Intelligence
35
score
GLM-5.1 (Non-reasoning) scores 35 (estimated) on the Artificial Analysis Intelligence Index, …
vs class avg: 17
Z AI•Open weights•April 7, 2026
GLM-5.1
Intelligence, Performance & Price Analysis
Source:Artificial Analysis scrapeType:Open weights
Speed
54.3
output tokens/sec
GLM-5.1 (Non-reasoning) generates output at 54.3 tokens per second (based on the median across …
Latency
1.80s
TTFT
GLM-5.1 (Non-reasoning) has a time to first token (TTFT) of 1. …
Input Price
$1.39
/ 1M tokens
GLM-5.1 (Non-reasoning) costs $1.39 per 1M input tokens (at the higher end, median: $0.60) and $4. …
Output Price
$4.40
/ 1M tokens
GLM-5.1 (Non-reasoning) costs $1.39 per 1M input tokens (at the higher end, median: $0.60) and $4. …
Decision Overview
Ethen's intelligence-driven routing assessment for this model
Overall verdict
Frontier research / complex reasoning
Reserve this model for the hardest requests; simpler or repetitive work should stay on cheaper routes.
Best for
- Complex reasoning
- Research synthesis
- Hard coding tasks
- Multi-step agentic workflows
- High-value tasks where quality outweighs cost
Weak or unsuitable
- High-volume chat
- Simple classification
- Cost-sensitive pipelines
- Routine high-volume generation
- Simple extraction or labeling
Routing recommendation
1
Cheaper routes for predictable extraction, labeling, or summarization
2
Cache-backed reuse for repeated prompts
Cost pressure
Premium pricing — $1.39 / M input, $4.40 / M output. Use cheaper routes for repetitive or low-risk work.
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 | Excellent | Intelligence score 35 places this model among top performers. Suitable for multi-step analysis and agentic loops. | Route complex tasks here; reserve simpler queries for cheaper models. |
| High-volume chat & customer-facing | Strong | Output speed 54.3 tokens/sec is adequate for chat. | Suitable for chat; monitor latency under concurrent load. |
| Latency-sensitive applications | Strong | TTFT 1.80s — adequate latency for most interactive use cases. | Suitable for real-time; test with your specific workload. |
| Cost-sensitive pipelines | Moderate | Output pricing at $4.40 is premium. Route high-volume simple tasks to cheaper alternatives. | Use only for high-value tasks; route simple queries to budget models. |
Cost pressure
HighPricing is premium — input $1.39, output $4.40. This model is expensive for high-volume or output-heavy workloads.
Cheaper substitutes
- Lower-cost models for repetitive or high-volume work
- Cache-backed reuse for repeated prompts
Route away when
- Output-heavy tasks
- High-frequency chat at scale
- Simple queries that cheaper models handle well
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-5.1 (Non-reasoning) scores 35 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other open weight non-reasoning models of similar size (median: 17).
Speed
Speed position
Output tokens per second · Higher is better · Evaluation results measured independently by Artificial Analysis
GLM-5.1 (Non-reasoning) generates output at 54.3 tokens per second (based on the median across providers serving the model), which is below average compared to other open weight non-reasoning models of similar size (median: 58.3 t/s).
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 premium — input $1.39, output $4.40. This model is expensive for high-volume or output-heavy workloads.
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.
Output 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.
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.
Latency & Response Time
Time to first token and end-to-end response latency.
End-to-End Response Time
Seconds to output 500 tokens, including reasoning model 'thinking' time · 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.
Bars intentionally show many models at once so the page feels like a benchmark report rather than a dashboard.
Latency: Time To First Answer Token
Seconds to first answer token received · Accounts for reasoning model 'thinking' time · Evaluation results measured independently by Artificial Analysis
Source and methodology are summarized below
Chart source and provenance are listed in Methodology & sources below.
Latency includes thinking / reasoning time when applicable. Lower is better for interactive use cases.
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.
Time per Intelligence Index Task
Weighted average decode time (minutes) per task; excludes TTFT and overhead time · 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.
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-5.1 (Non-reasoning) scores 35 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other open weight non-reasoning models of similar size (median: 17). GLM-5.1 (Non-reasoning) generates output at 54.3 tokens per second (based on the median across providers serving the model), which is below average compared to other open weight non-reasoning models of similar size (median: 58.3 t/s). GLM-5.1 (Non-reasoning) costs $1.39 per 1M input tokens (at the higher end, median: $0.60) and $4.40 per 1M output tokens (at the higher end, median: $2.40), based on the median across providers serving the model.
GLM-5.1
- Model type
- Open weights
- Reasoning
- No
- Input modalities
- GLM-5.1 (Non-reasoning) supports text input.
- Output modalities
- GLM-5.1 (Non-reasoning) supports text output.
- Context window
- 200k tokens
- Open weights / source
- Yes, GLM-5.1 (Non-reasoning) is open weights. The model weights are publicly available and can be downloaded for self-hosting.
- Parameters
- GLM-5.1 (Non-reasoning) has 744 billion parameters (40 billion active).
- Active parameters
- GLM-5.1 (Non-reasoning) is a Mixture of Experts (MoE) model with 744 billion total parameters, but only 40 billion active parameters are used during inference.
- License
- GLM-5.1 (Non-reasoning) is released under the Mit license. This license allows commercial use.
- API availability
- Yes, GLM-5.1 (Non-reasoning) is available via API through 8 providers.
Methodology & Provenance
This page is rendered from the normalized profile and page JSON for GLM-5.1.
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-5-1-non-reasoning__6241d67ad1.html · Canonical URL: https://artificialanalysis.ai/models/glm-5-1-non-reasoning
Normalized at: 2026-07-08T03:24:56+00:00 Technical specs, comparison summary, and FAQs are passed through from the normalized data without rewriting.
Visible FAQ answers and FAQ JSON-LD are emitted from the same normalized FAQ array, so structured data stays aligned with the rendered accordion.
Time per Intelligence Index Task: https://artificialanalysis.ai/models/glm-5-1-non-reasoning
Frequently Asked Questions
GLM-5.1 (Non-reasoning) was released on April 7, 2026.
GLM-5.1 (Non-reasoning) was created by Z AI.
GLM-5.1 (Non-reasoning) scores 35 (estimated) on the Artificial Analysis Intelligence Index, placing it well above average among other open weight non-reasoning models of similar size (median: 17).