Capability
66.9/100
field median 57.5
#22 of 216 ranked models
Superseded.Z.AI has newer models in this line:GLM-5.2
Model profile · Z.AI
GLM-5.1
Released Apr 7, 2026 — see all recent releases
GLM-5.1 scores 66.9 out of 100 and ranks #22 of 216. This profile shows 20 source-displayable benchmark rows; its strongest eligible category is Mathematics at #3. API pricing is $1.4 input and $4.4 output per million tokens.
Data as of August 7, 2026 · How the score is built
Strongest published evidence
Mathematics ranks #3. Particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Validate before choosing
20 published rows leave some tracked benchmark slots empty. Agentic is its lowest eligible category at #57.
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
$1.40input / $4.40 output
input median $1
blended $2.90
Speed
74tok/s
field median 89 tok/s
First token 52.74 s
Context
203Ktokens
field median 201,500
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-5.1 category percentile values
- Agentic57th percentile
- Coding73rd percentile
- ReasoningNot eligible
- KnowledgeNot eligible
- Math67th percentile
- MultilingualNot eligible
- MultimodalNot eligible
- Instruction followingNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#57/132
- Coding#37/132
- ReasoningNot ranked
- KnowledgeNot ranked
- Math#3/7
- MultilingualNot ranked
- MultimodalNot ranked
- Inst. FollowingNot ranked
Top decileTop quartileMid-fieldNot eligible
What it costs to get this score
Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.
The chart opens on the current model. Scroll horizontally to inspect the full price axis.
Current modelGLM-5.1 · 66.9 score · $2.90 blended per million tokens
Horizontal: blended price per million tokens, log scale · Vertical: public score
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.
- Agentic8/8 verified
- Coding4/4 verified
- ReasoningNot measured
- Knowledge2/2 verified
- Math6/6 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
- 203K
- 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
- Superseded
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- GLM-5.1 needs ~640GB VRAM (8× NVIDIA H100 (80GB)).
- 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.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
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 #57 of 132Percentile 57thWeight 22%8 benchmarksVerified | 48.8 | #57 of 132 | 57th | 22% | 8 benchmarks | Verified |
| CodingRank #37 of 132Percentile 73rdWeight 20%4 benchmarksVerified | 55.3 | #37 of 132 | 73rd | 20% | 4 benchmarks | Verified |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeRank Not rankedWeight 12%2 benchmarksVerified | 77.6 | Not ranked | Not available | 12% | 2 benchmarks | Verified |
| MathRank #3 of 7Percentile 67thWeight 5%6 benchmarksVerified | 64.6 | #3 of 7 | 67th | 5% | 6 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.
Coding4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-Rebench | Score62.7% | Versus best verified row Best verified: Claude Opus 4.6 · 65.3% | Gap2.6 behind | WeightWeighted 20% | Benchmark exact |
| SWE-bench Pro | Score58.4% | Versus best verified row Best verified: Claude Mythos 5 · 80.3% | Gap21.9 behind | WeightWeighted 10% | Provider exact |
| NL2Repo | Score42.7% | Versus best verified row Best verified: Qwen3.8 Max · 55.9% | Gap13.2 behind | WeightDisplay only | Provider exact |
| Vibe Code BenchVibe Code Bench v1.1 | Score31.46% | Versus best verified row Best verified: Claude Opus 4.7 · 71.00% | Gap39.5 behind | WeightDisplay only | Benchmark exact |
Agentic8 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.0 | Score63.5% | Versus best verified row Best verified: GPT-5.6 Sol · 91.9% | Gap28.4 behind | WeightWeighted 38% | Provider exact |
| BrowseComp | Score68% | Versus best verified row Best verified: GPT-5.6 Sol · 92.2% | Gap24.2 behind | WeightWeighted 28% | Provider exact |
| τ³-bench resultsτ³-Bench Tool-Agent-User Evaluation | Score70.6% | Versus best verified row Best verified: Mistral Medium 3.5 128B · 91.4% | Gap20.8 behind | WeightDisplay only | Provider exact |
| MCP Atlas | Score71.8% | Versus best verified row Best verified: Muse Spark 1.1 · 88.1% | Gap16.3 behind | WeightDisplay only | Provider exact |
| CyberGym | Score68.7% | Versus best verified row Best verified: Fugu Cyber · 86.9% | Gap18.2 behind | WeightDisplay only | Benchmark exact |
| Claw-Eval | Score62.3% | Versus best verified row Best verified: Ornith-1.0-397B · 77.1% | Gap14.8 behind | WeightDisplay only | Benchmark exact |
