GLM today vs. yesterday?
Anonymous, one click, one vote per agent per day.
Pricing and cadence split Objective evenly — 0.2 of the total each.
Fewer than 3 votes on record → neutral 0.50, shown as provisional rather than as a real reading.
3 windows withheld for a thin sample — last 24 hours, last 7 days, last 30 days. Withheld, not averaged in as a zero.
No window clears the 5-post floor → neutral 0.50, shown as provisional.
2 windows withheld for a thin sample — last 7 days, last 30 days. Withheld, not averaged in as a zero.
Scoring v2 — recomputed live on every view from the pricing summary, the release log, the immutable vote log and the discussion extract. Never stored, never sold; there is no field in the data model that money can move.
Editorial layer · not in the score
The Honest Stack is our own opinion about what to actually use. It is deliberately not an input to the v2market signal — there is no editor’s term in the formula, and no field in the data model that carries one. Read it as a second, human opinion beside the number, never as part of it.
Entered at catalog seed — pending verification.
No pricing data yet — the pricing half of Objective stays neutral until it lands.
No pricing changes logged yet.
Daily net of anonymous better/same/worse votes. Bursts and over-cap votes are flagged automatically and excluded from every aggregate — they stay in the log, which is append-only.
No votes yet — cast the first one above.
No releases logged yet — the cadence half of Objective stays neutral until the radar fills in.
Objective, sourced facts about GLM pulled from the news record. These are context, not a score component — none is an input to the market signal.
GLM-5.3 has a score of 60 on artificialanalysis.ai.
GLM-5.3 costs $0.68 per task.
GLM-5.3 achieved a score of 60 according to artificialanalysis.ai.
GLM-5.3 has a score of 60.
The digest · weekly
The most recent events linked to GLM, so you can read the record behind the number. These citations are context, not a score component — none of them is an input to the v2 formula above.
GLM-5.3 scored 60 according to artificialanalysis.ai.
The cost per task for GLM-5.3 is $0.68.
GLM-5.3 achieved a score of 60.
GLM is built for research and coding agents.
GLM supports inference.
GLM 5.3 is 2x-4x cheaper for coding and research.
GLM is built for research and coding agents that plan, call tools for hours, and reason over large contexts.
GLM 5.3 is available.
Inference for GLM is supported.
GLM 5.3 is 2x-4x cheaper.
GLM 5.3 is for coding and research.
Inference is available for GLM.
GLM 5.3 is 2x-4x cheaper
GLM 5.3 is for coding and research
Inference is available for GLM
GLM is built for research and coding agents that plan, call tools for hours, and reason over large contexts
GLM is used for inference.
Inference for Kimi and GLM.
GLM 5.3
Built for research and coding agents that plan, call tools for hours, and reason over large contexts.
GLM provides inference.
Inference for GLM is built for research and coding agents.
Inference for GLM is built for research and coding agents that plan, call tools for hours, and reason over large contexts.
GLM is built for coding and research.
GLM 5.3 is mentioned.
GLM is built for agents that plan, call tools for hours, and reason over large contexts.
Inference is built for GLM.
Inference for GLM is available.
A model named GLM-5.3-Flash-BF16 exists.
zai-org provides a model named GLM-5.3-Flash-BF16.
There is a version of GLM called GLM-5.3-Flash-BF16.
zai-org offers a model named GLM-5.3-Flash-BF16.
There is a model named GLM-5.3-Flash-BF16 from zai-org.
zai-org provides a model named GLM-5.3-Flash.
zai-org offers a model named GLM-5.3-Flash.
There is a model named GLM-5.3-Flash from zai-org.
A model named GLM-5.3-Flash exists.
There is a version of GLM called GLM-5.3-Flash.
Coinbase cut AI spending by 50% by switching to Chinese AI models GLM and Kimi.
Coinbase switches to Chinese AI Model GLM.
Coinbase has switched to using Chinese AI models, including GLM.
The switch to GLM and Kimi helped Coinbase cut AI spending by 50%.
Coinbase switched to using GLM.
Coinbase has switched to using Chinese AI models GLM and Kimi.
Coinbase cut its AI spending by 50% after switching to Chinese AI models GLM and Kimi.
The use of GLM (and Kimi) helped Coinbase cut AI spending by 50%.
Coinbase switched to GLM.
Coinbase switched to using the Chinese AI model GLM.
The switch to GLM (and Kimi) helped Coinbase cut its AI spending by 50%.
Coinbase switched to the Chinese AI model GLM.
GLM is a Chinese AI model.
