Data as of November 12, 2026 · How the score is built
GLM-5.3
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.
Where GLM-5.3 ranks by benchmark score:
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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 | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #14 of 135Percentile 90thWeight 22%17 benchmarksMixed sources | 66.7 | 17 benchmarks | Mixed sources | |||
| CodingRank #33 of 162Percentile 80thWeight 20%16 benchmarksVerified | 53.7 | 16 benchmarks | Verified | |||
| ReasoningRank #36 of 160Percentile 78thWeight 17%Not listedUnlisted sources | 57.3 | Not listed | Unlisted sources | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeRank #48 of 180Percentile 74thWeight 12%2 benchmarksVerified | 59.9 | 2 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%1 benchmarkVerified | Score pending | 1 benchmark | Verified | |||
| MathRank Not rankedWeight 5%2 benchmarksVerified | 67.0 | 2 benchmarks | Verified |
51 of 906 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow 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.
- Agentic14/17 verified
- Coding16/16 verified
- ReasoningNot measured
- MultimodalNot measured
- Knowledge2/2 verified
- MultilingualNot measured
- Inst. Following1/1 verified
- Math2/2 verified
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
- Agentic#14/135
- Coding#33/162
- Reasoning#36/160
- MultimodalNot ranked
- Knowledge#48/180
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
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.
Coding16 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| CursorBench 4.0 | 42.6% | Best verified: Gemini 4 Carbon · 60.4% | Gap17.8 behind | Run by CursorBench 4.0 | Counts10% ref. weight |
| FrontierSWE v2 | 30.2% | Best verified: Claude Fable 5.2 · 69.9% | Gap39.7 behind | Run by FrontierSWE v2 | Counts8% ref. weight |
| LiveCodeBench (Vals)LiveCodeBench, Vals AI run | 80.5% | Best verified: Claude Fable 5.1 · 90.5% | Gap10 behind | Independent run: Vals AI | Counts8% ref. weight |
| DeepSWE | 66.9% | Best verified: Muse Wonder Full · 79.0% | Gap12.1 behind | Reported by Z.AI | Shown only |
| Terminal-Bench 2.1 | 88.2% | Best verified: SWE-2 · 92.8% | Gap4.6 behind | Reported by Z.AI | Shown only |
| terminalBench3 | 28.3% | Best verified: DeepSeek V4.1 Flash 1017 · 30% | Gap1.7 behind | Reported by Z.AI | Shown only |
| NL2Repo | 58% | Best verified: DeepSeek V4.1 Flash 1017 · 68.2% | Gap10.2 behind | Reported by Z.AI | Shown only |
| ProgramBenchProgramBench: Can Language Models Rebuild Programs From Scratch? | 19.0% | Best verified: Claude Opus 5 · 93.0% | Gap74 behind | Reported by Z.AI | Shown only |
| FrontierSWE | 78.1% | Best verified: Kimi K3 · 81.2% | Gap3.1 behind | Reported by Z.AI | Shown only |
| sweMarathon | 42.5% | Best verified: Step 5 Preview · 72.7% | Gap30.2 behind | Reported by Z.AI | Shown only |
| PostTrainBench (lab runs)PostTrainBench lab-run results with no stated version | 39.8% | Best verified: GLM-5.3 · 39.8% | GapHighest listed | Reported by Z.AI | Shown only |
| VulcanBench v3 | 78.3% | Best verified: Grok 4.5 · 89.9% | Gap11.6 behind | Run by VulcanBench v3 | Shown only |
| OpenHarmony BenchOpenHarmony Bench v1.0 | 60.8% | Best verified: Qwen3.8 Max · 60.8% | GapBest verified | Run by OpenHarmony Bench | Shown only |
