GLM-4.6
Not tested yet
Language modelActive
GLM-4.6
Z.ai
- Released
- September 29, 2025
- Data date
- September 8, 2026
- Intelligence
- Overall score
- Speed
- 66.3 tokens/sOutput throughput
- Price
- Input · Output / 1M tokens
- Input
- Text
- Output
- Text
No published category-wide ranking is available for this model.
Entries 1-12 of 21. Page 1 of 2.
Published benchmarks
Each chart compares models in the same documented test and highlights the current model.
Finance
2 measurementsCorpFin v2
Overall
Higher is better50 modelsGLM-4.6: Rank 41 of 50Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-corp-fin-v2:2:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 50
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Latency in seconds: 78.061. Cost per task in USD: 0.04461. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-09-04-46dad6684551. Source payload hash: 46dad668455190ee4239b7c743d00d81c6fd3127e0d87123c518ec107c13b44d. Parser version: vals-astro-v2. Imported at: 2026-09-04
TaxEval v2
Overall
Higher is better54 modelsGLM-4.6: Rank 51 of 54Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-tax-eval-v2:2:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 54
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-01. Latency in seconds: 64.149. Cost per task in USD: 0.00928. Source ID: vals-ai. Evidence capture ID: vals-tax_eval_v2-2026-09-04-f0e140ab16fc. Source payload hash: f0e140ab16fcb52041f4f87d2872273c03ca8014fdb69b3da9afd01b0f1e77ec. Parser version: vals-astro-v2. Imported at: 2026-09-04
GPQA Diamond
Overall
Higher is better54 modelsGLM-4.6: Rank 42 of 54Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-gpqa:1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 54
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Latency in seconds: 474.834. Cost per task in USD: 0.01541. Source ID: vals-ai. Evidence capture ID: vals-gpqa-2026-09-04-fc438726f0d8. Source payload hash: fc438726f0d8523d320f02d75ae372a3fa38b28b36f70fe318d119f55153560d. Parser version: vals-astro-v2. Imported at: 2026-09-04
Coding
3 measurementsIOI
Overall
Higher is better24 modelsGLM-4.6: Rank 21 of 24Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-ioi:1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 24
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-08-09. Latency in seconds: 7105.854. Cost per task in USD: 1.516537. Source ID: vals-ai. Evidence capture ID: vals-ioi-2026-09-04-c6f99e8d7130. Source payload hash: c6f99e8d713092dbfdc9cbcfd6b376fbb7209970714528eab31f6b541d90f20c. Parser version: vals-astro-v2. Imported at: 2026-09-04
LiveCodeBench v6
Overall
Higher is better56 modelsGLM-4.6: Rank 37 of 56Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-lcb:1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 56
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Latency in seconds: 236.176. Cost per task in USD: 0.026909. Source ID: vals-ai. Evidence capture ID: vals-lcb-2026-09-04-95ca83387127. Source payload hash: 95ca83387127df287e9a6d928c411c98c9b65b4cefeee46eb4008ca1521f5ed4. Parser version: vals-astro-v2. Imported at: 2026-09-04
Vibe Code Bench v1.1
Overall
Higher is better41 modelsGLM-4.6: Rank 39 of 41Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-vibe-code-bench:1.1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 41
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-03. Harness: OpenHands. Latency in seconds: 10002.446. Cost per task in USD: 10.845475. Source ID: vals-ai. Evidence capture ID: vals-vibe-code-2026-09-04-36e999b7e462. Source payload hash: 36e999b7e46223c2148ae838adbbb53b2b213d96364826223f702be2a0806e36. Parser version: vals-astro-v2. Imported at: 2026-09-04
LegalBench
Overall
Higher is better53 modelsGLM-4.6: Rank 38 of 53Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-legal-bench:1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 53
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Latency in seconds: 29.819. Source ID: vals-ai. Evidence capture ID: vals-legal_bench-2026-09-04-ff81649923fb. Source payload hash: ff81649923fb684ff37d5f557fd320ba5ab433927831065ed781a94548f480ef. Parser version: vals-astro-v2. Imported at: 2026-09-04
MedQA
Overall
Higher is better27 modelsGLM-4.6: Rank 15 of 27Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-medqa:1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 27
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Latency in seconds: 28.734. Cost per task in USD: 0.004252. Source ID: vals-ai. Evidence capture ID: vals-medqa-2026-09-04-de083a524dbf. Source payload hash: de083a524dbf0fa1c2da5cf0a06bd001693d2426690681ffac4cd93ccf3fbeba. Parser version: vals-astro-v2. Imported at: 2026-09-04
