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GLM 5.2 vs GPT-5 Nano

GLM 5.2 leads GPT-5 Nano on the Artificial Analysis Intelligence Index, 33.7 to 13.0, and GPT-5 Nano costs 11.0x less per output token ($0.40 vs $4.40 per million). Updated October 21, 2026; 62 benchmarks have a result for both.

Replace GLM 5.2 Replace GPT-5 Nano
GLM 5.2Z.ai
Coding
61.1
Overall
62.5
Output $/M
$4.40
60
GPT-5 NanoOpenAI
Coding
No data
Overall
35.6
Output $/M
$0.40
1
vs boards won of 62 shared, 1 tied

GLM 5.2 or GPT-5 Nano: which to pick

Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding.

Pick GPT-5 Nano for output tokens 11.0x cheaper ($0.40 vs $4.40 per million) and a lower cost per solved task ($0.109 against $0.86).

Model AGLM 5.2Z.ai61.1/1004 independent benchmarks$0.86 per solved taskModel BGPT-5 NanoOpenAINot benchmarkedNo coding benchmark$0.109 per solved task
Shared results
62
GLM 5.2 only
36
GPT-5 Nano only
25
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 5.2 61.1 from 4 benchmarks, GPT-5 Nano not benchmarked.
Widest category
Agentic work, 62.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 5.2 99.1% against GPT-5 Nano 36.5%, 62.6 points apart on a 0 to 100 scale.
Output price
GLM 5.2 $4.40 against GPT-5 Nano $0.40 per million output tokens, 11.0x apart.
Context window
GLM 5.2 takes 1.05M tokens, GPT-5 Nano 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 5.2BarsGPT-5 NanoTies
Coding41
Intelligence20
Agentic work10
Reasoning1101 tie
Math60
Knowledge90
Web design60
General210
All shared boards6011 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index41
Coding benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
SWE-bench VerifiedNot measured34.8%Only GPT-5 Nano measured
LMArena WebDev1604Not measuredOnly GLM 5.2 measured
AA Coding Index68.8Not measuredOnly GLM 5.2 measured
LiveCodeBench (Vals AI)69.5%70.2%GPT-5 Nano
SWE-bench Verified (Vals AI)82.8%Not measuredOnly GLM 5.2 measured
Vibe Code Bench (Vals AI)64.0%Not measuredOnly GLM 5.2 measured
Code migration (Vals AI)37.9%Not measuredOnly GLM 5.2 measured
ProgramBench (Vals AI)0.5%Not measuredOnly GLM 5.2 measured
Terminal-Bench 2.1 (Vals AI)67.8%Not measuredOnly GLM 5.2 measured
SkillsBench (Vals AI)45.1%Not measuredOnly GLM 5.2 measured
LiveCodeBench (Artificial Analysis run)Not measured78.9%Only GPT-5 Nano measured
SciCode51.2%Not measuredOnly GLM 5.2 measured
Terminal-Bench Hard (Artificial Analysis run)50.8%17.4%GLM 5.2
Terminal-BenchNot measured21.8%Only GPT-5 Nano measured
FrontierCode24.5%Not measuredOnly GLM 5.2 measured
WeirdML70.1%38.1%GLM 5.2
ALE-Bench1047.0Not measuredOnly GLM 5.2 measured
LMArena Text: coding14871351GLM 5.2
LMArena Text: software and IT14861345GLM 5.2
LMArena WebDev: React1617Not measuredOnly GLM 5.2 measured
LMArena WebDev: HTML1565Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 18 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
AA Intelligence Index33.713.0GLM 5.2
Epoch Capabilities Index151.9139.2GLM 5.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
AA Agentic Index38.4Not measuredOnly GLM 5.2 measured
Design Arena: agents building web apps1222Not measuredOnly GLM 5.2 measured
Design Arena: agents building full-stack apps1241Not measuredOnly GLM 5.2 measured
Design Arena: agents building mobile apps1160Not measuredOnly GLM 5.2 measured
Design Arena: agents building Android apps1178Not measuredOnly GLM 5.2 measured
Design Arena: agents building Godot games1142Not measuredOnly GLM 5.2 measured
Design Arena: agents building games1206Not measuredOnly GLM 5.2 measured
Design Arena: HTML slides1175Not measuredOnly GLM 5.2 measured
Design Arena: PowerPoint slides1195Not measuredOnly GLM 5.2 measured
SREBench (Vals AI)0.0%Not measuredOnly GLM 5.2 measured
Finance Agent (Vals AI)49.7%Not measuredOnly GLM 5.2 measured
Legal Research Bench (Vals AI)31.3%Not measuredOnly GLM 5.2 measured
Excel Modeling Benchmark (Vals AI)61.5%Not measuredOnly GLM 5.2 measured
τ²-Bench Telecom99.1%36.5%GLM 5.2
APEX-Agents45.2%Not measuredOnly GLM 5.2 measured
PostTrainBench31.7%Not measuredOnly GLM 5.2 measured
Vending-Bench 2$8,314Not measuredOnly GLM 5.2 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured51.4%Only GPT-5 Nano measured
Neither model has a result on the other 23 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
Reasoning benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
GPQA Diamond85.6%63.4%GLM 5.2
MMMU Pro (Vals AI)Not measured70.9%Only GPT-5 Nano measured
