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Model comparison

GLM-4.7 vs GPT-5.5

Data verified

Head-to-head evidence from 23 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

61.16/100
Margin
12.4pts
winning →
GPT-5.5
OpenAI
73.51/100
1 category wins3 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); GPT-5.5 #9 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and GPT-5.5 share 23 comparable benchmark results. 4 of 8 categories are comparable. 7 results are unique to GLM-4.7; 34 to GPT-5.5.

Updated July 22, 2026
Shared results
23
GLM-4.7 only
7
GPT-5.5 only
34
Comparable categories
4 / 8

Pick GPT-5.5 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 23 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 61.16. 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 mathematics, where it averages 47.6 against 1.8. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 2.439% to 51.700%. GLM-4.7 does hit back in coding, 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 $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GPT-5.5 gives you the larger context window at 1M, compared with 200K for GLM-4.7.

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-4.7 and GPT-5.5
CategoryGLM-4.7ΔGPT-5.5
MathGLM-4.71.8Margin→ 45.8GPT-5.547.6
AgenticGLM-4.745.7Margin→ 35.9GPT-5.581.6
CodingGLM-4.775.4Margin← 16.8GPT-5.558.6
KnowledgeGLM-4.751.8Margin→ 6.0GPT-5.557.8
ReasoningGLM-4.7Not measuredMarginNo overlapGPT-5.585.0
MultimodalGLM-4.7Not measuredMarginNo overlapGPT-5.570.4

Decisive benchmark drivers

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

More
A · GLM-4.7B · GPT-5.5

  1. FrontierMath v2 (Tiers 1-3)

    Math
    Source ↗
    A 2.439%B 51.700%
    Winner: GPT-5.5Δ 49.3
    FrontierMath v2 (Tiers 1-3): GLM-4.7 scored 2.439%; GPT-5.5 scored 51.700%. GPT-5.5 wins this benchmark.

  2. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 82%
    Winner: GPT-5.5Δ 41
    Terminal-Bench 2.0: GLM-4.7 scored 41%; GPT-5.5 scored 82%. GPT-5.5 wins this benchmark.

  3. FrontierMath v2 (Tier 4)

    Math
    Source ↗
    A 0.000%B 35.400%
    Winner: GPT-5.5Δ 35.4
    FrontierMath v2 (Tier 4): GLM-4.7 scored 0.000%; GPT-5.5 scored 35.400%. GPT-5.5 wins this benchmark.

  4. BrowseComp

    Agentic
    Source ↗
    A 52%B 84.4%
    Winner: GPT-5.5Δ 32.4
    BrowseComp: GLM-4.7 scored 52%; GPT-5.5 scored 84.4%. GPT-5.5 wins this benchmark.

  5. HLE

    Knowledge
    Source ↗
    A 24.8%B 52.2%
    Winner: GPT-5.5Δ 27.4
    HLE: GLM-4.7 scored 24.8%; GPT-5.5 scored 52.2%. GPT-5.5 wins this benchmark.

Operational comparison

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

MetricGLM-4.7GPT-5.5Comparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputGPT-5.5$5 input / $30 outputGLM-4.7 has the lower combined listed price.
Generation speedtokens per secondGLM-4.782 tok/sGPT-5.5Not availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGLM-4.71.10 sGPT-5.5Not availableA complete latency comparison is not available.
Context windowmaximum listed tokensGLM-4.7200KGPT-5.51MGPT-5.5 lists the larger context window.

Benchmark Deep Dive

AgenticGPT-5.5 wins
BenchmarkGLM-4.7GPT-5.5Result
Terminal-Bench 2.0Source 41%82%GPT-5.5 leads
BrowseCompSource 52%84.4%GPT-5.5 leads
VITA-BenchSource 15.5%—Not comparable
AA Agentic IndexSource 25.4%44.9%GPT-5.5 leads
τ²-bench resultsSource 95.9%93.9%GLM-4.7 leads
Gert LabsSource 39.95%72.93%GPT-5.5 leads
GDPval-AASource 33.3%49.5%GPT-5.5 leads
GDPval-AASource 11651490GPT-5.5 leads
CyberGymSource —81.8%Not comparable
OSWorld-VerifiedSource —78.7%Not comparable
MCP AtlasSource —75.3%Not comparable
ToolathlonSource —55.6%Not comparable
APEX-Agents-AASource —37.7%Not comparable
ResearchClawBenchSource —17.0%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-4.7 wins
BenchmarkGLM-4.7GPT-5.5Result
SWE-bench VerifiedSource 73.8%—Not comparable
LiveCodeBenchSource 84.9%—Not comparable
SWE-RebenchSource 58.7%—Not comparable
AA Coding IndexSource 45.3%74.9%GPT-5.5 leads
AA-SciCodeSource 45.1%56.1%GPT-5.5 leads
AA LiveCodeBenchSource 89.4%—Not comparable
SWE-bench ProSource —58.6%Not comparable
Terminal-Bench 2.0Source —82.0%Not comparable
Vibe Code BenchSource —69.85%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-4.7GPT-5.5Result
AA-LCRSource 64.0%74.3%GPT-5.5 leads
CritPtSource 1.7%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-4.7GPT-5.5Result
GPQASource 85.7%93.6%GPT-5.5 leads
MMLU-ProSource 84.3%—Not comparable
HLESource 24.8%52.2%GPT-5.5 leads
Artificial Analysis Intelligence IndexSource 33.7%54.8%GPT-5.5 leads
AA-GPQA DiamondSource 85.9%93.5%GPT-5.5 leads
AA-HLESource 25.1%44.3%GPT-5.5 leads
AA-Omniscience IndexSource -34.6%20.1%GPT-5.5 leads
AA-Omniscience AccuracySource 29.3%56.9%GPT-5.5 leads
AA-Omniscience Hallucination RateSource 90.3%85.5%GPT-5.5 leads
GPQA-DSource —93.6%Not comparable
HLE w/o toolsSource —41.4%Not comparable
MathGPT-5.5 wins
BenchmarkGLM-4.7GPT-5.5Result
AIME 2025Source 95.7%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 2.439%51.700%GPT-5.5 leads
FrontierMath v2 (Tier 4)Source 0.000%35.400%GPT-5.5 leads
FrontierMath (legacy)Source —51.7%Not comparable
Multimodal
BenchmarkGLM-4.7GPT-5.5Result
Design Arena WebsiteSource 12551282GPT-5.5 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-4.7GPT-5.5Result
AA-IFBenchSource 67.9%75.9%GPT-5.5 leads
Frequently Asked Questions (5)

Which is better, GLM-4.7 or GPT-5.5?

GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 61.16. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 2.439% and 51.700%.

Which is better for knowledge tasks, GLM-4.7 or GPT-5.5?

GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 57.8 versus 51.8. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.

Which is better for coding, GLM-4.7 or GPT-5.5?

GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 58.6. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.

Which is better for math, GLM-4.7 or GPT-5.5?

GPT-5.5 has the edge for math in this comparison, averaging 47.6 versus 1.8. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-4.7 or GPT-5.5?

GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 45.7. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

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