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GLM 5.1 vs GLM 5.2

GLM 5.2 leads GLM 5.1 on coding, 61.1 to 49.2 on the Vibe Coding Index, at about the same output price ($4.40 vs $4.40 per million). Updated October 21, 2026; 84 benchmarks have a result for both.

Replace GLM 5.1 Replace GLM 5.2
GLM 5.1Z.ai
Coding
49.2
Overall
57.5
Output $/M
$4.40
16
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
68
vs boards won of 84 shared

GLM 5.1 or GLM 5.2: which to pick

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 49.2 for GLM 5.1.

Pick GLM 5.1 for a lower cost per solved task ($0.60 against $0.86) and work measured by LiveCodeBench (Vals AI) or Design Arena: agents building mobile apps, where it leads.

Model AGLM 5.1Z.ai49.2/1003 independent benchmarks$0.60 per solved taskModel BGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved task
Shared results
84
GLM 5.1 only
5
GLM 5.2 only
14
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 5.1 49.2 from 3 benchmarks, GLM 5.2 61.1 from 4 benchmarks.
Widest category
Coding, 10.0 points apart on average over 16 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5.1 31.5% against GLM 5.2 64.0%, 32.5 points apart on a 0 to 100 scale.
Output price
Both cost $4.40 per million output tokens.
Context window
GLM 5.1 takes 205K tokens, GLM 5.2 1.05M.

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 5.1BarsGLM 5.2Ties
Coding115
Intelligence02
Agentic work212
Reasoning18
Math14
Knowledge46
Web design44
General317
All shared boards1668
CodingLike-for-likeSix of these boards feed the Vibe Coding Index115
Coding benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
LMArena WebDev15081604GLM 5.2
AA Coding Index55.868.8GLM 5.2
LiveCodeBench (Vals AI)81.4%69.5%GLM 5.1
SWE-bench Verified (Vals AI)76.4%82.8%GLM 5.2
Vibe Code Bench (Vals AI)31.5%64.0%GLM 5.2
Code migration (Vals AI)25.8%37.9%GLM 5.2
ProgramBench (Vals AI)0.0%0.5%GLM 5.2
Terminal-Bench 2.1 (Vals AI)56.9%67.8%GLM 5.2
SkillsBench (Vals AI)Not measured45.1%Only GLM 5.2 measured
SciCode44.8%51.2%GLM 5.2
Terminal-Bench Hard (Artificial Analysis run)43.2%50.8%GLM 5.2
FrontierCodeNot measured24.5%Only GLM 5.2 measured
WeirdML57.1%70.1%GLM 5.2
ALE-Bench887.11047.0GLM 5.2
LMArena Text: coding14851487GLM 5.2
LMArena Text: software and IT14841486GLM 5.2
LMArena WebDev: React15051617GLM 5.2
LMArena WebDev: HTML15081565GLM 5.2
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
AA Intelligence Index26.133.7GLM 5.2
Epoch Capabilities Index149.8151.9GLM 5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index212
Agentic work benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
AA Agentic Index23.938.4GLM 5.2
Design Arena: agents building web apps11751222GLM 5.2
Design Arena: agents building full-stack apps11671241GLM 5.2
Design Arena: agents building mobile apps11621160GLM 5.1
Design Arena: agents building Android apps11801178GLM 5.1
Design Arena: agents building Godot games10531142GLM 5.2
Design Arena: agents building games11641206GLM 5.2
Design Arena: HTML slides11701175GLM 5.2
Design Arena: PowerPoint slides11881195GLM 5.2
Design Arena: spreadsheets1315Not measuredOnly GLM 5.1 measured
SREBench (Vals AI)Not measured0.0%Only GLM 5.2 measured
Finance Agent (Vals AI)44.8%49.7%GLM 5.2
Legal Research Bench (Vals AI)27.9%31.3%GLM 5.2
Excel Modeling Benchmark (Vals AI)Not measured61.5%Only GLM 5.2 measured
τ²-Bench Telecom97.7%99.1%GLM 5.2
APEX-Agents40.9%45.2%GLM 5.2
PostTrainBenchNot measured31.7%Only GLM 5.2 measured
Vending-Bench 2$5,634$8,314GLM 5.2
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index18
Reasoning benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
GPQA Diamond84.5%85.6%GLM 5.2
GPQA Diamond (Artificial Analysis run)86.8%89.5%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)30.1%41.1%GLM 5.2
GPQA Diamond (Epoch AI run)89.9%91.9%GLM 5.2
ARC-AGINot measured77.0%Only GLM 5.2 measured
ARC-AGI-2Not measured22.8%Only GLM 5.2 measured
SimpleBench55.1%58.8%GLM 5.2
Chess puzzles20.0%22.0%GLM 5.2
Mystery game puzzlesNot measured19.0%Only GLM 5.2 measured
EBR-benchNot measured9.5%Only GLM 5.2 measured
LMArena Text: hard prompts14731481GLM 5.2
LMArena Text: expert14781487GLM 5.2
LMArena Text: hard prompts in English14821481GLM 5.1
Kagi LLM BenchmarkNot measured62.6%Only GLM 5.2 measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index14
Math benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
AIME91.9%Not measuredOnly GLM 5.1 measured
OTIS Mock AIME93.3%86.4%GLM 5.1
FrontierMath, tiers 1 to 336.8%59.2%GLM 5.2
FrontierMath, tier 4Not measured29.3%Only GLM 5.2 measured
ProofBench22.2%35.0%GLM 5.2
LMArena Text: math14731483GLM 5.2
LMArena Text: mathematical work14701489GLM 5.2
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index46
Knowledge benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
MMLU Pro86.9%86.7%GLM 5.1
LegalBench (Vals AI)84.4%84.1%GLM 5.1
CaseLaw (Vals AI)51.5%Not measuredOnly GLM 5.1 measured
CorpFin (Vals AI)64.5%66.1%GLM 5.2
TaxEval (Vals AI)71.2%73.3%GLM 5.2
MedCode (Vals AI)41.6%40.8%GLM 5.1
SimpleQA Verified34.0%34.2%GLM 5.2
LMArena Text: science14781489GLM 5.2
LMArena Text: medicine and healthcare14741475GLM 5.2
LMArena Text: legal and government14651486GLM 5.2
LMArena Text: business and finance14521451GLM 5.1
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index44
Web design benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
Design Arena: websites12901285GLM 5.1
Design Arena: UI components12921296GLM 5.2
Design Arena: game dev12651268GLM 5.2
Design Arena: data viz13661285GLM 5.1
Design Arena: 3D13361288GLM 5.1
Design Arena: all code categories12681288GLM 5.2
Design Arena: SVG12181214GLM 5.1
Design Arena: ASCII art11421190GLM 5.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index317
General benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
Public Benefits Bench (Vals AI)61.8%Not measuredOnly GLM 5.1 measured
MedScribe (Vals AI)72.3%83.5%GLM 5.2
IFBench76.3%73.3%GLM 5.1
Long context reasoning (AA-LCR)73.7%78.3%GLM 5.2
DTBenchNot measured93.6%Only GLM 5.2 measured
LMCANot measured45.8%Only GLM 5.2 measured
Surface Evolver BenchNot measured55.6%Only GLM 5.2 measured
ExploitBench18.1%Not measuredOnly GLM 5.1 measured
LMArena Text14631470GLM 5.2
LMArena Text: instruction following14531466GLM 5.2
LMArena Text: creative writing14531463GLM 5.2
LMArena Text: English14761475GLM 5.1
LMArena Text: non-English14481459GLM 5.2
LMArena Text: Chinese15151522GLM 5.2
LMArena Text: German14671470GLM 5.2
LMArena Text: Japanese14351450GLM 5.2
LMArena Text: Korean14241440GLM 5.2
LMArena Text: French14701479GLM 5.2
LMArena Text: Spanish14681478GLM 5.2
LMArena Text: Russian14541468GLM 5.2
LMArena Text: Polish14491456GLM 5.2
LMArena Text: multi-turn14731469GLM 5.1
LMArena Text: longer queries14671480GLM 5.2
LMArena Text: writing and language14541463GLM 5.2
LMArena Text: entertainment and media14421450GLM 5.2
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.1 and GLM 5.2
BenchmarkGLM 5.1GLM 5.2Leader
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 5.1$0.0036Fits in one request
GLM 5.2$0.0036Fits in one request

