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Model comparison

GLM-5.1 vs GPT-5.5

Data verified

Head-to-head evidence from 28 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

67.74/100
Margin
5.8pts
winning →
GPT-5.5
OpenAI
73.51/100
2 category wins2 category wins

Public leaderboard positions: GLM-5.1 #18 (Supported); GPT-5.5 #9 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-5.1 and GPT-5.5 share 28 comparable benchmark results. 4 of 8 categories are comparable. 8 results are unique to GLM-5.1; 29 to GPT-5.5.

Updated July 23, 2026
Shared results
28
GLM-5.1 only
8
GPT-5.5 only
29
Comparable categories
4 / 8

Pick GPT-5.5 if you want the stronger benchmark profile. GLM-5.1 only becomes the better choice if mathematics is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 28 shared benchmark results across 7 evidence categories; 4 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GPT-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 67.74. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 65.4. The single biggest benchmark swing on the page is FrontierMath v2 (Tier 4), 12.500% to 35.400%. GLM-5.1 does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.

GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.1. That is roughly 6.8x on output cost alone. GPT-5.5 gives you the larger context window at 1M, compared with 203K for GLM-5.1.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for GLM-5.1 and GPT-5.5
CategoryGLM-5.1ΔGPT-5.5
AgenticGLM-5.165.4Margin→ 16.2GPT-5.581.6
MathGLM-5.162.0Margin← 14.4GPT-5.547.6
KnowledgeGLM-5.152.3Margin→ 5.5GPT-5.557.8
CodingGLM-5.161.3Margin← 2.7GPT-5.558.6
ReasoningGLM-5.1Not measuredMarginNo overlapGPT-5.585.0
MultimodalGLM-5.1Not measuredMarginNo overlapGPT-5.570.4

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · GLM-5.1B · GPT-5.5

  1. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 12.500%B 35.400%
    Winner: GPT-5.5Δ 22.9
    FrontierMath v2 (Tier 4): GLM-5.1 scored 12.500%; GPT-5.5 scored 35.400%. GPT-5.5 wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 63.5%B 82%
    Winner: GPT-5.5Δ 18.5
    Terminal-Bench 2.0: GLM-5.1 scored 63.5%; GPT-5.5 scored 82%. GPT-5.5 wins this benchmark.

  3. FrontierMath v2 (Tiers 1-3)

    Math
    Source ↗
    A 33.448%B 51.700%
    Winner: GPT-5.5Δ 18.3
    FrontierMath v2 (Tiers 1-3): GLM-5.1 scored 33.448%; GPT-5.5 scored 51.700%. GPT-5.5 wins this benchmark.

  4. BrowseComp

    Agentic
    Source ↗
    A 68%B 84.4%
    Winner: GPT-5.5Δ 16.4
    BrowseComp: GLM-5.1 scored 68%; GPT-5.5 scored 84.4%. GPT-5.5 wins this benchmark.

  5. SWE-bench Pro

    Coding
    Source ↗
    A 58.4%B 58.6%
    Winner: GPT-5.5Δ 0.2
    SWE-bench Pro: GLM-5.1 scored 58.4%; GPT-5.5 scored 58.6%. GPT-5.5 wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricGLM-5.1GPT-5.5Comparison
Input / output priceUSD per 1M tokensGLM-5.1$1.4 input / $4.4 outputGPT-5.5$5 input / $30 outputGLM-5.1 has the lower combined listed price.
Generation speedtokens per secondGLM-5.1Not availableGPT-5.5Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-5.1Not availableGPT-5.5Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-5.1203KGPT-5.51MGPT-5.5 lists the larger context window.