MGSM
Overall
Higher is better21 modelsGLM-4.6: Rank 13 of 21Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-mgsm:1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 21
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Latency in seconds: 34.307. Cost per task in USD: 0.004302. Source ID: vals-ai. Evidence capture ID: vals-mgsm-2026-09-04-8454c4c2a5d6. Source payload hash: 8454c4c2a5d6a567e4c17ce67f5f6eb9f3589353348190378d4b83b2df182750. Parser version: vals-astro-v2. Imported at: 2026-09-04
MMLU-Pro
Overall
Higher is better55 modelsGLM-4.6: Rank 40 of 55Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-mmlu-pro:1:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 55
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Latency in seconds: 46.996. Cost per task in USD: 0.006572. Source ID: vals-ai. Evidence capture ID: vals-mmlu_pro-2026-09-04-94eef672a38c. Source payload hash: 94eef672a38c8146fc1334fecbd173c083343c88de0f3047ae7d034776227e0f. Parser version: vals-astro-v2. Imported at: 2026-09-04
Agent tasks
2 measurementsAgent Poker Bench
Overall
Higher is better6 modelsGLM-4.6: Rank 6 of 6Data date: September 4, 2026
Scale: 0 rating - 1,103.18 rating
Methodology and context
- Comparison cohort
- vals-poker-agent:1:overall
- Metric
- TrueSkill rating
- Scale
- No upper bound is documented. The chart spans the recorded values and zero.
- Participants
- 6
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2025-12-23. Latency in seconds: 42.55249388614798. Standard error: 0. Cost per task in USD: 0.019527262827635273. Source ID: vals-ai. Evidence capture ID: vals-poker_agent-2026-09-04-2bf0dd7f88e8. Source payload hash: 2bf0dd7f88e861267360c45be912a71a2bde6ad507e691c297717570f949ee61. Parser version: vals-astro-v2. Imported at: 2026-09-04
Terminal-Bench 2.0
Overall
Higher is better24 modelsGLM-4.6: Rank 19 of 24Data date: September 4, 2026
Scale: 0% - 100%
Methodology and context
- Comparison cohort
- vals-terminal-bench-2:2.0:overall
- Metric
- accuracy
- Scale
- 0-100
- Participants
- 24
- Methodology and context
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-06-04. Latency in seconds: 576.085. Cost per task in USD: 0.14355. Source ID: vals-ai. Evidence capture ID: vals-terminal-bench-2-2026-09-04-12d4c34f346d. Source payload hash: 12d4c34f346dae49eab98600ff4962559491e99c55a589a5f988b82342903659. Parser version: vals-astro-v2. Imported at: 2026-09-04
Recorded measurement details
Every observation retains its source value and published test conditions.
CorpFin v2: Overall (Show measurement, test conditions, and source)
- Source value
- 56.838
- Score
- 56.838
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 2
- Category
- finance
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-corp-fin-v2:2:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-08-12. Latency in seconds: 78.061. Cost per task in USD: 0.04461. Source ID: vals-ai. Evidence capture ID: vals-corp_fin_v2-2026-09-04-46dad6684551. Source payload hash: 46dad668455190ee4239b7c743d00d81c6fd3127e0d87123c518ec107c13b44d. Parser version: vals-astro-v2. Imported at: 2026-09-04
GPQA Diamond: Overall (Show measurement, test conditions, and source)
- Source value
- 74.495
- Score
- 74.495
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- reasoning
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-gpqa:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Latency in seconds: 474.834. Cost per task in USD: 0.01541. Source ID: vals-ai. Evidence capture ID: vals-gpqa-2026-09-04-fc438726f0d8. Source payload hash: fc438726f0d8523d320f02d75ae372a3fa38b28b36f70fe318d119f55153560d. Parser version: vals-astro-v2. Imported at: 2026-09-04
IOI: Overall (Show measurement, test conditions, and source)
- Source value
- 4.334
- Score
- 4.334
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- coding
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-ioi:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-08-09. Latency in seconds: 7105.854. Cost per task in USD: 1.516537. Source ID: vals-ai. Evidence capture ID: vals-ioi-2026-09-04-c6f99e8d7130. Source payload hash: c6f99e8d713092dbfdc9cbcfd6b376fbb7209970714528eab31f6b541d90f20c. Parser version: vals-astro-v2. Imported at: 2026-09-04
LiveCodeBench v6: Overall (Show measurement, test conditions, and source)
- Source value
- 81.036
- Score
- 81.036
