Skip to main content
BenchLM
Data

Data as of October 17, 2026 · How the score is built

EstablishedOpen WeightReasoning

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 scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #91 of 128Percentile 29thWeight 22%4 benchmarksVerified
31.6
CodingRank #72 of 150Percentile 52ndWeight 20%6 benchmarksVerified
37.2
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalWeight 12%0 benchmarksNot measured
Not measured
KnowledgeRank #100 of 184Percentile 46thWeight 12%5 benchmarksMixed sources
44.1
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathRank Not rankedWeight 5%2 benchmarksMixed sources
1.8

17 of 681 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. Agentic4/4 verified
  2. Coding6/6 verified
  3. ReasoningNot measured
  4. MultimodalNot measured
  5. Knowledge3/5 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. Math1/2 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-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

  1. Agentic#91/128
  2. Coding#72/150
  3. ReasoningNot ranked
  4. MultimodalNot ranked
  5. Knowledge#100/184
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

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
Coding benchmark values, best verified comparison, weight, and source status
LiveCodeBench (Vals)LiveCodeBench, Vals AI runScore82.2%Versus best verified row

Best verified: Claude Fable 5.1 · 90.5%

Gap8.3 behindWeight8% ref. weight
SWE-RebenchScore58.7%Versus best verified row

Best verified: Claude Opus 4.6 · 65.3%

Gap6.6 behindWeight5% ref. weight
Terminal-Bench 2.0Score41.0%Versus best verified row

Best verified: GPT-5.5 · 82.0%

Gap41 behindWeightScored in Agentic
Provider exact
SWE-bench VerifiedSoftware Engineering Benchmark VerifiedScore73.8%Versus best verified row

Best verified: Claude Opus 5 · 96%

Gap22.2 behindWeightDisplay only
Provider exact
LiveCodeBench v6Score84.9%Versus best verified row

Best verified: Sakana Fugu-Ultra · 93.2%

Gap8.3 behindWeightDisplay only
Provider exact
SWE-bench (Vals)SWE-bench, Vals AI runScore69.4%Versus best verified row

Best verified: Claude Opus 5 · 97.0%

Gap27.6 behindWeightDisplay only
Agentic4 rows
Agentic benchmark values, best verified comparison, weight, and source status
BrowseCompScore52%Versus best verified row

Best verified: Atria Dawn Preview · 92.5%

Gap40.5 behindWeight8% ref. weight
Provider exact
Terminal-Bench 2.0Score41%Versus best verified row

Best verified: GPT-5.5 · 82%

Gap41 behindWeight7% ref. weight
Provider exact
VITA-BenchScore15.5%Versus best verified row

Best verified: Qwen3.7 Max · 47.9%

Gap32.4 behindWeightDisplay only
Benchmark exact
Gert LabsGert Labs Composite Game BenchmarkScore39.95%Versus best verified row

Best verified: Claude Opus 4.8 · 72.97%

Gap33 behindWeightDisplay only
Benchmark exact
Knowledge5 rows
Knowledge benchmark values, best verified comparison, weight, and source status
HLEHumanity's Last ExamScore24.8%Versus best verified row

Best verified: Claude Fable 5.1 · 65%

Gap40.2 behindWeight44% ref. weight
Provider exact
MMLU-ProMassive Multitask Language Understanding ProfessionalScore84.3%Versus best verified row

Best verified: Qwen3.7 Max · 89.6%

Gap5.3 behindWeight6% ref. weight
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore82.7%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap9.7 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore85.7%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap10.3 behindWeight3% ref. weight
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore80.1%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap15.4 behindWeight2% ref. weight
Math2 rows
Math benchmark values, best verified comparison, weight, and source status
AIME 2025American Invitational Mathematics Examination 2025Score95.7%Versus best verified row

Best verified: GPT-5.2 · 100%

Gap4.3 behindWeightDisplay only
HMMT Feb 2025Harvard-MIT Mathematics Tournament February 2025Score97.1%Versus best verified row

Best verified: GLM-4.7 · 97.1%

GapBest verifiedWeightDisplay only
Provider exact

Bars run 0–100; the dark tick marks the best source-verified value

All 17 rows

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. Dec 22, 2025 · you are here

    GLM-4.7

    Score 48.9 · Price not listed

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 parameters

Questions

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.

Watch GLM-4.7 in the weekly brief

Get one weekly email when material rank, price, availability, or benchmark evidence changes are worth revisiting.

Read a sample issue

Join 5,600+ readers.

Compare GLM-4.7 with every tracked model953 comparisons