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GLM 4.7 vs GPT-5.1

GLM 4.7 leads GPT-5.1 on coding, 55.1 to 40.4 on the Vibe Coding Index, and GLM 4.7 costs 4.5x less per output token ($2.20 vs $10.00 per million). Updated October 22, 2026; 74 benchmarks have a result for both.

Replace GLM 4.7 Replace GPT-5.1
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
18
GPT-5.1OpenAI
Coding
40.4
Overall
55.0
Output $/M
$10.00
55
vs boards won of 74 shared, 1 tied

GLM 4.7 or GPT-5.1: which to pick

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 40.4 for GPT-5.1, though on 2 benchmarks against 3.

Pick GPT-5.1 for a larger context window (400K against 205K tokens) and work measured by AA Coding Index or LiveCodeBench (Vals AI), where it leads.

Model AGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved taskModel BGPT-5.1OpenAI40.4/1003 independent benchmarks$1.02 per solved task
Shared results
74
GLM 4.7 only
7
GPT-5.1 only
27
Like-for-like categories
8 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

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 55.1 from 2 benchmarks, GPT-5.1 40.4 from 3 benchmarks.
Widest category
Agentic work, 9.8 points apart on average over 2 shared benchmarks.
Widest single gap
Chess puzzles: GLM 4.7 21.0% against GPT-5.1 42.0%, 21.0 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $2.20 against GPT-5.1 $10.00 per million output tokens, 4.5x apart.
Context window
GLM 4.7 takes 205K tokens, GPT-5.1 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-5.1Ties
Coding56
Intelligence02
Agentic work20
Reasoning09
Math241 tie
Knowledge112
Web design62
General220
All shared boards18551 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index56
Coding benchmarks for GLM 4.7 and GPT-5.1
BenchmarkGLM 4.7GPT-5.1Leader
SWE-bench VerifiedNot measured66.0%Only GPT-5.1 measured
LMArena WebDev14351396GLM 4.7
AA Coding Index45.349.4GPT-5.1
LiveCodeBench (Vals AI)82.2%86.5%GPT-5.1
SWE-bench Verified (Vals AI)69.4%69.8%GPT-5.1
Vibe Code Bench (Vals AI)Not measured24.6%Only GPT-5.1 measured
LiveCodeBench (Artificial Analysis run)89.4%86.8%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)31.8%45.5%GPT-5.1
Terminal-Bench33.4%47.6%GPT-5.1
GSO-BenchNot measured13.7%Only GPT-5.1 measured
WeirdMLNot measured60.8%Only GPT-5.1 measured
ALE-Bench399.5Not measuredOnly GLM 4.7 measured
LMArena Text: coding14541453GLM 4.7
LMArena Text: software and IT14531454GPT-5.1
LMArena WebDev: React14201307GLM 4.7
LMArena WebDev: HTML14471405GLM 4.7
LMArena WebDev: from an imageNot measured1421Only GPT-5.1 measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and GPT-5.1
BenchmarkGLM 4.7GPT-5.1Leader
AA Intelligence Index22.224.7GPT-5.1
Epoch Capabilities Index143.6149.8GPT-5.1
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for GLM 4.7 and GPT-5.1
BenchmarkGLM 4.7GPT-5.1Leader
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 apps1112Not measuredOnly GLM 4.7 measured
Design Arena: agents building Godot games1058Not measuredOnly GLM 4.7 measured
τ²-Bench Telecom95.9%81.9%GLM 4.7
DeepResearch BenchNot measured42.8%Only GPT-5.1 measured
Vending-Bench 2$2,377$1,473GLM 4.7
BrowseComp-Plus64.7%Not 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 index09
Reasoning benchmarks for GLM 4.7 and GPT-5.1
BenchmarkGLM 4.7GPT-5.1Leader
GPQA Diamond80.0%86.6%GPT-5.1
MMMU Pro (Vals AI)Not measured83.2%Only GPT-5.1 measured
GPQA Diamond (Artificial Analysis run)85.9%87.3%GPT-5.1
Humanity's Last Exam (Artificial Analysis run)27.4%28.5%GPT-5.1
GPQA Diamond (Epoch AI run)83.3%87.6%GPT-5.1
ARC-AGINot measured72.8%Only GPT-5.1 measured
ARC-AGI-2Not measured17.6%Only GPT-5.1 measured
SimpleBench47.7%53.2%GPT-5.1
Chess puzzles21.0%42.0%GPT-5.1
Mystery game puzzlesNot measured19.0%Only GPT-5.1 measured
VPCTNot measured58.7%Only GPT-5.1 measured
LMArena Text: hard prompts14431457GPT-5.1
LMArena Text: expert14241470GPT-5.1
LMArena Text: hard prompts in English14521462GPT-5.1
LMArena Vision: diagramsNot measured1264Only GPT-5.1 measured
LMArena Vision: homeworkNot measured1272Only GPT-5.1 measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index24
Math benchmarks for GLM 4.7 and GPT-5.1
BenchmarkGLM 4.7GPT-5.1Leader
AIME93.3%93.3%Tie
MGSM (Vals AI)88.2%93.0%GPT-5.1
AA Math Index95.094.0GLM 4.7
AIME 2025 (Artificial Analysis run)95.0%94.0%GLM 4.7
OTIS Mock AIME83.3%88.6%GPT-5.1
ProofBench6.0%Not measuredOnly GLM 4.7 measured
LMArena Text: math14241446GPT-5.1
LMArena Text: mathematical work14111449GPT-5.1
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index112
