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GLM 4.7 vs GPT-5.1-Codex-Mini

Updated October 21, 2026. 22 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Replace GPT-5.1-Codex-Mini
GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
19
GPT-5.1-Codex-MiniOpenAI
Coding
6.2
Overall
20.4
Output $/M
$2.00
2
vs boards won of 22 shared, 1 tied

Decision reading

Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 6.2 for GPT-5.1-Codex-Mini.

Pick GPT-5.1-Codex-Mini when the context window is larger, 400K against 205K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or Terminal-Bench, where it leads.

Model AGLM 4.7Z.ai33.6/1001 independent benchmark$0.29 per solved taskModel BGPT-5.1-Codex-MiniOpenAI6.2/1001 independent benchmarkNo cost per solved task
Shared results
22
GLM 4.7 only
58
GPT-5.1-Codex-Mini only
0
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GLM 4.7 33.6 from 1 benchmark, GPT-5.1-Codex-Mini 6.2 from 1 benchmark.
Widest category
Agentic work, 33.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.7 95.9% against GPT-5.1-Codex-Mini 62.9%, 33.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $1.98 against GPT-5.1-Codex-Mini $2.00 per million output tokens, 1.0x apart.
Context window
GLM 4.7 takes 205K tokens, GPT-5.1-Codex-Mini 400K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGLM 4.7BarsGPT-5.1-Codex-MiniTies
Coding32
Intelligence10
Agentic work10
Reasoning20
Math20
Knowledge10
Web design80
General101 tie
All shared boards1921 tie
CodingLike-for-likeFour of these boards feed the Vibe Coding Index32
Coding benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
LMArena WebDev14351244GLM 4.7
AA Coding Index45.3Not measuredOnly GLM 4.7 measured
LiveCodeBench (Vals AI)82.2%Not measuredOnly GLM 4.7 measured
SWE-bench Verified (Vals AI)69.4%Not measuredOnly GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)89.4%83.6%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)31.8%33.3%GPT-5.1-Codex-Mini
Terminal-Bench33.4%61.6%GPT-5.1-Codex-Mini
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding1454Not measuredOnly GLM 4.7 measured
LMArena Text: software and IT1453Not measuredOnly GLM 4.7 measured
LMArena WebDev: React1420Not measuredOnly GLM 4.7 measured
LMArena WebDev: HTML14471253GLM 4.7
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
AA Intelligence Index22.220.4GLM 4.7
Epoch Capabilities Index143.6Not measuredOnly GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
Design Arena: agents building full-stack apps1050Not measuredOnly GLM 4.7 measured
Design Arena: agents building mobile apps1105Not measuredOnly GLM 4.7 measured
Design Arena: agents building Android apps1118Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%62.9%GLM 4.7
Vending-Bench 2$2,377Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
Reasoning benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
GPQA Diamond80.0%Not measuredOnly GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)85.9%81.3%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)27.4%18.5%GLM 4.7
GPQA Diamond (Epoch AI run)83.3%Not measuredOnly GLM 4.7 measured
SimpleBench47.7%Not measuredOnly GLM 4.7 measured
Chess puzzles21.0%Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts1443Not measuredOnly GLM 4.7 measured
LMArena Text: expert1424Not measuredOnly GLM 4.7 measured
LMArena Text: hard prompts in English1452Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.091.7GLM 4.7
AIME 2025 (Artificial Analysis run)95.0%91.7%GLM 4.7
OTIS Mock AIME83.3%Not measuredOnly GLM 4.7 measured
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math1424Not measuredOnly GLM 4.7 measured
LMArena Text: mathematical work1411Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
Knowledge benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
MMLU Pro82.7%Not measuredOnly GLM 4.7 measured
LegalBench (Vals AI)83.4%Not measuredOnly GLM 4.7 measured
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%Not measuredOnly GLM 4.7 measured
TaxEval (Vals AI)68.8%Not measuredOnly GLM 4.7 measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%Not measuredOnly GLM 4.7 measured
MMLU Pro (Artificial Analysis run)85.6%82.0%GLM 4.7
SimpleQA Verified32.2%Not measuredOnly GLM 4.7 measured
LMArena Text: science1457Not measuredOnly GLM 4.7 measured
LMArena Text: medicine and healthcare1469Not measuredOnly GLM 4.7 measured
LMArena Text: legal and government1444Not measuredOnly GLM 4.7 measured
LMArena Text: business and finance1429Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
Design Arena: websites12261111GLM 4.7
Design Arena: UI components11991081GLM 4.7
Design Arena: game dev11891093GLM 4.7
Design Arena: data viz11951097GLM 4.7
Design Arena: 3D1191988GLM 4.7
Design Arena: all code categories12171094GLM 4.7
Design Arena: SVG1142977GLM 4.7
Design Arena: ASCII art11651104GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
General benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
MedScribe (Vals AI)68.6%Not measuredOnly GLM 4.7 measured
IFBench67.9%67.9%Tie
Long context reasoning (AA-LCR)71.0%66.7%GLM 4.7
CL-bench15.9%Not measuredOnly GLM 4.7 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text1435Not measuredOnly GLM 4.7 measured
LMArena Text: instruction following1412Not measuredOnly GLM 4.7 measured
LMArena Text: creative writing1401Not measuredOnly GLM 4.7 measured
LMArena Text: English1453Not measuredOnly GLM 4.7 measured
LMArena Text: non-English1417Not measuredOnly GLM 4.7 measured
LMArena Text: Chinese1495Not measuredOnly GLM 4.7 measured
LMArena Text: German1426Not measuredOnly GLM 4.7 measured
LMArena Text: Japanese1439Not measuredOnly GLM 4.7 measured
LMArena Text: Korean1399Not measuredOnly GLM 4.7 measured
LMArena Text: French1432Not measuredOnly GLM 4.7 measured
LMArena Text: Spanish1434Not measuredOnly GLM 4.7 measured
LMArena Text: Russian1423Not measuredOnly GLM 4.7 measured
LMArena Text: Polish1435Not measuredOnly GLM 4.7 measured
LMArena Text: multi-turn1446Not measuredOnly GLM 4.7 measured
LMArena Text: longer queries1432Not measuredOnly GLM 4.7 measured
LMArena Text: writing and language1411Not measuredOnly GLM 4.7 measured
LMArena Text: entertainment and media1400Not measuredOnly GLM 4.7 measured
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and GPT-5.1-Codex-Mini
BenchmarkGLM 4.7GPT-5.1-Codex-MiniLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 4.7$0.0015Fits in one request
GPT-5.1-Codex-Mini$0.0013Fits in one request

