Skip to main content
BenchLM
Data

Data as of October 10, 2026 · How the score is built

CurrentOpen WeightReasoning

Released Aug 26, 20261M contextGLM-5.3-Flash model card

GLM-5.3-Flash

Decision readingGLM-5.3-Flash scores 57.4 out of 100 and ranks #55 of 218. This profile shows 21 source-displayable benchmark rows; its strongest eligible category is Multimodal & Grounded at #14. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.

Released Aug 26, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

57.4/100

field median 50.1#55 of 218 ranked models

Public

#55of 218

Verified —

Price

Self-hosted; infrastructure cost varies

input median $0.95No comparable first-party hosted token rate

Speed

Not measured

field median 97 tok/sTime to first token not measured

Context

1Mtokens

field median 256,000Maximum output length is tracked separately

Strongest published evidence

Multimodal & Grounded ranks #14. Particularly strong for screenshots, documents, charts, and grounded multimodal workflows.

Validate before choosing

21 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.

Source-linked · 21 displayable benchmark rows

Follow model changes

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #34 of 123Percentile 73rdWeight 22%6 benchmarksVerified
55.7
CodingRank #42 of 146Percentile 72ndWeight 20%8 benchmarksVerified
49.3
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalRank #14 of 54Percentile 75thWeight 12%5 benchmarksVerified
84.0
KnowledgeRank #43 of 177Percentile 76thWeight 12%2 benchmarksVerified
60.5
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathWeight 5%0 benchmarksNot measured
Not measured

21 of 667 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic6/6 verified
  2. Coding8/8 verified
  3. ReasoningNot measured
  4. Multimodal5/5 verified
  5. Knowledge2/2 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. MathNot measured
Verified sourceProvisionalNot measured

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

GLM-5.3-Flash category percentile values

  • Agentic73rd percentile
  • Coding72nd percentile
  • ReasoningNot eligible
  • Multimodal75th percentile
  • Knowledge76th percentile
  • MultilingualNot eligible
  • Instruction followingNot eligible
  • MathNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#34/123
  2. Coding#42/146
  3. ReasoningNot ranked
  4. Multimodal#14/54
  5. Knowledge#43/177
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding8 rows
Coding benchmark values, best verified comparison, weight, and source status
DeepSWEScore63.4%Versus best verified row

Best verified: Gemini 4 Argon · 77.9%

Gap14.5 behindWeight15% ref. weight
LiveCodeBench (Vals)LiveCodeBench, Vals AI runScore80.5%Versus best verified row

Best verified: Claude Fable 5.1 · 90.5%

Gap10 behindWeight8% ref. weight
FrontierSWE v2Score18.1%Versus best verified row

Best verified: GPT-6 Astra · 65.5%

Gap47.4 behindWeight8% ref. weight
Terminal-Bench 2.1Score84.3%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap8.5 behindWeightScored in Agentic
NL2RepoScore56.3%Versus best verified row

Best verified: DeepSeek V4.1 Flash · 65.4%

Gap9.1 behindWeightDisplay only
SWE-bench (Vals)SWE-bench, Vals AI runScore92.0%Versus best verified row

Best verified: Claude Opus 5 · 97.0%

Gap5 behindWeightDisplay only
OpenHarmony BenchOpenHarmony Bench v1.0Score57.3%Versus best verified row

Best verified: Qwen3.8 Max · 60.8%

Gap3.5 behindWeightDisplay only
Bug Hunt BenchScore17.7 fixesVersus best verified row

Best verified: Claude Sonnet 5.5 · 51.3 fixes

Gap33.6 behindWeightDisplay only
Agentic6 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.1Score84.3%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap8.5 behindWeight8% ref. weight
AutomationBenchScore48.8%Versus best verified row

Best verified: DeepSeek V4.1 Flash · 54.8%

Gap6 behindWeight5% ref. weight
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI runScore62.9%Versus best verified row

Best verified: GPT-6 Astra · 87.3%

Gap24.4 behindWeight3% ref. weight
Toolathlon-VerifiedScore78.4%Versus best verified row

Best verified: Claude Opus 5 · 80.6%

Gap2.2 behindWeight3% ref. weight
Agents' Last ExamScore26.3%Versus best verified row

Best verified: GPT-6 Astra · 59.3%

Gap33 behindWeight3% ref. weight
HLE w/ toolsHumanity's Last Exam with toolsScore55.3%Versus best verified row

Best verified: Claude Opus 5.5 · 67.7%

Gap12.4 behindWeight3% ref. weight
Multimodal5 rows
Multimodal benchmark values, best verified comparison, weight, and source status
OfficeQA ProScore62.4%Versus best verified row

Best verified: Claude Opus 5.5 · 67.7%

Gap5.3 behindWeightWeighted 25%
CharXivCharXiv ReasoningScore89.4%Versus best verified row