Knowledge benchmarks for GLM 4.7 and GPT-5.1
BenchmarkGLM 4.7GPT-5.1Leader
MMLU Pro82.7%86.4%GPT-5.1
LegalBench (Vals AI)83.4%85.7%GPT-5.1
CaseLaw (Vals AI)54.9%73.4%GPT-5.1
CorpFin (Vals AI)46.4%63.8%GPT-5.1
TaxEval (Vals AI)68.8%74.9%GPT-5.1
MortgageTax (Vals AI)Not measured61.4%Only GPT-5.1 measured
MedQA (Vals AI)93.7%96.4%GPT-5.1
MedCode (Vals AI)32.8%52.7%GPT-5.1
MMLU Pro (Artificial Analysis run)85.6%87.0%GPT-5.1
SimpleQA Verified32.2%48.0%GPT-5.1
LMArena Text: science14571459GPT-5.1
LMArena Text: medicine and healthcare14691456GLM 4.7
LMArena Text: legal and government14441459GPT-5.1
LMArena Text: business and finance14291431GPT-5.1
FACTS Benchmark SuiteNot measured45.0%Only GPT-5.1 measured
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index62
Web design benchmarks for GLM 4.7 and GPT-5.1
BenchmarkGLM 4.7GPT-5.1Leader
Design Arena: websites12251187GLM 4.7
Design Arena: UI components11961163GLM 4.7
Design Arena: game dev11881178GLM 4.7
Design Arena: data viz11951204GPT-5.1
Design Arena: 3D11901060GLM 4.7
Design Arena: all code categories12161170GLM 4.7
Design Arena: SVG11421149GPT-5.1
Design Arena: ASCII art11651119GLM 4.7
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index220
General benchmarks for GLM 4.7 and GPT-5.1
BenchmarkGLM 4.7GPT-5.1Leader
SAGE (Vals AI)Not measured43.2%Only GPT-5.1 measured
MedScribe (Vals AI)68.6%88.1%GPT-5.1
IFBench67.9%72.9%GPT-5.1
Long context reasoning (AA-LCR)71.0%80.0%GPT-5.1
DTBenchNot measured90.1%Only GPT-5.1 measured
LMCANot measured43.9%Only GPT-5.1 measured
CL-bench15.9%23.7%GPT-5.1
CL-bench Life10.9%17.3%GPT-5.1
LMArena Text14351443GPT-5.1
LMArena Text: instruction following14121443GPT-5.1
LMArena Text: creative writing14011427GPT-5.1
LMArena VisionNot measured1250Only GPT-5.1 measured
LMArena Text: English14531451GLM 4.7
LMArena Text: non-English14171431GPT-5.1
LMArena Text: Chinese14951495GPT-5.1
LMArena Text: German14261438GPT-5.1
LMArena Text: Japanese14391453GPT-5.1
LMArena Text: Korean13991401GPT-5.1
LMArena Text: French14321450GPT-5.1
LMArena Text: Spanish14341433GLM 4.7
LMArena Text: Russian14231435GPT-5.1
LMArena Text: Polish14351440GPT-5.1
LMArena Text: multi-turn14461450GPT-5.1
LMArena Text: longer queries14321448GPT-5.1
LMArena Text: writing and language14111428GPT-5.1
LMArena Text: entertainment and media14001415GPT-5.1
LMArena Vision: EnglishNot measured1252Only GPT-5.1 measured
LMArena Vision: ChineseNot measured1270Only GPT-5.1 measured
LMArena Vision: reading textNot measured1255Only GPT-5.1 measured
LMArena Vision: humorNot measured1263Only GPT-5.1 measured
LMArena Vision: captioningNot measured1252Only GPT-5.1 measured
LMArena Vision: recognizing thingsNot measured1232Only GPT-5.1 measured
LMArena Vision: creative writingNot measured1266Only GPT-5.1 measured
LMArena DocumentNot measured1403Only GPT-5.1 measured
Neither model has a result on the other 12 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
BenchmarkGLM 4.7GPT-5.1Leader
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$0.0017Fits in one request
GPT-5.1$0.0063Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.037Fits in one request
GPT-5.1$0.092Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.142Exceeds the context window
GPT-5.1$0.350Fits in one request

GLM 4.7 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
SpecificationZ.ai GLM 4.7OpenAI GPT-5.1
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025November 13, 2025
Context window205K400K
Input $/Mtok$0.60$1.25
Output $/Mtok$2.20$10.00
Coding index55.140.4
Independent measurements behind it23
Cost per solved task$0.29$1.02
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than GPT-5.1 for coding?

Pick GLM 4.7 for coding: it scores 55.1 on the Vibe Coding Index against 40.4 for GPT-5.1, though on 2 benchmarks against 3. Pick GPT-5.1 for a larger context window (400K against 205K tokens) and work measured by AA Coding Index or LiveCodeBench (Vals AI), where it leads.

Which is cheaper, GLM 4.7 or GPT-5.1?

GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.1 costs $1.25 and $10.00. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and GPT-5.1 $1.02.

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

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

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

74 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and GPT-5.1's on 3. Where only one model has a result, the page says so instead of naming a winner.

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