BenchLeader

GLM 5.3 Flash

Reasoning effort

GLM 5.3 Flash is a Zhipu AI open-weights reasoning model, released 20 Aug 2026. Its best configuration ranks #79 of 775 on the BenchLeader Index at 62.8 ±3.9, in the upper half. The ± is the point: 160 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in maths (70) and lowest in agents & tools (60). At $0.238 per million tokens blended it is cheaper than most ranked models. Output speed of 77 tokens per second puts it slower than most, with a first answer in 28.6 s. It has been measured at 5 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 19 Oct 2026.

Blended price
$0.238/M
$0.150 in · $0.500 out
Output speed
77 tok/s
measured by Artificial Analysis
First answer
29 s
first token 0.61 s
Context
1M
Full answer
35 s
median, reasoning included
Cost per run
$0.284
one full Intelligence Index run
Released
20 Aug 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index63
  2. Reasoning67
  3. Coding63
  4. Agents & tools60
  5. Maths70
  6. Knowledge65
  7. Human preference67
  8. Multimodal64
  9. Long context65
  10. Composite65

Versions

Zhipu AI has shipped 3 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
GLM 5.3 Flashthis page20 Aug 202662.8#79
GLM 4.7 Flash19 Jan 202651.4#344
GLM-4.5-Flash28 Jul 2025––

Reasoning-effort configurations

The same model behaves differently depending on how much it is allowed to think. Each row is one setting, scored only on the benchmarks that were run at that setting. “Not stated” collects results from publishers that did not say which setting they used; for a reasoning model that is usually its thinking mode, but we do not assume it. Pick a setting here or at the top of the page to see its price, speed and category scores.

EffortIndexRankSpeedFirst answerChat reply costCategories
no reasoning––77 tok/s29 s$0.0002Agents & tools 44
low––77 tok/s29 s$0.0002Reasoning 44
high––77 tok/s29 s$0.0002Reasoning 55
max54.2#25477 tok/s29 s$0.0002Agents & tools 51 · Coding 52 · Knowledge 59 · Maths 51 · Reasoning 63
not statedbest62.8#7977 tok/s29 s$0.0002Agents & tools 60 · Coding 63 · Composite 65 · Human preference 67 · Knowledge 65 · Long context 65 · Maths 70 · Multimodal 64 · Reasoning 67

How its index has moved

8 Sept 2026 to 20 Oct 2026
505968
  • not stated
  • max

The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.

Benchmark results

See all LLM benchmarks. One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Metadata

Benchmarknot statedSourceTrend
AA Openness Indexnot in index44.4#27Artificial Analysis

Reasoning

Benchmarklowhighmaxnot statedSourceTrend
GPQA Diamond––90.2%#44–Epoch AI Benchmarking Hub
Arena Hard Promptseffort not stated–––1500#34Arena (LMArena)
LiveBench 2.0 Reasoningnot in indexeffort not stated–––77.6%#42LiveBench
LiveBench 1.0 Reasoningnot in indexeffort not stated–––77.6%#57LiveBench
GPQA Diamond (AA)not in indexeffort not stated–––91.2%#49Artificial Analysis
Humanity's Last Exam (AA)not in indexeffort not stated–––39.9%#85Artificial Analysis
GPQA Diamond (Vals)not in index––86.4%#41–Vals AI
ARC-AGI-147.0%#17271.8%#13291.0%#63–ARC Prize
ARC-AGI-227.9%#12750.1%#10365.8%#68–ARC Prize
CritPteffort not stated–––15.4%#88Artificial Analysis
Chess Puzzlesnot in index––21.0%#87–Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index––8.0%#118–Epoch AI Benchmarking Hub
BTF-3not in indexeffort not stated–––14.9%#1Epoch AI Benchmarking Hub

Coding

Benchmarklowhighmaxnot statedSourceTrend
SciCodeeffort not stated–––51.6%#93SciCode
FrontierCode––31.8%#31–Cognition
Arena Codingeffort not stated–––1525#27Arena (LMArena)
Arena WebDeveffort not stated–––1607#26Arena (LMArena)
LiveBench 2.0 Codingnot in indexeffort not stated–––80.6%#30LiveBench
LiveBench 1.0 Codingnot in indexeffort not stated–––79.0%#26LiveBench
SciCode (AA)not in indexeffort not stated–––51.6%#92Artificial Analysis
LiveCodeBench––80.5%#66–Vals AI
IOI––52.5%#22–Vals AI
SWE-bench (Vals)not in index––92.0%#10–Vals AI
Code Migrationnot in index––20.5%#51–Vals AI
ProgramBenchnot in index––0.0%#32–Vals AI
Vibe Code Bench 1-100not in index––16.0%#17–Vals AI
Vibe Code Bench v1.1not in index––30.8%#72–Vals AI
Surface Evolver Benchnot in index––52.5%#17–Epoch AI Benchmarking Hub
DeepSWE v1.1not in index––63.4%#30–Epoch AI Benchmarking Hub
CursorBenchnot in index26.9%#7231.1%#6236.8%#47–Cursor
ALE-Benchnot in index–303.6#117––Epoch AI Benchmarking Hub
FrontierSWEnot in index––18.1%#17–Epoch AI Benchmarking Hub
Terminal-Bench 4.0 (AA)not in indexeffort not stated–––32.8%#41Artificial Analysis
Terminal-Bench 2.1 (AA)not in indexeffort not stated–––84.3%#30Artificial Analysis
FrontierCode 1.1 (AA)not in indexeffort not stated–––41.0%#23Artificial Analysis