Same cost

Repository review

50K input + 3K output tokens

GLM 5.1$0.083Fits in one request
GLM 5.2$0.083Fits in one request

Same cost

Agent loop

200K input + 10K output tokens

GLM 5.1$0.324Exceeds the context window
GLM 5.2$0.324Fits in one request

Same cost

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 5.1 and GLM 5.2
SpecificationZ.ai GLM 5.1Z.ai GLM 5.2
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedApril 7, 2026June 16, 2026
Context window205K1.05M
Input $/Mtok$1.40$1.40
Output $/Mtok$4.40$4.40
Coding index49.261.1
Independent measurements behind it34
Cost per solved task$0.60$0.86
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.1 better than GLM 5.2 for coding?

Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 49.2 for GLM 5.1. Pick GLM 5.1 for a lower cost per solved task ($0.60 against $0.86) and work measured by LiveCodeBench (Vals AI) or Design Arena: agents building mobile apps, where it leads.

Which is cheaper, GLM 5.1 or GLM 5.2?

GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; GLM 5.2 costs $1.40 and $4.40. Per solved SWE-bench Verified task, GLM 5.1 costs $0.60 and GLM 5.2 $0.86.

Which has the bigger context window, GLM 5.1 or GLM 5.2?

GLM 5.2, with 1.05M tokens against 205K.

How many benchmarks is the GLM 5.1 vs GLM 5.2 comparison based on?

84 benchmarks have a result for both models as of October 21, 2026. GLM 5.1's coding index rests on 3 independent benchmarks and GLM 5.2's on 4. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-5-1-vs-glm-5-2">LLMBenchmark.ai, GLM 5.1 vs GLM 5.2</a></p>