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How to read the charts

A chart appears only where the job is a comparison, and every chart has one sentence above it that says what it shows, computed from the same numbers. Teal marks this model, the cheapest price or a measured result; amber marks articles and changes; everything else is grey. When the data a chart needs is missing, the chart is not drawn.

Price strip

Who serves it, cheapest first. One dot per live provider on a log price axis; equal prices stack; the cheapest is teal, the median is a white tick, and the sponsor is never highlighted. Hidden with fewer than two priced providers.

Prices for the same weights vary 14×

GLM-5.3-Flash: input price per million tokens across 32 live providers.

$0.010$0.10$1.00$10.00Relace: $0.018 in / $0.14 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashOpenInference: $0.018 in / $0.11 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashSail Research: $0.050 in / $0.18 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashWafer: $0.064 in / $0.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashStreamLake: $0.075 in / $0.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashGMICloud: $0.075 in / $0.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashNear AI: $0.080 in / $0.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashReka: $0.080 in / $2.40 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashNovita: $0.084 in / $0.28 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingDeepInfra: $0.12 in / $0.40 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingInferenceNet: $0.12 in / $0.39 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashNextBit: $0.13 in / $0.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashPhala: $0.13 in / $0.42 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashDigitalOcean: $0.13 in / $0.44 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashMakora: $0.14 in / $0.47 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashTogether AI: $0.15 in / $0.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingFireworks AI: $0.15 in / $0.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingZ.ai: $0.15 in / $0.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingBaseten: $0.15 in / $0.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingParasail: $0.15 in / $0.50 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingMistral: $0.15 in / $0.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flash+9Inceptron: $0.20 in / $0.75 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashMorph: $0.25 in / $0.65 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashcheapest $0.018median $0.15
$0.010$0.10$1.00$10.00Relace: $0.018 in / $0.14 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashOpenInference: $0.018 in / $0.11 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashSail Research: $0.050 in / $0.18 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashWafer: $0.064 in / $0.20 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashStreamLake: $0.075 in / $0.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashGMICloud: $0.075 in / $0.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashNear AI: $0.080 in / $0.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashReka: $0.080 in / $2.40 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashNovita: $0.084 in / $0.28 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingDeepInfra: $0.12 in / $0.40 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingInferenceNet: $0.12 in / $0.39 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashNextBit: $0.13 in / $0.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashPhala: $0.13 in / $0.42 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashDigitalOcean: $0.13 in / $0.44 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashMakora: $0.14 in / $0.47 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashTogether AI: $0.15 in / $0.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingFireworks AI: $0.15 in / $0.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingZ.ai: $0.15 in / $0.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingBaseten: $0.15 in / $0.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/zai-org/GLM-5.3-Flash?expand[]=inferenceProviderMappingParasail: $0.15 in / $0.50 out per 1M · observed 2026-10-24 · https://docs.parasail.io/parasail-docs/billing/pricingMistral: $0.15 in / $0.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flash+9Inceptron: $0.20 in / $0.75 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashMorph: $0.25 in / $0.65 out per 1M · observed 2026-10-24 · https://openrouter.ai/z-ai/glm-5.3-flashcheapest $0.018 · median $0.15 (white tick)

OpenRouter endpoints + provider lists · observed 2026-10-25

Benchmark standing

Is this score good? A rail with a tick for every model that has a measured (third-party) row on the same benchmark and this model as a teal dot. With fewer than three measured models there is no standing to show, so the number stands alone.

#3 of 22 on SWE-bench Verified so far

moonshotai/Kimi-K2.5: the first model on the agentic-coding page whose measured row sits in the top half. Each tick is a model on this site with a measured SWE-bench Verified row; author-reported scores are not drawn.

MiniMaxAI/MiniMax-M2.5: 75.8 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-5: 72.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3.2: 70 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 69.6 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 68.2 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 64.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Thinking: 63.4 · observed 2026-10-25 · https://www.swebench.com/MiniMaxAI/MiniMax-M2: 61 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 60.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 54.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 53.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 52.2 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 51.6 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 47 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 43.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3-0324: 42 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 38 · observed 2026-10-25 · https://www.swebench.com/openai/gpt-oss-120b: 26 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 9 · observed 2026-10-25 · https://www.swebench.com/010070.8 · #3 of 22
MiniMaxAI/MiniMax-M2.5: 75.8 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-5: 72.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3.2: 70 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 69.6 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 68.2 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 64.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Thinking: 63.4 · observed 2026-10-25 · https://www.swebench.com/MiniMaxAI/MiniMax-M2: 61 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 60.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-480B-A35B-Instruct: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.6: 55.4 · observed 2026-10-25 · https://www.swebench.com/zai-org/GLM-4.5: 54.2 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 53.4 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 52.2 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen3-Coder-30B-A3B-Instruct: 51.6 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 47 · observed 2026-10-25 · https://www.swebench.com/moonshotai/Kimi-K2-Instruct: 43.8 · observed 2026-10-25 · https://www.swebench.com/deepseek-ai/DeepSeek-V3-0324: 42 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 38 · observed 2026-10-25 · https://www.swebench.com/openai/gpt-oss-120b: 26 · observed 2026-10-25 · https://www.swebench.com/Qwen/Qwen2.5-Coder-32B-Instruct: 9 · observed 2026-10-25 · https://www.swebench.com/010070.8 · #3 of 22

swebench.com leaderboard · observed 2026-10-25

Terminal-Bench: one of 2 measured models, so no standing yet

GLM-5.3-Flash on Terminal-Bench. The number alone, until a third model has a measured row.

