Capability
60.8/100
field median 59.1
#70 of 230 ranked models
GLM-4.7
EstablishedReleased Oct 1, 2025Open WeightReasoning200K context
Released Oct 1, 2025 — see all recent releases
Decision reading
GLM-4.7 scores 60.8 out of 100 and ranks #70 of 230. This profile shows 13 source-displayable benchmark rows; its strongest eligible category is Agentic at #61. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Data as of September 2, 2026 · How the score is built
Strongest published evidence
Agentic ranks #61. Particularly useful for coding agents, browser research, and computer-use workflows.
Validate before choosing
13 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
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.
Price
Self-hosted; infrastructure cost varies
input median $1
No comparable first-party hosted token rate
Speed
94tok/s
field median 90 tok/s
First token 22.54 s
Context
200Ktokens
field median 256,000
Reported for this model; direct source link not stored
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-4.7 category percentile values
- Agentic58th percentile
- Coding54th percentile
- ReasoningNot eligible
- KnowledgeNot eligible
- MathNot eligible
- MultilingualNot eligible
- MultimodalNot eligible
- Instruction followingNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#61/143
- Coding#68/148
- ReasoningNot ranked
- KnowledgeNot ranked
- MathNot ranked
- MultilingualNot ranked
- MultimodalNot ranked
- Inst. FollowingNot ranked
Top decileTop quartileMid-fieldNot eligible
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.
- Agentic4/4 verified
- Coding3/3 verified
- ReasoningNot measured
- Knowledge1/3 verified
- Math2/3 verified
- MultilingualNot measured
- MultimodalNot measured
- Inst. FollowingNot measured
Verified sourceProvisionalNot measured
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
- Not published
- Context window
- 200K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Established
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
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.
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 parametersCategory 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 #61 of 143Percentile 58thWeight 22%4 benchmarksVerified | 51.4 | #61 of 143 | 58th | 22% | 4 benchmarks | Verified |
| CodingRank #68 of 148Percentile 54thWeight 20%3 benchmarksVerified | 52.8 | #68 of 148 | 54th | 20% | 3 benchmarks | Verified |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeRank Not rankedWeight 12%3 benchmarksMixed sources | 27.6 | Not ranked | Not available | 12% | 3 benchmarks | Mixed sources |
| MathRank Not rankedWeight 5%3 benchmarksMixed sources | 26.0 | Not ranked | Not available | 5% | 3 benchmarks | Mixed sources |
| 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 |
Benchmark ledger
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.
Coding3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| LiveCodeBenchLiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code | Score84.9% | Versus best verified row Best verified: Qwen3.7 Max · 91.6% | Gap6.7 behind | WeightWeighted 38% | Provider exact |
| SWE-Rebench | Score58.7% | Versus best verified row Best verified: Claude Opus 4.6 · 65.3% | Gap6.6 behind | WeightWeighted 20% | Benchmark exact |
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score73.8% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap22.2 behind | WeightWeighted 16% | Provider exact |
Agentic4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.0 | Score41% | Versus best verified row Best verified: GPT-5.6 Sol · 91.9% | Gap50.9 behind | WeightWeighted 38% | Provider exact |
| BrowseComp | Score52% | Versus best verified row Best verified: GPT-5.6 Sol · 92.2% | Gap40.2 behind | WeightWeighted 28% | Provider exact |
| VITA-Bench | Score15.5% | Versus best verified row Best verified: Qwen3.7 Max · 47.9% | Gap32.4 behind | WeightDisplay only | Benchmark exact |
| Gert LabsGert Labs Composite Game Benchmark | Score39.95% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap33 behind | WeightDisplay only | Benchmark exact |
Knowledge3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| HLEHumanity's Last Exam | Score24.8% | Versus best verified row Best verified: Claude Fable 5.1 · 65% | Gap40.2 behind | WeightWeighted 45% | Provider exact |
| MMLU-ProMassive Multitask Language Understanding Professional | Score84.3% | Versus best verified row Best verified: Qwen3.7 Max · 89.6% | Gap5.3 behind | WeightWeighted 30% | Reported |
| GPQAGraduate-Level Google-Proof Q&A | Score85.7% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap9.8 behind | WeightWeighted 7% | Reported |
Math3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score2.439% | Versus best verified row Best verified: GPT-5.6 Sol · 89.000% | Gap86.6 behind | WeightWeighted 30% | Benchmark exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score0.000% | Versus best verified row Best verified: GPT-5.6 Sol · 83.000% | Gap83 behind | WeightWeighted 10% | Benchmark exact |
| AIME 2025American Invitational Mathematics Examination 2025 | Score95.7% | Versus best verified row Best verified: MAI-Thinking-1 · 97% | Gap1.3 behind | WeightDisplay only | Reported |
Lineage
The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Oct 1, 2025 · you are here
GLM-4.7Score 60.8 · Price not listed
Base entry
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-4.7 ranks #70 of 230 on the public leaderboard with a score of 60.77/100. Its source-verified position is #38 of 105.
GLM-4.7 is a open weight model with a 200K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
13 of 416 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Agentic at #61, while its lowest eligible position is Coding at #68. particularly useful for coding agents, browser research, and computer-use workflows.
Frequently asked questions
How does GLM-4.7 perform overall in AI benchmarks?
GLM-4.7 ranks #70 out of 230 models on the public BenchAlign leaderboard, with a score of 60.77/100. Its evidence status is Supported, and this profile shows 13 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is GLM-4.7 good for knowledge and understanding?
GLM-4.7 has source-displayable benchmark coverage for knowledge and understanding, 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-4.7 good for coding and programming?
GLM-4.7 ranks #68 out of 148 eligible models for coding and programming, with a public category score of 52.8/100. 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.
Is GLM-4.7 good for mathematics?
GLM-4.7 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-4.7 good for agentic tool use and computer tasks?
GLM-4.7 ranks #61 out of 143 eligible models for agentic tool use and computer tasks, with a public category score of 51.4/100. 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.
Is GLM-4.7 open source?
GLM-4.7 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.
Does GLM-4.7 have full benchmark coverage on BenchLM?
No. GLM-4.7 currently has 30 source-displayable rows across 416 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.
What is the context window size of GLM-4.7?
GLM-4.7 has a reported context window of 200K 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.
Compare GLM-4.7 with every tracked model409 comparisons
Last updated September 2, 2026. Runtime fields remain blank until a sourced snapshot exists.
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