| SWE-bench (Vals)SWE-bench, Vals AI run | 95.4% | Best verified: Claude Opus 5 · 97.0% | Gap1.6 behind | Independent run: Vals AI | Shown only |
| Bug Hunt Bench | 19.0 fixes | Best verified: Claude Fable 5.2 · 58.0 fixes | Gap39 behind | Run by Bug Hunt Bench | Shown only |
| PostTrainBenchPostTrainBench v1.2 | 34.5% | Best verified: Claude Opus 5.5 · 43.8% | Gap9.3 behind | Run by PostTrainBench | Shown only |
Agentic17 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| Terminal-Bench 4.1 | 41.20% | Best verified: Claude Opus 5.5 · 65.80% | Gap24.6 behind | Run by Terminal-Bench 4.1 | Counts9% ref. weight |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | 71.5% | Best verified: Claude Opus 5.5 · 87.6% | Gap16.1 behind | Independent run: Vals AI | Counts4% ref. weight |
| Terminal-Bench 2.1 | 88.2% | Best verified: SWE-2 · 92.8% | Gap4.6 behind | Reported by Z.AI | Shown only |
| AutomationBench | 48.2% | Best verified: MiniMax M3.1 Flash · 62.2% | Gap14 behind | Reported by Z.AI | Shown only |
| Toolathlon-Verified | 73.0% | Best verified: Claude Opus 5 · 80.6% | Gap7.6 behind | Reported by Z.AI | Shown only |
| Agents' Last Exam | 28.5% | Best verified: GPT-6.1 Terra · 60.0% | Gap31.5 behind | Reported by Z.AI | Shown only |
| HLE w/ toolsHumanity's Last Exam with tools | 62.5% | Best verified: Kimi K3.1 Pro · 70.0% | Gap7.5 behind | Reported by Z.AI | Shown only |
| FinchFinch (FinWorkBench): Finance & Accounting across Spreadsheet-Centric Enterprise Workflows | 65.1% | Best verified: Mistral Large 4 · 73.3% | Gap8.2 behind | Reported by a third party | Shown only |
| b3 Agent Securityb3: Backbone Breaker Benchmark | 93.0% | Best verified: Mistral Large 4 · 97.1% | Gap4.1 behind | Reported by a third party | Shown only |
| Cyber Misuse RefusalRefusal of harmful cyber requests (JailbreakBench, StrongREJECT, AgentHarm) | 87.0% | Best verified: Mistral Large 4 · 96.6% | Gap9.6 behind | Reported by a third party | Shown only |
| terminalBench3 | 28.3% | Best verified: DeepSeek V4.1 Flash 1017 · 30% | Gap1.7 behind | Reported by Z.AI | Shown only |
| CyberGym | 84.5% | Best verified: MiMo-V2.6-Flash · 95.1% | Gap10.6 behind | Reported by Z.AI | Shown only |
| ExploitGym | 15.0% | Best verified: Gemini 4 Carbon · 53.5% | Gap38.5 behind | Reported by Z.AI | Shown only |
| Hermes IndexHermes Index: agentic capability and cost per task inside Hermes Agent | 39.2% | Best verified: Claude Opus 5.5 · 63.3% | Gap24.1 behind | Run by Hermes Index | Shown only |
| Hermes BenchHermes Bench: 150 agent tasks graded on the files and state the agent leaves behind | 71.8% | Best verified: Claude Opus 5.5 · 76.7% | Gap4.9 behind | Run by Hermes Bench | Shown only |
| MazeBenchMazeBench: visual spatial reasoning in a 3D open world | 0% | Best verified: GPT-6 Astra · 23% | Gap23 behind | Run by MazeBench | Shown only |
| CWE-benchCWE-bench v2 | 42.0% | Best verified: Claude Fable 5.2 · 75.0% | Gap33 behind | Run by CWE-bench | Shown only |
Knowledge2 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | 86.8% | Best verified: Claude Fable 5.1 · 92.4% | Gap5.6 behind | Independent run: Vals AI | Counts6% ref. weight |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | 88.1% | Best verified: Gemini 3.1 Pro · 95.5% | Gap7.4 behind | Independent run: Vals AI | Counts2% ref. weight |
Inst. Following1 row