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- coding
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-lcb:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Latency in seconds: 236.176. Cost per task in USD: 0.026909. Source ID: vals-ai. Evidence capture ID: vals-lcb-2026-09-04-95ca83387127. Source payload hash: 95ca83387127df287e9a6d928c411c98c9b65b4cefeee46eb4008ca1521f5ed4. Parser version: vals-astro-v2. Imported at: 2026-09-04
LegalBench: Overall (Show measurement, test conditions, and source)
- Source value
- 79.608
- Score
- 79.608
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- legal
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-legal-bench:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Latency in seconds: 29.819. Source ID: vals-ai. Evidence capture ID: vals-legal_bench-2026-09-04-ff81649923fb. Source payload hash: ff81649923fb684ff37d5f557fd320ba5ab433927831065ed781a94548f480ef. Parser version: vals-astro-v2. Imported at: 2026-09-04
MedQA: Overall (Show measurement, test conditions, and source)
- Source value
- 92.225
- Score
- 92.225
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- health
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-medqa:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-04-16. Latency in seconds: 28.734. Cost per task in USD: 0.004252. Source ID: vals-ai. Evidence capture ID: vals-medqa-2026-09-04-de083a524dbf. Source payload hash: de083a524dbf0fa1c2da5cf0a06bd001693d2426690681ffac4cd93ccf3fbeba. Parser version: vals-astro-v2. Imported at: 2026-09-04
MGSM: Overall (Show measurement, test conditions, and source)
- Source value
- 89.746
- Score
- 89.746
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- math
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-mgsm:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-01-09. Latency in seconds: 34.307. Cost per task in USD: 0.004302. Source ID: vals-ai. Evidence capture ID: vals-mgsm-2026-09-04-8454c4c2a5d6. Source payload hash: 8454c4c2a5d6a567e4c17ce67f5f6eb9f3589353348190378d4b83b2df182750. Parser version: vals-astro-v2. Imported at: 2026-09-04
MMLU-Pro: Overall (Show measurement, test conditions, and source)
- Source value
- 82.203
- Score
- 82.203
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1
- Category
- knowledge
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-mmlu-pro:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-09-01. Latency in seconds: 46.996. Cost per task in USD: 0.006572. Source ID: vals-ai. Evidence capture ID: vals-mmlu_pro-2026-09-04-94eef672a38c. Source payload hash: 94eef672a38c8146fc1334fecbd173c083343c88de0f3047ae7d034776227e0f. Parser version: vals-astro-v2. Imported at: 2026-09-04
Agent Poker Bench: Overall (Show measurement, test conditions, and source)
- Source value
- 945.7558288256936
- Score
- 945.7558288256936
- Metric
- TrueSkill rating
- Unit
- rating
- Benchmark version
- 1
- Category
- agentic
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-poker-agent:1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2025-12-23. Latency in seconds: 42.55249388614798. Standard error: 0. Cost per task in USD: 0.019527262827635273. Source ID: vals-ai. Evidence capture ID: vals-poker_agent-2026-09-04-2bf0dd7f88e8. Source payload hash: 2bf0dd7f88e861267360c45be912a71a2bde6ad507e691c297717570f949ee61. Parser version: vals-astro-v2. Imported at: 2026-09-04
TaxEval v2: Overall (Show measurement, test conditions, and source)
- Source value
- 66.235
- Score
- 66.235
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 2
- Category
- finance
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-tax-eval-v2:2:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-01. Latency in seconds: 64.149. Cost per task in USD: 0.00928. Source ID: vals-ai. Evidence capture ID: vals-tax_eval_v2-2026-09-04-f0e140ab16fc. Source payload hash: f0e140ab16fcb52041f4f87d2872273c03ca8014fdb69b3da9afd01b0f1e77ec. Parser version: vals-astro-v2. Imported at: 2026-09-04
Terminal-Bench 2.0: Overall (Show measurement, test conditions, and source)
- Source value
- 28.09
- Score
- 28.09
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 2.0
- Category
- agentic
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-terminal-bench-2:2.0:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: public. Leaderboard date: 2026-06-04. Latency in seconds: 576.085. Cost per task in USD: 0.14355. Source ID: vals-ai. Evidence capture ID: vals-terminal-bench-2-2026-09-04-12d4c34f346d. Source payload hash: 12d4c34f346dae49eab98600ff4962559491e99c55a589a5f988b82342903659. Parser version: vals-astro-v2. Imported at: 2026-09-04
