Data as of October 17, 2026 · How the score is built
Released Dec 22, 2025200K context
GLM-4.7
Decision readingGLM-4.7 scores 48.9 out of 100 and ranks #103 of 224. This profile shows 17 source-displayable benchmark rows; its strongest eligible category is Coding at #72. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released Dec 22, 2025 — see all recent releases
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.
Capability
48.9/100
field median 49.6#103 of 224 ranked models
Public
#103of 224
Verified #61 of 88
Price
Self-hosted; infrastructure cost varies
input median $0.75No comparable first-party hosted token rate
Speed
38tok/s
field median 92 tok/sFirst token 55.52 s
Context
200Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Coding ranks #72. Particularly well-suited for software development and code generation tasks.
Validate before choosing
17 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Source-linked · 17 displayable benchmark rows
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 #91 of 128Percentile 29thWeight 22%4 benchmarksVerified | 31.6 | 4 benchmarks | Verified | |||
| CodingRank #72 of 150Percentile 52ndWeight 20%6 benchmarksVerified | 37.2 | 6 benchmarks | Verified | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeRank #100 of 184Percentile 46thWeight 12%5 benchmarksMixed sources | 44.1 | 5 benchmarks | Mixed sources | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathRank Not rankedWeight 5%2 benchmarksMixed sources | 1.8 | 2 benchmarks | Mixed sources |
17 of 681 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.
- Agentic4/4 verified
- Coding6/6 verified
- ReasoningNot measured
- MultimodalNot measured
- Knowledge3/5 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- Math1/2 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-4.7 category percentile values
- Agentic29th percentile
- Coding52nd percentile
- ReasoningNot eligible
- MultimodalNot eligible
- Knowledge46th percentile
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#91/128
- Coding#72/150
- ReasoningNot ranked
- MultimodalNot ranked
- Knowledge#100/184
- MultilingualNot ranked
- Inst. FollowingNot ranked
- MathNot ranked
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.
Coding6 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| LiveCodeBench (Vals)LiveCodeBench, Vals AI run | Score82.2% | Versus best verified row Best verified: Claude Fable 5.1 · 90.5% | Gap8.3 behind | Weight8% ref. weight | |
| SWE-Rebench | Score58.7% | Versus best verified row Best verified: Claude Opus 4.6 · 65.3% | Gap6.6 behind | Weight5% ref. weight | Benchmark exact |
| Terminal-Bench 2.0 | Score41.0% | Versus best verified row Best verified: GPT-5.5 · 82.0% | Gap41 behind | WeightScored in Agentic | Provider exact |
| SWE-bench VerifiedSoftware Engineering Benchmark Verified | Score73.8% | Versus best verified row Best verified: Claude Opus 5 · 96% | Gap22.2 behind | WeightDisplay only | Provider exact |
| LiveCodeBench v6 | Score84.9% | Versus best verified row Best verified: Sakana Fugu-Ultra · 93.2% | Gap8.3 behind | WeightDisplay only | Provider exact |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score69.4% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap27.6 behind | WeightDisplay only | Verified |
Agentic4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| BrowseComp | Score52% | Versus best verified row Best verified: Atria Dawn Preview · 92.5% | Gap40.5 behind | Weight8% ref. weight | Provider exact |
| Terminal-Bench 2.0 | Score41% | Versus best verified row Best verified: GPT-5.5 · 82% | Gap41 behind | Weight7% ref. weight | 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 |
Knowledge5 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 | Weight44% ref. weight | Provider exact |
| MMLU-ProMassive Multitask Language Understanding Professional | Score84.3% | Versus best verified row Best verified: Qwen3.7 Max · 89.6% | Gap5.3 behind | Weight6% ref. weight | Reported |
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | Score82.7% | Versus best verified row Best verified: Claude Fable 5.1 · 92.4% | Gap9.7 behind | Weight6% ref. weight | Verified |
| GPQAGraduate-Level Google-Proof Q&A | Score85.7% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap10.3 behind | Weight3% ref. weight | Reported |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | Score80.1% | Versus best verified row Best verified: Gemini 3.1 Pro · 95.5% | Gap15.4 behind | Weight2% ref. weight |
Math2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME 2025American Invitational Mathematics Examination 2025 | Score95.7% | Versus best verified row Best verified: GPT-5.2 · 100% | Gap4.3 behind | WeightDisplay only | Reported |
| HMMT Feb 2025Harvard-MIT Mathematics Tournament February 2025 | Score97.1% | Versus best verified row Best verified: GLM-4.7 · 97.1% | GapBest verified | WeightDisplay only | Provider exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 17 rowsLineage
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.
Base entry
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
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 #103 of 224 on the public leaderboard with a score of 48.85/100. Its source-verified position is #61 of 88.
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.
17 of 681 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #72, while its lowest eligible position is Knowledge at #100. particularly well-suited for software development and code generation tasks.
Last updated October 17, 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.
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 parametersQuestions
How does GLM-4.7 perform overall in AI benchmarks?
GLM-4.7 ranks #103 out of 224 models on the public BenchAlign leaderboard, with a score of 48.85/100. Its evidence status is Supported, and this profile shows 17 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 ranks #100 out of 184 eligible models for knowledge and understanding, with a public category score of 44.1/100. 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 coding and programming?
GLM-4.7 ranks #72 out of 150 eligible models for coding and programming, with a public category score of 37.2/100. 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 #91 out of 128 eligible models for agentic tool use and computer tasks, with a public category score of 31.6/100. 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 32 source-displayable rows across 681 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.