Capability
53.9/100
field median 58.1
#107 of 232 ranked models
GLM-4.6
EstablishedReleased Sep 1, 2025Open WeightReasoning200K context
Released Sep 1, 2025 — see all recent releases
Decision reading
GLM-4.6 scores 53.9 out of 100 and ranks #107 of 232. This profile shows 3 source-displayable benchmark rows; its strongest eligible category is Coding at #84. No comparable first-party API price is published in the catalog.
Data as of September 4, 2026 · How the score is built
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Strongest published evidence
Coding ranks #84. Particularly well-suited for software development and code generation tasks.
Validate before choosing
3 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
Not listed
input median $1
No comparable first-party hosted token rate
Speed
65tok/s
field median 92 tok/s
First token 2.68 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-4.6 category percentile values
- AgenticNot eligible
- 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
- AgenticNot ranked
- Coding#84/183
- 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.
- AgenticNot measured
- Coding1/1 verified
- ReasoningNot measured
- KnowledgeNot measured
- Math2/2 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 |
|---|---|---|---|---|---|---|
| AgenticWeight 22%0 benchmarksNot measured | Not measured | Not ranked | Not available | 22% | 0 benchmarks | Not measured |
| CodingRank #84 of 183Percentile 54thWeight 20%1 benchmarkVerified | 48.8 | #84 of 183 | 54th | 20% | 1 benchmark | Verified |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| MathRank Not rankedWeight 5%2 benchmarksVerified | 27.5 | Not ranked | Not available | 5% | 2 benchmarks | Verified |
| 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.
Coding1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Vibe Code BenchVibe Code Bench v1.1 | Score3.09% | Versus best verified row Best verified: Claude Opus 4.7 · 71.00% | Gap67.9 behind | WeightDisplay only | Benchmark exact |
Math2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score3.819% | Versus best verified row Best verified: GPT-5.6 Sol · 89.000% | Gap85.2 behind | WeightWeighted 30% | Benchmark exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score2.128% | Versus best verified row Best verified: GPT-6 Astra · 97.600% | Gap95.5 behind | WeightWeighted 10% | Benchmark exact |
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.
Sep 1, 2025 · you are here
GLM-4.6Score 53.9 · 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-4.6 ranks #107 of 232 on the public leaderboard with a score of 53.94/100. Its source-verified position is #70 of 130.
GLM-4.6 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.
Tracked from Vals.ai Vibe Code Bench v1.1 exact leaderboard data. Kept unranked until broader benchmark coverage is available.
3 of 422 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #84. particularly well-suited for software development and code generation tasks.
Frequently asked questions
How does GLM-4.6 perform overall in AI benchmarks?
GLM-4.6 ranks #107 out of 232 models on the public BenchAlign leaderboard, with a score of 53.94/100. Its evidence status is Supported, and this profile shows 3 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is GLM-4.6 good for coding and programming?
GLM-4.6 ranks #84 out of 183 eligible models for coding and programming, with a public category score of 48.8/100. 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-4.6 good for mathematics?
GLM-4.6 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-4.6 open source?
GLM-4.6 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-4.6 have full benchmark coverage on BenchLM?
No. GLM-4.6 currently has 14 source-displayable rows across 422 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-4.6?
GLM-4.6 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-4.6 with every tracked model410 comparisons
Last updated September 4, 2026. Runtime fields remain blank until a sourced snapshot exists.
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