fable 5.2 · 75.0%

Gap33 behindRun by CWE-benchShown only
Knowledge2 rows
Knowledge benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
MMLU-Pro (Vals)MMLU-Pro, Vals AI run86.8%

Best verified: Claude Fable 5.1 · 92.4%

Gap5.6 behindIndependent run: Vals AICounts6% ref. weight
GPQA Diamond (Vals)GPQA Diamond, Vals AI run88.1%

Best verified: Gemini 3.1 Pro · 95.5%

Gap7.4 behindIndependent run: Vals AICounts2% ref. weight
Inst. Following1 row
Inst. Following benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
Gray Swan IPI (15 attempts)Gray Swan Indirect Prompt Injection, attack success at 15 attempts31.5%

Best verified: Gemini 4 Carbon · 0.0%

Gap31.5 behindLab-reported, from another publisher’s documentShown only
Math2 rows
Math benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-368.772%

Best verified: Claude Fable 5.2 · 95.088%

Gap26.3 behindRun by FrontierMath v2 (Tiers 1-3)CountsWeighted 30%
FrontierMath v2 (Tier 4)FrontierMath v2 Tier 429.268%

Best verified: Claude Fable 5.2 · 100.000%

Gap70.7 behindRun by FrontierMath v2 (Tier 4)CountsWeighted 10%
External signals13 rows
External signals benchmark results, best verified comparison, who ran each test, and whether it counts toward a rank
FrontierPhysicsEnd-to-End Physics Research Benchmark for AI Agents4.2%

Best verified: Claude Opus 5.5 · 27.4%

Gap23.2 behindRun by FrontierPhysicsShown only
Cookie-BenchCookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation80.4%

Best verified: Claude Opus 5.5 · 91.5%

Gap11.1 behindLab-reported, from another publisher’s documentShown only
BlenderWorld (text-only)BlenderWorld: Benchmarking Visual Agents for 3D Creation from Real-World References (text-only subset)69.7%

Best verified: Claude Opus 5.5 · 86.1%

Gap16.4 behindLab-reported, from another publisher’s documentShown only
NYT ConnectionsNYT Connections LLM Benchmark (extended version)74.2%

Best verified: Gemini 4 Argon · 98.6%

Gap24.4 behindRun by NYT ConnectionsShown only
Joke Arena1537

Best verified: GPT-6 Astra · 1564

Gap27 behindRun by Joke ArenaShown only
Landing Page Arena1477

Best verified: GPT-6.1 Sol · 1691

Gap214 behindRun by Landing Page ArenaShown only
Code2Video1496

Best verified: Claude Opus 5.5 · 1585

Gap89 behindRun by Code2VideoShown only
StealthMarkStealthMark Intelligence Benchmarks (overall score)52.9

Best verified: GPT-6 Astra · 74.6

Gap21.7 behindRun by StealthMarkShown only
BabaIsBenchBabaIsBench (Baba Is You intro levels, pass@1)96%

Best verified: Claude Opus 5 · 100%

Gap4 behindRun by BabaIsBenchShown only
MaxxBenchMaxxBench (mutated DeepSWE tasks)56.7%

Best verified: Claude Opus 5.5 · 76.4%

Gap19.7 behindRun by MaxxBenchShown only
ExploitBenchExploitBench v8-bench54%

Best verified: GPT-6 Astra · 100%

Gap45.6 behindReported by Z.AIShown only
Chess PuzzlesChess Puzzles (Epoch AI)37.0%

Best verified: Claude Fable 5.2 · 79.0%

Gap42 behindRun by Chess PuzzlesShown only
Mystery Game PuzzlesMystery Game Puzzles (Epoch AI)33.0%

Best verified: Claude Fable 5.2 · 90.0%

Gap57 behindRun by Mystery Game PuzzlesShown only

Outlined bars are lab-reported. Filled bars are benchmark-run or independent.