Vibe Code Bench v1.1: Overall (Show measurement, test conditions, and source)
- Source value
- 3.09
- Score
- 3.09
- Metric
- accuracy
- Unit
- %
- Benchmark version
- 1.1
- Category
- coding
- Task
- overall
- Task label
- Overall
- Direction
- Higher is better
- Comparison cohort
- vals-vibe-code-bench:1.1:overall
- Source type
- independent-evaluator
- Evaluator
- Vals AI
- Status
- active
- Retrieved at
- 2026-09-04
- Methodology
- Source model ID: zai/glm-4.6. Source model name: GLM-4.6. Evaluator: Vals AI. Dataset type: private. Leaderboard date: 2026-09-03. Harness: OpenHands. Latency in seconds: 10002.446. Cost per task in USD: 10.845475. Source ID: vals-ai. Evidence capture ID: vals-vibe-code-2026-09-04-36e999b7e462. Source payload hash: 36e999b7e46223c2148ae838adbbb53b2b213d96364826223f702be2a0806e36. Parser version: vals-astro-v2. Imported at: 2026-09-04
- Comparison methodology
- Harness: OpenHands
Our model tests
Community tests you can inspect
Compare how the models respond to the same prompt. Each test shows the first attempt, with no subsequent fixes to the generated code. These results do not contribute to an overall score.
Pelican on a bicycle
A community classic for free-form SVG drawing.
Prompt and test conditions
Generate an SVG of a pelican riding a bicycle
Token limit including reasoning: 8,192 tokens
Task origin (Simon Willison)Profile
Specifications and access
Published information about this model. Existing estimates are explicitly labeled.
Additional source observations
Dated documentation and model-card observations. Provider limits, native context and extended context can differ. Configuration notes retain the source wording.
- License
- MITSource
- Architecture
- MoE hybrid reasoning modelSource
- Rounded context label
- 200KSource
- Model configuration context
- 202,752 tokens (Native checkpoint context; distinct from the rounded model-card and serving labels.)Source
- Input
- TextSource
- Output
- TextSource
- Reasoning mode
- thinking, non-thinkingSource
- Tool calling
- YesSource
- API model ID
- glm-4.6Source
- Sampling temperature
- 1Source
- Code evaluation settings
- top_p=0.95, top_k=40Source
Pricing
Published prices
Prices apply to the stated unit. Resolution, output length, and provider can change the cost.
Cost example
100 API requests
$0.15
100 requests with 1,000 input and 500 output tokens each calculate to $0.15. Input accounts for $0.05; output accounts for $0.1.
- Input price
- $0.5 / 1M tokensBase tier for each request
- Output price
- $2 / 1M tokensBase tier for each request
The calculation uses documented token prices and no cache discount. It excludes extra tools, tax, reasoning tokens, and further hidden output tokens.
Open sourceHead-to-head comparisons
Compare this model
Each matchup compares this model with exactly one other model from the same category.
There are no published direct comparisons for this model yet.
Evidence
Sources and data date
Every statement links to its underlying documentation or leaderboard.
- Speed
Median across serving providers, Non-reasoning. Output speed after the first streaming chunk, not total response time. Classified as deprecated by Artificial Analysis. Artificial Analysis maintains only the default 10K-input workload there. Retrieved September 8, 2026.
- Price
Base API prices without caching or batch discounts. Higher context tiers and other rates are listed under Costs.
- Input
Documented modalities. Retrieved September 8, 2026.
- Output
Documented modalities. Retrieved September 8, 2026.
Research date September 8, 2026. 5 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication.
Open evidence questions and research notes (original wording)
Not found in the inspected sources
parameters-billions. Raw card does not state GLM-4.6 parameter count.
Unresolved
detailed-benchmarks. Card says eight public benchmarks and embeds a table image, but the linked image returned 404 during capture.
Unresolved
release-date. No normalized release-date field.
- Model metadataSource
- API pricingSource
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Vals AI (retrieved September 4, 2026)Source
- Artificial Analysis (retrieved September 8, 2026)Source
- Model metadata (retrieved September 8, 2026)Source
- Model metadata (retrieved September 8, 2026)Source
- Model metadata (retrieved September 8, 2026)Source
- Speed measurement methodologySource