Best verified: Qwen3.8 Max · 93.5%

Gap4.1 behindWeightWeighted 20%
Chartography (tools)Chartography with image and code toolsScore78.0%Versus best verified row

Best verified: Claude Sonnet 5.5 · 90.2%

Gap12.2 behindWeightDisplay only
BabyVisionScore53.4%Versus best verified row

Best verified: Qwen3.8 Max · 82.0%

Gap28.6 behindWeightDisplay only
MMVUMultimodal Multi-disciplinary Video UnderstandingScore80.5%Versus best verified row

Best verified: Qwen3.8 Max · 82.4%

Gap1.9 behindWeightDisplay only
Knowledge2 rows
Knowledge benchmark values, best verified comparison, weight, and source status
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore86.1%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap6.3 behindWeight6% ref. weight
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore86.4%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap9.1 behindWeight2% ref. weight

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Oct 1, 2025

    GLM-4.7-Flash

    Score 39.4 · Price not listed

  2. Aug 26, 2026 · you are here

    GLM-5.3-Flash

    Score 57.4 · Price not listed

Radar

GLM-5.3-Flash release history

Full release history

Radar confirmed these at the source. Use GLM-5.3-Flash in your work? Explore Radar to follow supported changes and choose your alerts.

Radar

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
glm-5.3-flashGLM-5.3-Flash model card
Context window
1MGLM-5.3-Flash model card
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
Not sourced yet
Output modalities
Not sourced yet
Parameters
Not sourced yet
Availability
Z.AI publishes the FP8 GLM-5.3-Flash checkpoint on Hugging Face under MIT and documents local serving with SGLang, vLLM, TokenSpeed, and KTransformers. The launch post says the exact glm-5.3-flash API model is available to GLM Coding Plan users and through Z.AI API services, but the public pricing table does not yet list a standalone per-token rate for this SKU.
Cloud regions
Not tracked yet
Lifecycle
Current
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Not documented in the pricing recordGLM-5.3-Flash model card
Self-host
Open weights available; hardware estimate not sourced
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

GLM-5.3-Flash ranks #55 of 218 on the public leaderboard with a score of 57.36/100. It does not yet have enough sourced coverage for a verified position.

GLM-5.3-Flash is a open weight model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Z.AI publishes the FP8 GLM-5.3-Flash checkpoint on Hugging Face under MIT and documents local serving with SGLang, vLLM, TokenSpeed, and KTransformers. The launch post says the exact glm-5.3-flash API model is available to GLM Coding Plan users and through Z.AI API services, but the public pricing table does not yet list a standalone per-token rate for this SKU.

GLM-5.3-Flash sits in the GLM-5 family with GLM-5, GLM-5.2, GLM-5.1, GLM-5.3, GLM-5-Turbo, GLM-5V-Turbo, GLM-5 (Reasoning). Its explicit predecessor is GLM-4.7-Flash. 21 of 667 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Multimodal & Grounded at #14, while its lowest eligible position is Knowledge at #43. particularly strong for screenshots, documents, charts, and grounded multimodal workflows.

Last updated October 10, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.

Estimate VRAM from known parameters

Questions

How does GLM-5.3-Flash perform overall in AI benchmarks?

GLM-5.3-Flash ranks #55 out of 218 models on the public BenchAlign leaderboard, with a score of 57.36/100. Its evidence status is Estimated, and this profile shows 21 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is GLM-5.3-Flash good for knowledge and understanding?

GLM-5.3-Flash ranks #43 out of 177 eligible models for knowledge and understanding, with a public category score of 60.5/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GLM-5.3-Flash good for coding and programming?

GLM-5.3-Flash ranks #42 out of 146 eligible models for coding and programming, with a public category score of 49.3/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GLM-5.3-Flash good for agentic tool use and computer tasks?

GLM-5.3-Flash ranks #34 out of 123 eligible models for agentic tool use and computer tasks, with a public category score of 55.7/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GLM-5.3-Flash good for multimodal and grounded tasks?

GLM-5.3-Flash ranks #14 out of 54 eligible models for multimodal and grounded tasks, with a public category score of 84/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GLM-5.3-Flash open source?

GLM-5.3-Flash is an open-weight model from Z.AI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

Which sibling models are related to GLM-5.3-Flash?

GLM-5.3-Flash belongs to the GLM-5 family. Related tracked variants include GLM-5, GLM-5.2, GLM-5.1, plus 4 more. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does GLM-5.3-Flash have full benchmark coverage on BenchLM?

No. GLM-5.3-Flash currently has 39 source-displayable rows across 667 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of GLM-5.3-Flash?

GLM-5.3-Flash has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

Watch GLM-5.3-Flash in the weekly brief

Get one weekly email when material rank, price, availability, or benchmark evidence changes are worth revisiting.

Read a sample issue

Join 5,500+ readers.

Compare GLM-5.3-Flash with every tracked model888 comparisons