GPT-5.1-Codex-Mini costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.033Fits in one request
GPT-5.1-Codex-Mini$0.018Fits in one request

GPT-5.1-Codex-Mini costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.128Exceeds the context window
GPT-5.1-Codex-Mini$0.070Fits in one request

GPT-5.1-Codex-Mini costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 4.7 and GPT-5.1-Codex-Mini
SpecificationZ.ai GLM 4.7OpenAI GPT-5.1-Codex-Mini
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025November 13, 2025
Context window205K400K
Input $/Mtok$0.54$0.25
Output $/Mtok$1.98$2.00
Coding index33.66.2
Independent measurements behind it11
Cost per solved task$0.29Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than GPT-5.1-Codex-Mini for coding?

Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 6.2 for GPT-5.1-Codex-Mini. Pick GPT-5.1-Codex-Mini when the context window is larger, 400K against 205K tokens, or when what matters is Terminal-Bench Hard (Artificial Analysis run) or Terminal-Bench, where it leads.

Which is cheaper, GLM 4.7 or GPT-5.1-Codex-Mini?

GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; GPT-5.1-Codex-Mini costs $0.25 and $2.00. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, GLM 4.7 or GPT-5.1-Codex-Mini?

GPT-5.1-Codex-Mini, with 400K tokens against 205K.

How many benchmarks is the GLM 4.7 vs GPT-5.1-Codex-Mini comparison based on?

22 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and GPT-5.1-Codex-Mini's on 1. Where only one model has a result, the page says so instead of naming a winner.

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