Agents & tools

Benchmarkno reasoningmaxnot statedSourceTrend
Terminal-Bench35.8%#52––Terminal-Bench
APEX-Agentseffort not stated––52.8%#24Mercor
Arena Agenteffort not stated––-1.5#32Arena (LMArena)
LiveBench Long Agenticnot in indexeffort not stated––28.4%#32LiveBench
LiveBench Browsernot in indexeffort not stated––84.0%#10LiveBench
LiveBench Agentic Codingnot in indexeffort not stated––56.8%#25LiveBench
Terminal-Bench 2.1 (Vals)–62.9%#38–Vals AI
GDPval-AA v2.1not in indexeffort not stated––57.1%#20Artificial Analysis
τ³-Banking (AA)effort not stated––47.2%#8Artificial Analysis
ITBench SRE (AA)not in indexeffort not stated––51.2%#4Artificial Analysis
Analyst Agent (AA)not in indexeffort not stated––25.0%#32Artificial Analysis
CyberBenchnot in index–67.9%#27–Vals AI
Finance Agent v2not in index–57.9%#14–Vals AI
Harvey's Legal Agent Benchmarknot in index–10.4%#13–Vals AI
Legal Research Benchnot in index–45.2%#17–Vals AI
SkillsBenchnot in index–40.2%#29–Vals AI
SREBenchnot in index–0.8%#26–Vals AI
Tax Agent Benchnot in index–31.6%#22–Vals AI
Terminal-Bench 4.0 (Vals)not in index–37.4%#14–Vals AI
Terminal-Bench Sciencenot in index–2.9%#32–Vals AI
AutomationBenchnot in indexeffort not stated––60.4%#33Artificial Analysis
GDP.pdf (AA)not in indexeffort not stated––15.4%#95Artificial Analysis
Harvey LAB-AA v1.1not in indexeffort not stated––0.8%#19Harvey
EnterpriseOps-Gymnot in indexeffort not stated––33.2%#36Artificial Analysis
AA-Briefcase v1.1not in indexeffort not stated––1453#37Artificial Analysis
Terminal-Bench-Science 0.1 (AA)not in indexeffort not stated––4.8%#38Artificial Analysis
CWE-Bench (AA)effort not stated––55.8%#8Artificial Analysis
DeepSec-Bench (AA)effort not stated––19.9%#11Artificial Analysis
CyberGym end-to-end (AA)effort not stated––74.0%#5Artificial Analysis

Maths

Benchmarkmaxnot statedSourceTrend
FrontierMath Tiers 1–355.8%#46–Epoch AI Benchmarking Hub
FrontierMath Tier 417.1%#55–Epoch AI Benchmarking Hub
OTIS Mock AIME93.9%#56–Epoch AI Benchmarking Hub
ProofBench21.0%#49–Vals AI
LiveBench Mathematicsnot in indexeffort not stated–81.2%#59LiveBench
Arena Mathseffort not stated–1506#8Arena (LMArena)

Knowledge

Benchmarkmaxnot statedSourceTrend
LiveBench 2.0 Data Analysisnot in indexeffort not stated–76.4%#24LiveBench
LiveBench Researchnot in indexeffort not stated–70.1%#29LiveBench
LiveBench 1.0 Data Analysisnot in indexeffort not stated–76.4%#34LiveBench
AA-Omniscienceeffort not stated–7.5#98Artificial Analysis
MMLU-Pro86.1%#47–Vals AI
LegalBench83.9%#45–Vals AI
TaxEval75.6%#16–Vals AI
Excel Modeling Benchmarknot in index55.9%#47–Vals AI
MedScribenot in index88.9%#9–Vals AI
GDP.pdfnot in index14.0%#42–Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in indexeffort not stated–27.5%#200Artificial Analysis
AA-Omniscience: non-hallucinationnot in indexeffort not stated–72.4%#34Artificial Analysis

Instruction following

Benchmarknot statedSourceTrend
LiveBench Languagenot in index77.3%#42LiveBench
LiveBench Instruction Followingnot in index52.8%#67LiveBench
Arena Instruction Followingnot in index1472#34Arena (LMArena)

