Skip to main content
BenchLM
Advertise

Model comparison

GLM-4.7 vs GPT-5.3 Codex

Data verified

Head-to-head evidence from 16 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

61.16/100
Margin
5.5pts
winning →
66.69/100
1 category wins1 category wins

Public leaderboard positions: GLM-4.7 #42 (Supported); GPT-5.3 Codex #26 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GLM-4.7 and GPT-5.3 Codex share 16 comparable benchmark results. 2 of 8 categories are comparable. 14 results are unique to GLM-4.7; 5 to GPT-5.3 Codex.

Updated July 20, 2026
Shared results
16
GLM-4.7 only
14
GPT-5.3 Codex only
5
Comparable categories
2 / 8

Pick GPT-5.3 Codex 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 16 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GPT-5.3 Codex is clearly ahead on the BenchAlign aggregate, 66.69 to 61.16. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GPT-5.3 Codex's sharpest advantage is in agentic, where it averages 71.4 against 45.7. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 77.3%. 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-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 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-5.3 Codex gives you the larger context window at 400K, 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-5.3 Codex
CategoryGLM-4.7ΔGPT-5.3 Codex
AgenticGLM-4.745.7Margin→ 25.7GPT-5.3 Codex71.4
CodingGLM-4.775.4Margin← 8.2GPT-5.3 Codex67.2
KnowledgeGLM-4.751.8MarginNo overlapGPT-5.3 CodexNot measured
MathGLM-4.71.8MarginNo overlapGPT-5.3 CodexNot measured

Decisive benchmark drivers

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

More
A · GLM-4.7B · GPT-5.3 Codex

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 41%B 77.3%
    Winner: GPT-5.3 CodexΔ 36.3
    Terminal-Bench 2.0: GLM-4.7 scored 41%; GPT-5.3 Codex scored 77.3%. GPT-5.3 Codex wins this benchmark.

  2. SWE-bench Verified

    Coding
    Source ↗
    A 73.8%B 85%
    Winner: GPT-5.3 CodexΔ 11.2
    SWE-bench Verified: GLM-4.7 scored 73.8%; GPT-5.3 Codex scored 85%. GPT-5.3 Codex wins this benchmark.

  3. SWE-Rebench

    Coding
    Source ↗
    A 58.7%B 58.2%
    Winner: GLM-4.7Δ 0.5
    SWE-Rebench: GLM-4.7 scored 58.7%; GPT-5.3 Codex scored 58.2%. GLM-4.7 wins this benchmark.

Operational comparison

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

MetricGLM-4.7GPT-5.3 CodexComparison
Input / output priceUSD per 1M tokensGLM-4.7$0 input / $0 outputGPT-5.3 Codex$1.75 input / $14 outputGLM-4.7 has the lower combined listed price.
Generation speedtokens per secondGLM-4.782 tok/sGPT-5.3 Codex79 tok/sGLM-4.7 has the higher measured throughput.
First-answer latencyseconds to first tokenGLM-4.71.10 sGPT-5.3 Codex88.26 sGLM-4.7 reaches the first token sooner.
Context windowmaximum listed tokensGLM-4.7200KGPT-5.3 Codex400KGPT-5.3 Codex lists the larger context window.

Benchmark Deep Dive

AgenticGPT-5.3 Codex wins
BenchmarkGLM-4.7GPT-5.3 CodexResult
Terminal-Bench 2.0Source 41%77.3%GPT-5.3 Codex leads
BrowseCompSource 52%—Not comparable
VITA-BenchSource 15.5%—Not comparable
AA Agentic IndexSource 25.4%—Not comparable
τ²-bench resultsSource 95.9%86%GLM-4.7 leads
Gert LabsSource 39.95%57.47%GPT-5.3 Codex leads
GDPval-AASource 33.3%—Not comparable
GDPval-AASource 1165—Not comparable
OSWorld-VerifiedSource —64.7%Not comparable
JobBenchSource —33.7%Not comparable
CodingGLM-4.7 wins
BenchmarkGLM-4.7GPT-5.3 CodexResult
SWE-bench VerifiedSource 73.8%85%GPT-5.3 Codex leads
LiveCodeBenchSource 84.9%—Not comparable
SWE-RebenchSource 58.7%58.2%GLM-4.7 leads
AA Coding IndexSource 45.3%—Not comparable
AA-SciCodeSource 45.1%53.2%GPT-5.3 Codex leads
AA LiveCodeBenchSource 89.4%—Not comparable
SWE-bench ProSource —56.8%Not comparable
Vibe Code BenchSource —61.77%Not comparable
Reasoning
BenchmarkGLM-4.7GPT-5.3 CodexResult
AA-LCRSource 64.0%74.0%GPT-5.3 Codex leads
CritPtSource 1.7%16.9%GPT-5.3 Codex leads
Knowledge
BenchmarkGLM-4.7GPT-5.3 CodexResult
GPQASource 85.7%—Not comparable
MMLU-ProSource 84.3%—Not comparable
HLESource 24.8%—Not comparable
Artificial Analysis Intelligence IndexSource 33.7%44.3%GPT-5.3 Codex leads
AA-GPQA DiamondSource 85.9%91.5%GPT-5.3 Codex leads
AA-HLESource 25.1%39.9%GPT-5.3 Codex leads
AA-Omniscience IndexSource -34.6%9.9%GPT-5.3 Codex leads
AA-Omniscience AccuracySource 29.3%51.8%GPT-5.3 Codex leads
AA-Omniscience Hallucination RateSource 90.3%86.9%GPT-5.3 Codex leads
Math
BenchmarkGLM-4.7GPT-5.3 CodexResult
AIME 2025Source 95.7%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 2.439%—Not comparable
FrontierMath v2 (Tier 4)Source 0.000%—Not comparable
Multimodal
BenchmarkGLM-4.7GPT-5.3 CodexResult
Design Arena WebsiteSource 12581195GLM-4.7 leads
AA-MMMU-ProSource —78.5%Not comparable
Inst. Following
BenchmarkGLM-4.7GPT-5.3 CodexResult
AA-IFBenchSource 67.9%75.4%GPT-5.3 Codex leads
Frequently Asked Questions (3)

Which is better, GLM-4.7 or GPT-5.3 Codex?

GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 66.69 to 61.16. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 77.3%.

Which is better for coding, GLM-4.7 or GPT-5.3 Codex?

GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 67.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GLM-4.7 or GPT-5.3 Codex?

GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 71.4 versus 45.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

Related Comparisons

Explore More

Z.AI Compare Pricing Methodology Find Your Best LLM Overall Rankings
Last updated: July 20, 2026

Choose a model with this week’s evidence

Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.

One email each week. Unsubscribe anytime.