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| Gray Swan IPI (15 attempts)Gray Swan Indirect Prompt Injection, attack success at 15 attempts | 31.5% | Best verified: Gemini 4 Carbon · 0.0% | Gap31.5 behind | Lab-reported, from another publisher’s document | Shown only |
Math2 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | 68.772% | Best verified: Claude Fable 5.2 · 95.088% | Gap26.3 behind | Run by FrontierMath v2 (Tiers 1-3) | CountsWeighted 30% |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | 29.268% | Best verified: Claude Fable 5.2 · 100.000% | Gap70.7 behind | Run by FrontierMath v2 (Tier 4) | CountsWeighted 10% |
External signals13 rows
| Benchmark | Score | Versus best verified row | Gap | Who ran it | Use in ranking |
|---|---|---|---|---|---|
| FrontierPhysicsEnd-to-End Physics Research Benchmark for AI Agents | 4.2% | Best verified: Claude Opus 5.5 · 27.4% | Gap23.2 behind | Run by FrontierPhysics | Shown only |
| Cookie-BenchCookie-Bench: Continuous On-screen Key Interaction Evaluation for Web Generation | 80.4% | Best verified: Claude Opus 5.5 · 91.5% | Gap11.1 behind | Lab-reported, from another publisher’s document | Shown 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 behind | Lab-reported, from another publisher’s document | Shown only |
| NYT ConnectionsNYT Connections LLM Benchmark (extended version) | 74.2% | Best verified: Gemini 4 Argon · 98.6% | Gap24.4 behind | Run by NYT Connections | Shown only |
| Joke Arena | 1537 | Best verified: GPT-6 Astra · 1564 | Gap27 behind | Run by Joke Arena | Shown only |
| Landing Page Arena | 1477 | Best verified: GPT-6.1 Sol · 1691 | Gap214 behind | Run by Landing Page Arena | Shown only |
| Code2Video | 1496 | Best verified: Claude Opus 5.5 · 1585 | Gap89 behind | Run by Code2Video | Shown only |
| StealthMarkStealthMark Intelligence Benchmarks (overall score) | 52.9 | Best verified: GPT-6 Astra · 74.6 | Gap21.7 behind | Run by StealthMark | Shown only |
| BabaIsBenchBabaIsBench (Baba Is You intro levels, pass@1) | 96% | Best verified: Claude Opus 5 · 100% | Gap4 behind | Run by BabaIsBench | Shown only |
| MaxxBenchMaxxBench (mutated DeepSWE tasks) | 56.7% | Best verified: Claude Opus 5.5 · 76.4% | Gap19.7 behind | Run by MaxxBench | Shown only |
| ExploitBenchExploitBench v8-bench | 54% | Best verified: GPT-6 Astra · 100% | Gap45.6 behind | Reported by Z.AI | Shown only |
| Chess PuzzlesChess Puzzles (Epoch AI) | 37.0% | Best verified: Claude Fable 5.2 · 79.0% | Gap42 behind | Run by Chess Puzzles | Shown only |
| Mystery Game PuzzlesMystery Game Puzzles (Epoch AI) | 33.0% | Best verified: Claude Fable 5.2 · 90.0% | Gap57 behind | Run by Mystery Game Puzzles | Shown 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.
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.
| Host | Input /1M | Output /1M | Cached /1M | Context | Listed speed |
|---|---|---|---|---|---|
| OpenRouter | $0.07 | $1.40 | $0.069 | 1.05M | — |
| DeepInfra | $0.56 | $2.50 | $0.13 | 1.05M | — |
| Baseten | $1.40 | $4.40 | $0.14 | — | — |
| Fireworks AI | $1.40 | $4.40 | $0.26 | — | — |
| Nebius Token Factory | $1.40 | $4.40 | $0.26 | 1.05M | 455 tok/s |
| Novita AI | $1.40 | $4.40 | $0.26 | 1.05M | — |
| Together AI | $1.40 | $4.40 | $0.26 | 1.05M | — |
| SambaNova | $2.80 | $8.80 | $0.14 | — | — |
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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.