GLM-5.3 Flash benchmarks

GLM-5.3 Flash is an open-weights model from Z.ai, released 26 Aug 2026. It ranks 23rd of 51 on our overall leaderboard and 1st of 50 for value. At A$0.33 per million tokens (blended), it costs about 11× cheaper than the median model we track. It writes about 57 tokens a second, and answers start after 38 s on average, thinking included. Its weights are public, so it can be hosted in Australia on your own infrastructure or an Australian cloud region.

#23 OF 51 OVERALLA$0.33 / 1M TOKENS1M CONTEXTUPDATED 23 SEP 2026
CONTEXT WINDOW
1,000,000 tokens
MAX OUTPUT
128,000 tokens
INPUT
text, image, video
WEIGHTS
Open (MIT)
RELEASED
26 Aug 2026
OUTPUT SPEED
57 tok/s
ANSWER STARTS AFTER
38 s (thinking included)
Z.AI DOCS
§ 02 — RESULTS

Benchmark results.

Independent test scores are for the default setting. “Among tracked” ranks GLM-5.3 Flash against the 51 models on this site; LMArena ranks run across its full leaderboard.

ALL RESULTS8 OF 8 CORE SIGNALS
GLM-5.3 Flash benchmark results
BENCHMARKRESULTAMONG TRACKEDSOURCE DETAIL
INDEPENDENT TESTS · ARTIFICIAL ANALYSIS
AA Intelligence Index41.8#17 of 51Default
AA Coding Index71.5#20 of 42Default
GPQA Diamond91.2%#26 of 44Default
Humanity’s Last Exam39.9%#34 of 51Default
AA-LCR80.0%#27 of 51Default
Terminal-Bench 2.184.3%#14 of 42Default
τ²-Bench (banking)47.2%#6 of 41Default
SciCode51.6%#32 of 46Default
BLIND HUMAN VOTES · LMARENA
LMArena Text10,038 votes · ±71475#24 of 42#29 of 402 on LMArena · 13 Sep 2026
LMArena WebDev5,818 votes · ±91612#14 of 45#18 of 129 on LMArena · 22 Sep 2026
LMArena Vision3,110 votes · ±121275#22 of 33#30 of 152 on LMArena · 13 Sep 2026
LMArena Agent43,164 sessions#25#22 of 37#25 of 46 on LMArena · 15 Sep 2026
§ 03 — COST

What GLM-5.3 Flash costs in Australian dollars.

List prices converted at the snapshot’s RBA rate, excluding GST. Reasoning models are billed for their thinking as output tokens, so heavier settings cost more than the job estimates show.

LIST PRICE
GLM-5.3 Flash price per million tokens
PER MILLION TOKENSAUDUSD LIST
Input tokensA$0.21US$0.15
Output tokensA$0.70US$0.50
Blended (3 in : 1 out)A$0.33US$0.24
EVERYDAY JOBSESTIMATES
Estimated cost of common jobs in Australian dollars
JOBCOST (AUD)MEDIAN MODEL
Customer support replyPER 1,000 REPLIESA$0.63A$6.58
Summarise a 30-page documentPER 100 DOCUMENTSA$0.48A$4.74
Agentic coding taskPER 10 TASKSA$0.37A$3.72
§ 04 — AUSTRALIA

Running GLM-5.3 Flash in Australia.

Its weights are public, so it can be hosted in Australia on your own infrastructure or an Australian cloud region. How the platforms compare →

AUSTRALIAN CLOUD REGIONS
GLM-5.3 Flash availability in Australian cloud regions
PLATFORMAVAILABILITYDETAIL
AWS Bedrock · SydneyNOT OFFERED
AWS Bedrock · MelbourneNOT OFFERED
Azure · Australia EastNOT OFFERED
Google Vertex AI · SydneyNOT OFFERED
Your own infrastructureSELF-HOST

Open weights: run it on your own servers or GPU instances in an Australian region.

§ 06 — QUESTIONS

GLM-5.3 Flash, answered.

GLM-5.3 Flash lists at US$0.15 per million input tokens and US$0.50 per million output tokens — A$0.21 and A$0.70 at A$1 = US$0.7123 (RBA, 22 Sep 2026), excluding GST. A typical customer support reply works out at about A$0.63 per 1,000 replies, before any reasoning tokens.

GLM-5.3 Flash’s weights are public, so it can be hosted in Australia on your own infrastructure or an Australian cloud region. Availability comes from the cloud providers’ own documentation; check your provider’s current terms before relying on it for data-residency obligations.

It ranks 16th of 42 on our coding ranking, with an Artificial Analysis Coding Index of 71.5 and an LMArena WebDev rating of 1612 (18th of 129). The current leader is Claude Fable 5.1.

It ranks 11th of 41 on our agents ranking, completing 47.2% of τ²-Bench banking customer-service tasks and 84.3% of Terminal-Bench 2.1 tasks.

Artificial Analysis measures GLM-5.3 Flash at about 57 tok/s of output, with the answer starting after 38 s on average once thinking time is included (default setting).

Z.ai documents a context window of 1,000,000 tokens (1M), with up to 128,000 output tokens. On AA-LCR, which tests reasoning across ~100,000-token document sets, it scores 80.0%.

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