Intelligence
19
score
GLM-4.5 (Reasoning) scores 19 (estimated) on the Artificial Analysis Intelligence Index, placing it …
vs class avg: 25
Z AI•Open weights•July 28, 2025
GLM-4.5
Intelligence, Performance & Price Analysis
Source:Artificial Analysis scrapeType:Open weights
Input Price#56/93
$0.60
/ 1M tokens
GLM-4.5 (Reasoning) costs $0.60 per 1M input tokens (somewhat higher than average, median: $0. …
Output Price#53/93
$2.20
/ 1M tokens
GLM-4.5 (Reasoning) costs $0.60 per 1M input tokens (somewhat higher than average, median: $0. …
Speed
not provided
This profile does not provide this metric.
Latency
not provided
This profile does not provide this metric.
Decision Overview
Ethen's intelligence-driven routing assessment for this model
Overall verdict
Capable everyday model
Good for routine tasks; route complex reasoning and premium workloads to stronger models.
Best for
- Routine Q&A
- Content summarization
- Classification
- Light coding
Weak or unsuitable
- Complex reasoning
- Agentic workflows
- Research-grade analysis
- Predictable high-volume throughput
Routing recommendation
1
Cheaper routes for predictable extraction, labeling, or summarization
2
Cache-backed reuse for repeated prompts
Cost pressure
Moderate pricing — $0.60 / M input, $2.20 / M output. Costs are manageable, but volume should still be reviewed.
Model-fit matrix
Use this model where capability pays for itself; route away where volume, latency, or budget dominate.
| Use case | Fit | Reason | Routing note |
|---|---|---|---|
| Complex reasoning & agentic workflows | Moderate | Intelligence score 19 handles routine reasoning but may struggle with open-ended agentic tasks. | Route simpler sub-tasks here; keep hard reasoning on a stronger model. |
| Cost-sensitive pipelines | Moderate | Output pricing at $2.20 is premium. Route high-volume simple tasks to cheaper alternatives. | Use only for high-value tasks; route simple queries to budget models. |
Cost pressure
MediumPricing is moderate — input $0.60, output $2.20. Costs accumulate at volume but are manageable for valuable tasks.
Cheaper substitutes
- Cheaper routes for predictable extraction, labeling, or summarization
Route away when
- Predictable high-volume throughput
- Non-critical classification and extraction
Highlights
Key benchmark results at a glance.
Intelligence
Intelligence position
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
GLM-4.5 (Reasoning) scores 19 (estimated) on the Artificial Analysis Intelligence Index, placing it below average among other open weight models of similar size (median: 25).
Speed
Speed position
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Higher throughput supports faster interactive use.
Context, Cost & Pricing
Price/value position
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Pricing is moderate — input $0.60, output $2.20. Costs accumulate at volume but are manageable for valuable tasks.
Intelligence & Quality
Benchmark scores and quality indices measuring model capability.
AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Artificial Analysis Openness Index: Score
Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open) · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Intelligence
Artificial Analysis Intelligence Index · Higher is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Speed & Throughput
Output tokens per second and generation throughput.
Speed
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Speed should be read together with latency and cost; fast output does not always mean better user-perceived performance.
Context, Cost & Pricing
Context window size, cost per task, and token pricing.
Context Window
Context window: tokens limit · Higher is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Larger context windows enable complex multi-turn tasks but may increase latency and cost for long inputs.
Cost per Intelligence Index Task
Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost per Task
Weighted average cost (USD) per Intelligence Index task · Lower is better · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Cost to Run Artificial Analysis Intelligence Index
Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Higher values indicate stronger overall capability. Bars show how models compare on composite intelligence metrics.
Ethen Gateway should route away from this model when the task is simple, repetitive, or likely to generate many output tokens.
Pricing: Cache Hit, Input, and Output
Price (USD per M Tokens) · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Architecture & Scale
Model size, parameters, and architectural details.
Model Size: Total and Active Parameters
Comparison between total model parameters and parameters active during inference · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Larger models generally perform better but cost more to serve. Active parameter count matters for inference efficiency.
Specifications
Technical Specifications
GLM-4.5 (Reasoning) scores 19 (estimated) on the Artificial Analysis Intelligence Index, placing it below average among other open weight models of similar size (median: 25). GLM-4.5 (Reasoning) costs $0.60 per 1M input tokens (somewhat higher than average, median: $0.59) and $2.20 per 1M output tokens (better than average, median: $2.20), based on the median across providers serving the model.
GLM-4.5
- Model type
- Open weights
- Reasoning
- Yes
- Input modalities
- GLM-4.5 (Reasoning) supports text input.
- Output modalities
- GLM-4.5 (Reasoning) supports text output.
- Context window
- 130k tokens
- Open weights / source
- Yes, GLM-4.5 (Reasoning) is open weights. The model weights are publicly available and can be downloaded for self-hosting.
- Parameters
- GLM-4.5 (Reasoning) has 355 billion parameters (32 billion active).
- Active parameters
- GLM-4.5 (Reasoning) is a Mixture of Experts (MoE) model with 355 billion total parameters, but only 32 billion active parameters are used during inference.
- License
- GLM-4.5 (Reasoning) is released under the MIT license. This license allows commercial use.
- API availability
- Yes, GLM-4.5 (Reasoning) is available via API through 1 provider.
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for GLM-4.5.
Benchmark values are preserved as normalized; only layout, disclosure ordering, and typography are adjusted for readability.
Source file: md/scrape/artificial_analysis_model_pages/artificialanalysis_ai__models__glm-4.5__2285718ed1.html · Canonical URL: https://artificialanalysis.ai/models/glm-4.5
Normalized at: 2026-07-08T03:24:56+00:00 Technical specs, comparison summary, and FAQs are passed through from the normalized data without rewriting.
Ranks shown on summary cards come directly from the normalized profile. No rank or class display is synthesized in the renderer.
Visible FAQ answers and FAQ JSON-LD are emitted from the same normalized FAQ array, so structured data stays aligned with the rendered accordion.
Speed: https://artificialanalysis.ai/models/glm-4.5
Frequently Asked Questions
GLM-4.5 (Reasoning) was released on July 28, 2025.
GLM-4.5 (Reasoning) was created by Z AI.
GLM-4.5 (Reasoning) scores 19 (estimated) on the Artificial Analysis Intelligence Index, placing it below average among other open weight models of similar size (median: 25).