Benchmark Deep Dive

AgenticGPT-5.5 wins
BenchmarkGLM-5.1GPT-5.5Result
Terminal-Bench 2.0Source 63.5%82%GPT-5.5 leads
BrowseCompSource 68%84.4%GPT-5.5 leads
τ³-bench resultsSource 70.6%—Not comparable
MCP AtlasSource 71.8%75.3%GPT-5.5 leads
CyberGymSource 68.7%81.8%GPT-5.5 leads
Claw-EvalSource 62.3%—Not comparable
AA Agentic IndexSource 29.9%44.9%GPT-5.5 leads
τ²-bench resultsSource 97.7%93.9%GLM-5.1 leads
GDPval-AASource 37.8%49.5%GPT-5.5 leads
Gert LabsSource 60.11%72.93%GPT-5.5 leads
GDPval-AASource 12571490GPT-5.5 leads
ResearchClawBenchSource 18.2%17.0%GLM-5.1 leads
OSWorld-VerifiedSource —78.7%Not comparable
ToolathlonSource —55.6%Not comparable
APEX-Agents-AASource —37.7%Not comparable
OSWorld 2.0Source —13.0%Not comparable
JobBenchSource —42.7%Not comparable
ExploitGymSource —13.4%Not comparable
AA BriefcaseSource —1154Not comparable
AA AutomationBenchSource —42.1%Not comparable
AA EnterpriseOps-GymSource —46.6%Not comparable
AA Harvey LABSource —86.3%Not comparable
AA ITBenchSource —45.8%Not comparable
AA Tau3 BankingSource —31.3%Not comparable
terminalBenchHardSource —60.6%Not comparable
aaTerminalBench21Source —84.3%Not comparable
CodingGLM-5.1 wins
BenchmarkGLM-5.1GPT-5.5Result
SWE-bench ProSource 58.4%58.6%GPT-5.5 leads
NL2RepoSource 42.7%—Not comparable
SWE-RebenchSource 62.7%—Not comparable
Vibe Code BenchSource 31.46%69.85%GPT-5.5 leads
AA Coding IndexSource 55.8%74.9%GPT-5.5 leads
AA-SciCodeSource 43.8%56.1%GPT-5.5 leads
Terminal-Bench 2.0Source —82.0%Not comparable
React Native EvalsSource —84.7%Not comparable
cursorBench31Source —59.2%Not comparable
cursorBench32Source —58.4%Not comparable
FrontierCode 1.1 MainSource —43.0%Not comparable
Reasoning
BenchmarkGLM-5.1GPT-5.5Result
AA-LCRSource 62.3%74.3%GPT-5.5 leads
CritPtSource 4.6%27.1%GPT-5.5 leads
MRCR v2 64K-128KSource —83.1%Not comparable
MRCR v2 128K-256KSource —87.5%Not comparable
ARC-AGI-2Source —85%Not comparable
KnowledgeGPT-5.5 wins
BenchmarkGLM-5.1GPT-5.5Result
GPQA-DSource 86.2%93.6%GPT-5.5 leads
HLESource 52.3%52.2%GLM-5.1 leads
Artificial Analysis Intelligence IndexSource 40.2%54.8%GPT-5.5 leads
AA-GPQA DiamondSource 86.8%93.5%GPT-5.5 leads
AA-HLESource 28.0%44.3%GPT-5.5 leads
AA-Omniscience IndexSource 1.9%20.1%GPT-5.5 leads
AA-Omniscience AccuracySource 24.2%56.9%GPT-5.5 leads
AA-Omniscience Hallucination RateSource 29.4%85.5%GLM-5.1 leads
GPQASource —93.6%Not comparable
HLE w/o toolsSource —41.4%Not comparable
MathGLM-5.1 wins
BenchmarkGLM-5.1GPT-5.5Result
AIME26Source 95.3%—Not comparable
HMMT Nov 2025Source 94.0%—Not comparable
HMMT Feb 2026Source 82.6%—Not comparable
MMAnswerBenchSource 83.8%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 33.448%51.700%GPT-5.5 leads
FrontierMath v2 (Tier 4)Source 12.500%35.400%GPT-5.5 leads
FrontierMath (legacy)Source —51.7%Not comparable
Multimodal
BenchmarkGLM-5.1GPT-5.5Result
Design Arena WebsiteSource 13051282GLM-5.1 leads
MMMU-ProSource —81.2%Not comparable
MMMU-Pro w/ PythonSource —83.2%Not comparable
OfficeQA ProSource —54.1%Not comparable
AA-MMMU-ProSource —79.9%Not comparable
Inst. Following
BenchmarkGLM-5.1GPT-5.5Result
AA-IFBenchSource 76.3%75.9%GLM-5.1 leads
Frequently Asked Questions (5)

Which is better, GLM-5.1 or GPT-5.5?

GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 67.74. The biggest single separator in this matchup is FrontierMath v2 (Tier 4), where the scores are 12.500% and 35.400%.

Which is better for knowledge tasks, GLM-5.1 or GPT-5.5?

GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 57.8 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-5.1 or GPT-5.5?

GLM-5.1 has the edge for coding in this comparison, averaging 61.3 versus 58.6. Inside this category, Vibe Code Bench is the benchmark that creates the most daylight between them.

Which is better for math, GLM-5.1 or GPT-5.5?

GLM-5.1 has the edge for math in this comparison, averaging 62 versus 47.6. Inside this category, FrontierMath v2 (Tier 4) is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-5.1 or GPT-5.5?

GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 65.4. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
GPT-5.5
API / mo$26,250
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

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