BenchLeader

Claude Opus 4.6 vs GLM 5.3

Verdict
  • Claude Opus 4.6 (thinking) leads on quality: 62.5 vs 60.0.
  • Claude Opus 4.6 (thinking) is stronger in knowledge, maths, reasoning.
  • GLM 5.3 (max) is stronger in coding, agents & tools, human preference.
  • GLM 5.3 (max) is 4.7× cheaper ($2.15 vs $10.00 per 1M blended).
  • GLM 5.3 (max) streams 1.7× faster (66 vs 38 tokens per second).
MetricClaude Opus 4.6 (thinking)GLM 5.3 (max)
BenchLeader Index62.560.0
Coding score63.664.3
Knowledge score62.255.8
Maths score65.160.0
Reasoning score75.666.9
Agents & tools score52.8
Human preference score68.9
Blended price $/M$10.00$2.15
Output speed38 tok/s66 tok/s
Time to first answer2.0 s33.5 s
Context window1M1M
GPQA Diamond90.9%
FrontierMath Tiers 1–368.8%
FrontierMath Tier 429.3%
OTIS Mock AIME91.1%
SimpleQA Verified41.0%
Terminal-Bench41.8%
SciCode56.5%
WeirdML75.4%
ProofBench49.0%
LMArena Text1486
LMArena Hard Prompts1509
LMArena Coding1526
LMArena WebDev1614
LMArena Agent2.6
AIME (Vals)95.6%
LiveCodeBench84.7%80.5%
MMLU-Pro89.1%86.8%
IOI68.4%
LegalBench85.3%84.8%
CorpFin67.0%
TaxEval76.0%72.4%
MedQA95.4%
Terminal-Bench 2.1 (Vals)71.5%
SWE-bench (Vals)78.2%95.4%
GPQA Diamond (Vals)89.7%88.1%
Vals Index57.0
SWE-Bench Pro51.9%
Kagi LLM Benchmark83.6%

Data as of 2026-09-13. Best configuration of each model; every score links to its source on the model pages.

Claude Opus 4.6 vs GLM 5.3: questions

Is Claude Opus 4.6 better than GLM 5.3?
Claude Opus 4.6 (thinking) leads on quality: 62.5 vs 60.0. The BenchLeader Index combines every independent quality benchmark; Claude Opus 4.6 (thinking) is ahead overall as of 2026-09-13, but check the category scores for your use.
Is Claude Opus 4.6 better than GLM 5.3 for coding?
GLM 5.3 scores higher in coding (64 vs 64 on the category index, where 50 is average).
Which is cheaper, Claude Opus 4.6 or GLM 5.3?
GLM 5.3 is cheaper: $2.15 against $10.00 per million tokens, blended at three input tokens per output token.
Which is faster, Claude Opus 4.6 or GLM 5.3?
GLM 5.3 streams faster: 66 against 38 output tokens per second.
Which has the larger context window?
Both accept 1M tokens of context.