BenchLeader

GLM-5 vs GPT-5.5 Pro

Verdict
  • GPT-5.5 Pro (xhigh) leads on quality: 67.1 vs 59.6.
  • GLM-5 (thinking) is stronger in agents & tools, coding, composite, instruction following, knowledge, long context.
  • GPT-5.5 Pro (xhigh) is stronger in maths, reasoning.
  • GLM-5 (thinking) is 44× cheaper ($1.55 vs $67.50 per 1M blended).
  • GLM-5 (thinking) streams 15.2× faster (61 vs 4 tokens per second).
MetricGLM-5 (thinking)GPT-5.5 Pro (xhigh)
BenchLeader Index59.667.1
Agents & tools score57.9
Coding score59.1
Composite score64.4
Instruction following score70.7
Knowledge score59.1
Long context score64.2
Maths score63.274.5
Reasoning score53.380.1
Blended price $/M$1.55$67.50
Output speed61 tok/s4 tok/s
Time to first answer52.4 s3.5 s
Context window205k1.1M
FrontierMath Tiers 1–387.7%
FrontierMath Tier 478.0%
AA Intelligence Index27.9
IFBench72.3%
AA-LCR75.7%
AA-Omniscience0.3
Terminal-Bench Hard43.2%
GPQA Diamond (AA)82.0%
Humanity's Last Exam (AA)29.3%
τ²-Bench Telecom (AA)98.3%
AIME (Vals)91.7%
LiveCodeBench81.9%
MMLU-Pro86.0%
LegalBench84.1%
CorpFin62.9%
TaxEval70.0%
MedQA94.3%
SWE-bench (Vals)71.4%
GPQA Diamond (Vals)83.3%
Kagi LLM Benchmark75.0%
ARC-AGI-195.0%
ARC-AGI-284.2%
τ²-bench81.0%
CritPt2.0%30.6%
APEX-Agents (AA)14.4%
CaseLaw v252.5%
Terminal-Bench 2.0 (Vals)49.4%
Vibe Code Bench v1.123.4%
LMCA53.9%
DTBench96.0%

Data as of 2026-09-19. Best configuration of each model; every score links to its source on the model pages.

GLM-5 vs GPT-5.5 Pro: questions

Is GLM-5 better than GPT-5.5 Pro?
GPT-5.5 Pro (xhigh) leads on quality: 67.1 vs 59.6. The BenchLeader Index combines every independent quality benchmark; GPT-5.5 Pro (xhigh) is ahead overall as of 2026-09-19, but check the category scores for your use.
Which is cheaper, GLM-5 or GPT-5.5 Pro?
GLM-5 is cheaper: $1.55 against $67.50 per million tokens, blended at three input tokens per output token.
Which is faster, GLM-5 or GPT-5.5 Pro?
GLM-5 streams faster: 61 against 4 output tokens per second.
Which has the larger context window?
GPT-5.5 Pro accepts more context: 1.1M against 205k tokens.