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Feb 12, 2026

    GLM-5

    Score 54.2 · $1 / $3.2

  2. Apr 7, 2026

    GLM-5.1

    Score 55.5 · $1.4 / $4.4

  3. Jun 16, 2026

    GLM-5.2

    Score 59.7 · $1.4 / $4.4

  4. Aug 14, 2026 · you are here

    GLM-5.3

    Score 61.0 · Price not listed

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
zai-org/GLM-5.3Z.AI GLM-5.3 model card
Context window
1MZ.AI GLM-5.3 model card
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
textZ.AI GLM-5.3 guide
Output modalities
textZ.AI GLM-5.3 guide
Parameters
Not sourced yet
Availability
Z.AI now publishes the 753.33B-parameter FP8 checkpoint under the custom GLM-5.3 License. The model card documents local serving with SGLang, vLLM, TokenSpeed, Transformers, KTransformers, Unsloth, and Ascend-compatible inference frameworks.
Cloud regions
Not tracked yet
Lifecycle
Current
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing recordZ.AI GLM-5.3 model card
Self-host
GLM-5.3 needs ~640GB VRAM (8× NVIDIA H100 (80GB)).
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

GLM-5.3 ranks #48 of 215 on the public leaderboard with a score of 60.98/100. Its source-verified position is #32 of 81.

GLM-5.3 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.

Z.AI now publishes the 753.33B-parameter FP8 checkpoint under the custom GLM-5.3 License. The model card documents local serving with SGLang, vLLM, TokenSpeed, Transformers, KTransformers, Unsloth, and Ascend-compatible inference frameworks.

GLM-5.3 has cybersecurity benchmark results. The cybersecurity benchmark comparison shows them beside other models, one security job at a time, with the access route for restricted models.

GLM-5.3 sits in the GLM-5 family with GLM-5, GLM-5.2, GLM-5.1, GLM-5 (Reasoning), GLM-5.3-Flash, GLM-5-Turbo, GLM-5V-Turbo. Its explicit predecessor is GLM-5.2. 51 of 906 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Agentic at #14, while its lowest eligible position is Knowledge at #48. particularly useful for coding agents, browser research, and computer-use workflows.

Last updated November 12, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Where to run GLM-5.3 by API

8 hosts publish a per-token price for it. OpenRouter has the lowest output rate, $1.40 per million tokens. Prices read from each host’s own pages on November 11, 2026.

Inference hosts for GLM-5.3 with input, output and cached-input prices per million tokens.
HostInput /1MOutput /1MCached /1MContextListed speed
OpenRouter$0.07$1.40$0.0691.05M—
DeepInfra$0.56$2.50$0.131.05M—
Baseten$1.40$4.40$0.14——
Fireworks AI$1.40$4.40$0.26——
Nebius Token Factory$1.40$4.40$0.261.05M455 tok/s
Novita AI$1.40$4.40$0.261.05M—
Together AI$1.40$4.40$0.261.05M—
SambaNova$2.80$8.80$0.14——
Compare inference hosts

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5.3
API / mo (DeepInfra)$2,297
Self-host / mo$27,962
Break-evenNot reached at 70% load
Model the full break-even

Questions

How does GLM-5.3 perform overall in AI benchmarks?

GLM-5.3 ranks #48 out of 215 models on the public BenchAlign leaderboard, with a score of 60.98/100. Its evidence status is Supported, and this profile shows 51 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is GLM-5.3 good for knowledge and understanding?

GLM-5.3 ranks #48 out of 180 eligible models for knowledge and understanding, with a public category score of 59.9/100. 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.

Is GLM-5.3 good for coding and programming?

GLM-5.3 ranks #33 out of 162 eligible models for coding and programming, with a public category score of 53.7/100. 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.

Is GLM-5.3 good for mathematics?

GLM-5.3 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.

Is GLM-5.3 good for agentic tool use and computer tasks?

GLM-5.3 ranks #14 out of 135 eligible models for agentic tool use and computer tasks, with a public category score of 66.7/100. 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.

Is GLM-5.3 good for instruction following?

GLM-5.3 has source-displayable benchmark coverage for instruction following, 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.

Is GLM-5.3 open source?

GLM-5.3 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.

Which sibling models are related to GLM-5.3?

GLM-5.3 belongs to the GLM-5 family. Related tracked variants include GLM-5, GLM-5.2, GLM-5.1, plus 4 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.

Does GLM-5.3 have full benchmark coverage on BenchLM?

No. GLM-5.3 currently has 79 source-displayable rows. 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.

What is the context window size of GLM-5.3?

GLM-5.3 has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

Compare GLM-5.3 with every tracked model1030 comparisons