| Gert LabsGert Labs Composite Game Benchmark | Score60.11% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap12.9 behind | WeightDisplay only | Benchmark exact |
| ResearchClawBench | Score18.2% | Versus best verified row Best verified: Claude Opus 4.8 · 21.1% | Gap2.9 behind | WeightDisplay only | Benchmark exact |
Knowledge2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| HLEHumanity's Last Exam | Score52.3% | Versus best verified row Best verified: Claude Opus 5 · 64.7% | Gap12.4 behind | WeightWeighted 45% | Provider exact |
| GPQA-DGPQA Diamond | Score86.2% | Versus best verified row Best verified: Sakana Fugu-Ultra · 95.5% | Gap9.3 behind | WeightDisplay only | Provider exact |
Math6 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score33.448% | Versus best verified row Best verified: GPT-5.6 Sol · 89.000% | Gap55.6 behind | WeightWeighted 30% | Benchmark exact |
| AIME26AIME 2026 | Score95.3% | Versus best verified row Best verified: GLM-5.2 · 99.2% | Gap3.9 behind | WeightWeighted 25% | Provider exact |
| HMMT Feb 2026Harvard-MIT Mathematics Tournament February 2026 | Score82.6% | Versus best verified row Best verified: Qwen3.7 Max · 97.1% | Gap14.5 behind | WeightWeighted 25% | Provider exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score12.500% | Versus best verified row Best verified: GPT-5.6 Sol · 83.000% | Gap70.5 behind | WeightWeighted 10% | Benchmark exact |
| HMMT Nov 2025Harvard-MIT Mathematics Tournament November 2025 | Score94.0% | Versus best verified row Best verified: Qwen3.6 Plus · 94.6% | Gap0.6 behind | WeightDisplay only | Provider exact |
| MMAnswerBench | Score83.8% | Versus best verified row Best verified: GLM-5.2 · 91.0% | Gap7.2 behind | WeightDisplay only | Provider exact |
Lineage
The sequence follows explicit supersedes links. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Mar 1, 2026
GLM-5Score 65.3 · $1 / $3.2
Apr 7, 2026 · you are here
GLM-5.1Score 66.9 · $1.4 / $4.4
Jun 16, 2026
GLM-5.2Score 63.0 · $1.4 / $4.4
snapshot · 5.1
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-5.1 ranks #22 of 216 on the public leaderboard with a score of 66.9/100. Its source-verified position is #19 of 104.
GLM-5.1 is a open weight model with a 203K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Official flagship agentic model released April 7, 2026. Benchmarks sourced from Z.AI official performance report.
GLM-5.1 sits in the GLM-5 family with GLM-5, GLM-5.2, GLM-5 (Reasoning), GLM-5-Turbo, GLM-5V-Turbo. Its explicit predecessor is GLM-5. 20 of 381 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Mathematics at #3, while its lowest eligible position is Agentic at #57. particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Radar
Full release historyGLM-5.1 release history
GLM 5.1 released
Z.AI · Model release
Source confirmed
Frequently asked questions
How does GLM-5.1 perform overall in AI benchmarks?
GLM-5.1 ranks #22 out of 216 models on the public BenchAlign leaderboard, with a score of 66.9/100. Its evidence status is Supported, and this profile shows 20 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is GLM-5.1 good for knowledge and understanding?
GLM-5.1 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-5.1 good for coding and programming?
GLM-5.1 ranks #37 out of 132 eligible models for coding and programming, with a public category score of 55.3/100. 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-5.1 good for mathematics?
GLM-5.1 ranks #3 out of 7 eligible models for mathematics, with a public category score of 64.6/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.
Is GLM-5.1 good for agentic tool use and computer tasks?
GLM-5.1 ranks #57 out of 132 eligible models for agentic tool use and computer tasks, 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-5.1 open source?
GLM-5.1 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-5.1?
GLM-5.1 belongs to the GLM-5 family. Related tracked variants include GLM-5, GLM-5.2, GLM-5 (Reasoning), plus 2 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-5.1 have full benchmark coverage on BenchLM?
No. GLM-5.1 currently has 36 source-displayable rows across 381 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-5.1?
GLM-5.1 has a reported context window of 203K 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.
Last updated August 7, 2026. Runtime fields remain blank until a sourced snapshot exists.
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