35.76percent (accuracy, mean of trials)

source · observed 2026-10-25

Memory fit

Will it fit? Weights-only memory at INT4, FP8 and BF16 on a log scale against four tiers: a 16 GB card, a 64 GB Mac (unified memory), two 80 GB GPUs and an eight-GPU node. Always labelled as an estimate. Hidden without a parameter count.

Needs an 8×80 GB node even at INT4

GLM-5.3-Flash: about 193 GB of weights at INT4 before KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 193 GB of weights (estimate; KV cache and activations extra)INT4193 GBFP8: about 386 GB of weights (estimate; KV cache and activations extra)FP8386 GBBF16: about 771 GB of weights (estimate; KV cache and activations extra)BF16771 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 193 GB of weights (estimate; KV cache and activations extra)INT4193 GBFP8: about 386 GB of weights (estimate; KV cache and activations extra)FP8386 GBBF16: about 771 GB of weights (estimate; KV cache and activations extra)BF16771 GB

Estimate Parameters from the repo's safetensors metadata (2026-10-24). How we estimate

Lineage bar

What did the community build on it? One proportional bar: quantized builds in teal, fine-tunes, merges and adapters in greys. Hidden when nothing is built on the model yet.

245 builds on the Hub, 201 of them quantized

GLM-5.3-Flash's derivatives by kind, from the Hugging Face model tree.

201 quantized (Hugging Face model tree)32 fine-tunes (Hugging Face model tree)4 merges (Hugging Face model tree)8 adapters (Hugging Face model tree)201 quantized32 fine-tunes4 merges8 adapters
201 quantized (Hugging Face model tree)32 fine-tunes (Hugging Face model tree)4 merges (Hugging Face model tree)8 adapters (Hugging Face model tree)201 quantized32 fine-tunes4 merges8 adapters

Hugging Face model tree · snapshot 2026-10-25

Release rail

What happened since release? One dated rail: the weights as a teal dot, articles as amber triangles (each links to the piece), repo commits as grey ticks whose height counts the commits that day, and today with the provider count. Hidden with fewer than two dated events.

10 articles and 18 repo commits around the release

GLM-5.3-Flash, from the first article to today.

Hub API + repo commits + article radar · observed 2026-10-24

Change pulse

Was this a busy week? Fourteen daily columns of typed changes: new models and stage moves in teal, price, licence and context changes in amber, provider and quant changes in grey. Repo commits never count. A quiet window gets one line instead of a chart.

253 new quants, 95 new entries on the radar and 42 providers dropped in 14 days

Every model on the watch list and the release radar, by day.

2026-10-12: 1 new open model122026-10-13: no typed changes2026-10-14: 28 new entries on the radar, 34 providers dropped, 6 price moves, 4 providers added142026-10-15: no typed changes2026-10-16: no typed changes162026-10-17: 2 new open models2026-10-18: no typed changes182026-10-19: no typed changes2026-10-20: no typed changes202026-10-21: no typed changes2026-10-22: no typed changes222026-10-23: 21 providers added, 6 providers dropped, 66 new entries on the radar, 1 stage move2026-10-24: 253 new quants, 23 price moves, 1 new entry on the radar, 2 providers dropped, 1 provider added, 1 context change242026-10-25: no typed changes25new models and stage movesprice, licence, contextproviders and quants
2026-10-12: 1 new open model122026-10-13: no typed changes2026-10-14: 28 new entries on the radar, 34 providers dropped, 6 price moves, 4 providers added142026-10-15: no typed changes2026-10-16: no typed changes162026-10-17: 2 new open models2026-10-18: no typed changes182026-10-19: no typed changes2026-10-20: no typed changes202026-10-21: no typed changes2026-10-22: no typed changes222026-10-23: 21 providers added, 6 providers dropped, 66 new entries on the radar, 1 stage move2026-10-24: 253 new quants, 23 price moves, 1 new entry on the radar, 2 providers dropped, 1 provider added, 1 context change242026-10-25: no typed changes25new modelspriceproviders

events.json · 14 days to 2026-10-25

Capability strip

What is it good for? Six marks for six jobs: filled means a measured result, half means the author reported one, hollow means a structural signal, dashed means nothing yet. Each mark links to the job. Never hidden, because "nothing yet" is an answer.

Measured evidence for coding; author-reported for reasoning

GLM-5.3-Flash. Coding: measured · Tools: structural · Reasoning: author-reported · Long docs: structural · Vision: structural · Chat: structural.

Use-case evidence on the model page · observed 2026-10-24

Spark

Is it used, and is coverage growing? A short line with a teal end dot, always beside the figure it trends, never alone. Hidden with fewer than seven points; download sparks appear once the engine has kept seven nights of history.

6.4M downloads in 30 days

6.4M

Nightly signals history · 2026-10-07 to 2026-10-25

Inline bars

Scanning a column. A short log-scale bar beside the number in a table cell, so long columns of context lengths or downloads can be compared at a glance. A missing value is a dash and no bar.

ModelContextDownloads, 30 days
gpt-oss-120b 128K 2.9M
GLM-5.3-Flash 1M 6.4M
GLM-5.3 1M 2M
DeepSeek-R1 160K 1.5M
Qwen3-32B 40K 2.9M

The first five models of the agentic-coding use case. Context from the model config; downloads from the Hub snapshot 2026-10-25.

Card art and logos

Art is mood, never data. Card art identifies a family, a job or an article type at a glance; it never encodes a value. A subject without a picture gets a gradient from the same palette. The small square next to an org's name is the org's own logo from the Hub, falling back to its family's art.

A family with pinned art (Qwen)
A family without art yet: its palette's gradient (GLM)
A job (coding agents)
The ringer board: one image in four columns

zai-org · Qwen · deepseek-ai