Human preference

Benchmarknot statedSourceTrend
Arena Text1474#41Arena (LMArena)
Arena Creative Writingnot in index1434#67Arena (LMArena)
Arena Multi-turnnot in index1474#52Arena (LMArena)
Arena Longer Queriesnot in index1481#45Arena (LMArena)

Multimodal

Benchmarkmaxnot statedSourceTrend
Arena Visioneffort not stated–1294#24Arena (LMArena)
LiveBench Designnot in indexeffort not stated–77.2%#22LiveBench
MMMU-Pro (Vals)not in index86.0%#23–Vals AI
AA-3Dnot in indexeffort not stated–935#16Artificial Analysis

Long context

Benchmarknot statedSourceTrend
AA-LCR80.0%#77Artificial Analysis
MLCRnot in index51.1%#12Artificial Analysis

Composite

Benchmarkmaxnot statedSourceTrend
Epoch Capabilities Indexnot in indexeffort not stated–153.1#46Epoch AI Benchmarking Hub
LiveBench 2.0effort not stated–70.6%#23LiveBench
LiveBench 1.0not in indexeffort not stated–71.6%#53LiveBench
AA Intelligence Index v4.3.2effort not stated–41.8#55Artificial Analysis
Vals Indexnot in index50.9#19–Vals AI
AA Cyber Indexnot in indexeffort not stated–49.9#4Artificial Analysis

Where it wins

Benchmarks where this configuration ranks in the top five of every configuration measured.

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
Modal166 tok/s0.47 s$0.150$0.5001.0Mnvfp4
Makora127 tok/s0.72 s$0.140$0.4701.0M–
Baseten118 tok/s0.43 s$0.150$0.5001.0Mfp8
Fireworks113 tok/s0.68 s$0.225$0.7501.0M–
DigitalOcean108 tok/s0.92 s$0.131$0.4381.0M–
Baseten107 tok/s0.38 s$0.150$0.5001.0Mfp8
inference.net106 tok/s0.67 s$0.120$0.3881.0Mfp4
CoreWeave105 tok/s0.89 s$0.150$0.5001.0Mnvfp4
Friendli103 tok/s1.61 s$0.150$0.5001.0M–
Prime Intellect101 tok/s0.96 s$0.150$0.5001.0M–
Fireworks95 tok/s1.69 s$0.150$0.5001.0M–
Crusoe91 tok/s0.75 s$0.150$0.5001.0Mfp4
Parasail90 tok/s0.67 s$0.188$0.6251.0Mfp4
Reka AI89 tok/s3.10 s$0.030$2.401.0M–
Together85 tok/s1.77 s$0.150$0.5001.0M–
Parasail75 tok/s1.16 s$0.150$0.5001.0Mfp4
inference.net72 tok/s0.82 s$0.120$0.3881.0Mfp4
Wafer66 tok/s0.56 s$0.074$0.2001.0Mnvfp4
Venice63 tok/s8.76 s$0.150$0.5001.0M–
Z.ai47 tok/s4.40 s$0.150$0.5001.0Mfp8
NovitaAI45 tok/s3.63 s$0.084$0.2801.0Mfp8
AtlasCloud43 tok/s4.53 s$0.150$0.5001.0Mfp8
SiliconFlow43 tok/s8.37 s$0.150$0.5001.0Mfp8
GMICloud43 tok/s7.93 s$0.075$0.2501.0Mfp8
Inceptron41 tok/s1.13 s$0.100$0.6901.0Mfp8
StreamLake31 tok/s3.51 s$0.074$0.2461.0Mfp8
Phala27 tok/s4.88 s$0.128$0.4251.0Mnvfp4
Relace24 tok/s1.65 s$0.075$0.5001.0M–
Open Inference23 tok/s0.99 s$0.024$0.1201.0Mfp4
DeepInfra20 tok/s1.72 s$0.150$0.5001.0Mfp4
NextBit19 tok/s2.98 s$0.120$0.4001.0Mfp4
Sail Research18 tok/s1.57 s$0.090$0.1501.0Mfp4
Sail Research17 tok/s1.71 s$0.090$0.1501.0Mfp4
NEAR AI10 tok/s4.29 s$0.080$0.5001.0Mfp8
Morph9 tok/s5.26 s$0.164$0.6501.0Mfp8

Price history

Listed price per 1M tokens over time, as recorded by OpenRouter for the provider with the longest history.

$0.00$0.550$1.10$1.65$2.20Sept 26Oct 26
input outputnow $0.028 in · $0.446 out

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache at $0.030 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0002$0.000232.5 s
Summarize a 30-page report12,000 / 600$0.0021$0.001036.4 s
Code edit6,000 / 1,500$0.0017$0.001148.1 s
Agentic coding session60,000 / 4,000$0.011$0.00561.3 min
Structured extraction2,000 / 200$0.0004$0.000231.2 s

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See also

Data as of 21 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “GLM 5.3 Flash: benchmarks, pricing, speed and rank”, https://www.benchleader.com/models/glm-5-3-flash, data as of 21 Oct 2026.