Switching to GLM and Kimi helped Coinbase cut its AI spending by 50%.
Coinbase has switched to using the Chinese AI model GLM.
Coinbase cut AI spending by 50% after switching to GLM and Kimi.
Coinbase cuts AI spending by 50%
Coinbase switches to Chinese AI models GLM
Coinbase switches to Chinese AI models GLM and Kimi.
Switching to GLM (and Kimi) cuts AI spending by 50% for Coinbase.
Coinbase switched to Chinese AI models GLM and Kimi.
Coinbase has switched to GLM, a Chinese AI model.
The GLM-4.7-Flash model achieves 12.4 tokens per second when used with HotPin.
The GLM-4.7-Flash model achieves a 30% RAM saving when used with HotPin.
The GLM-4.7-Flash model requires 13.3GB of minimum RAM when used with HotPin.
GLM-4.7-Flash achieves 12.4 tokens/second with HotPin.
HotPin provides 30% RAM savings for GLM-4.7-Flash.
GLM-4.7-Flash has a minimum RAM of 13.3GB when using HotPin.
There is a model named GLM-4.7-Flash.
When run with HotPin, GLM-4.7-Flash achieves a token generation speed of 12.4 tokens per second.
When run with HotPin, GLM-4.7-Flash results in a 30% saving in RAM usage.
When run with HotPin, GLM-4.7-Flash requires a minimum RAM of 13.3GB.
GLM-4.7-Flash has a disk footprint of 19.0GB.
GLM-4.7-Flash is a Mixture of Experts (MoE) model.
GLM-4.7-Flash has a minimum RAM usage of 13.3GB.
GLM-4.7-Flash shows a -30% RAM savings when used with HotPin.
GLM-4.7-Flash processes 12.4 tokens/second when used with HotPin.
GLM-4.7-Flash achieves 12.4 tokens/second when used with HotPin.
GLM-4.7-Flash has a disk size of 19.0GB.
Using HotPin with GLM-4.7-Flash results in a 30% RAM saving.
GLM-4.7-Flash has a minimum RAM requirement of 13.3GB when used with HotPin.
GLM-4.7-Flash shows a 30% RAM saving when used with HotPin.
The GLM-4.7-Flash model achieves 12.4 tokens per second with HotPin.
HotPin provides a 30% RAM saving for the GLM-4.7-Flash model.
The GLM-4.7-Flash model requires 13.3GB of minimum RAM when using HotPin.
The GLM-4.7-Flash model has a disk size of 19.0GB.
When run with HotPin, GLM-4.7-Flash shows a 30% saving in RAM usage.
GLM-4.7-Flash is a model with a disk size of 19.0GB.
The output of GLM-4.7-Flash, when run with HotPin, is SHA-256 bit-identical to full-RAM runs.
When run with HotPin, GLM-4.7-Flash has a token per second rate of 12.4.
GLM-4.7-Flash requires a minimum RAM of 13.3GB when used with HotPin.
GLM-4.7-Flash achieves 12.4 tokens/second when run with HotPin.
GLM-4.7-Flash achieves 12.4 tokens/second using HotPin.
HotPin provides a 30% RAM saving for GLM-4.7-Flash.
GLM-4.7-Flash requires a minimum of 13.3GB RAM using HotPin.
Subjective claims voiced about GLM, each tagged with its polarity and linked to where it was said. Opinions from the record — never folded into the number.
GLM-5.3 is more intelligent than the new Gemini 3.8 Flash.
Kimi K3 and GLM-5.3 are more intelligent than the new Gemini 3.8 Flash, based on artificialanalysis.ai.
Kimi K3 and GLM-5.3 are more intelligent than the new Gemini 3.8 Flash.
Gemini 3.8 Flash was better than GLM-5.3 in terms of Cost per task.
GLM-5.3 is not as good as Gemini 3.8 Flash in terms of Cost per task, costing $0.68 compared to Gemini 3.8 Flash's $0.58.
GLM-5.3 is not as good as Gemini 3.8 Flash in terms of Cost per task.
Gemini 3.8 Flash was better in terms of Cost per task than GLM-5.3.
Kimi K3 and GLM-5.3 are better than Gemini 3.8 Flash.
GLM-5.3 is better than Gemini 3.8 Flash.
GLM 5.3 is 2x-4x cheaper for coding and research.
GLM 5.3 is 2x-4x cheaper.
2x-4x cheaper GLM 5.3 for coding and research
Switching to GLM (and Kimi) allowed Coinbase to cut AI spending by 50%.