GPQA Diamond (Artificial Analysis run)89.5%67.6%GLM 5.2
Humanity's Last Exam (Artificial Analysis run)41.1%9.5%GLM 5.2
GPQA Diamond (Epoch AI run)91.9%69.4%GLM 5.2
ARC-AGI77.0%20.7%GLM 5.2
ARC-AGI-222.8%2.6%GLM 5.2
SimpleBench58.8%Not measuredOnly GLM 5.2 measured
Chess puzzles22.0%22.0%Tie
Mystery game puzzles19.0%9.0%GLM 5.2
EBR-bench9.5%Not measuredOnly GLM 5.2 measured
VPCTNot measured37.2%Only GPT-5 Nano measured
LMArena Text: hard prompts14811328GLM 5.2
LMArena Text: expert14871321GLM 5.2
LMArena Text: hard prompts in English14811338GLM 5.2
LMArena Vision: diagramsNot measured1141Only GPT-5 Nano measured
LMArena Vision: homeworkNot measured1157Only GPT-5 Nano measured
Kagi LLM Benchmark62.6%62.2%GLM 5.2
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index60
Math benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
AIMENot measured81.2%Only GPT-5 Nano measured
MATH 500 (Vals AI)Not measured93.8%Only GPT-5 Nano measured
MGSM (Vals AI)Not measured89.3%Only GPT-5 Nano measured
AA Math IndexNot measured83.7Only GPT-5 Nano measured
AIME 2025 (Artificial Analysis run)Not measured83.7%Only GPT-5 Nano measured
OTIS Mock AIME86.4%81.1%GLM 5.2
FrontierMath, tiers 1 to 359.2%20.0%GLM 5.2
FrontierMath, tier 429.3%2.4%GLM 5.2
ProofBench35.0%12.0%GLM 5.2
MATH level 5Not measured95.2%Only GPT-5 Nano measured
LMArena Text: math14831317GLM 5.2
LMArena Text: mathematical work14891326GLM 5.2
Neither model has a result on the other 1 board here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
Knowledge benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
MMLU Pro86.7%76.1%GLM 5.2
LegalBench (Vals AI)84.1%50.1%GLM 5.2
CaseLaw (Vals AI)Not measured52.6%Only GPT-5 Nano measured
CorpFin (Vals AI)66.1%Not measuredOnly GLM 5.2 measured
TaxEval (Vals AI)73.3%67.4%GLM 5.2
MortgageTax (Vals AI)Not measured53.6%Only GPT-5 Nano measured
MedQA (Vals AI)Not measured93.3%Only GPT-5 Nano measured
MedCode (Vals AI)40.8%30.4%GLM 5.2
MMLU Pro (Artificial Analysis run)Not measured78.0%Only GPT-5 Nano measured
SimpleQA Verified34.2%11.7%GLM 5.2
LMArena Text: science14891318GLM 5.2
LMArena Text: medicine and healthcare14751335GLM 5.2
LMArena Text: legal and government14861298GLM 5.2
LMArena Text: business and finance14511311GLM 5.2
Neither model has a result on the other 4 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index60
Web design benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
Design Arena: websites12851101GLM 5.2
Design Arena: UI components12961067GLM 5.2
Design Arena: game dev12681045GLM 5.2
Design Arena: data viz12851056GLM 5.2
Design Arena: 3D1288966GLM 5.2
Design Arena: all code categories12881084GLM 5.2
Design Arena: SVG1214Not measuredOnly GLM 5.2 measured
Design Arena: ASCII art1190Not measuredOnly GLM 5.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index210
General benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
SAGE (Vals AI)Not measured30.4%Only GPT-5 Nano measured
MedScribe (Vals AI)83.5%72.9%GLM 5.2
IFBench73.3%67.6%GLM 5.2
Long context reasoning (AA-LCR)78.3%45.0%GLM 5.2
DTBench93.6%62.7%GLM 5.2
LMCA45.8%7.9%GLM 5.2
Fiction.LiveBenchNot measured44.4%Only GPT-5 Nano measured
Surface Evolver Bench55.6%Not measuredOnly GLM 5.2 measured
LMArena Text14701320GLM 5.2
LMArena Text: instruction following14661306GLM 5.2
LMArena Text: creative writing14631249GLM 5.2
LMArena VisionNot measured1159Only GPT-5 Nano measured
LMArena Text: English14751321GLM 5.2
LMArena Text: non-English14591313GLM 5.2
LMArena Text: Chinese15221356GLM 5.2
LMArena Text: German14701327GLM 5.2
LMArena Text: Japanese14501227GLM 5.2
LMArena Text: Korean14401269GLM 5.2
LMArena Text: French1479Not measuredOnly GLM 5.2 measured
LMArena Text: Spanish14781360GLM 5.2
LMArena Text: Russian14681296GLM 5.2
LMArena Text: Polish14561314GLM 5.2
LMArena Text: multi-turn14691311GLM 5.2
LMArena Text: longer queries14801312GLM 5.2
LMArena Text: writing and language14631283GLM 5.2
LMArena Text: entertainment and media14501268GLM 5.2
LMArena Vision: EnglishNot measured1170Only GPT-5 Nano measured
LMArena Vision: ChineseNot measured1176Only GPT-5 Nano measured
LMArena Vision: reading textNot measured1152Only GPT-5 Nano measured
LMArena Vision: humorNot measured1110Only GPT-5 Nano measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.2 and GPT-5 Nano
BenchmarkGLM 5.2GPT-5 NanoLeader
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 5.2$0.0036Fits in one request
GPT-5 Nano$0.0003Fits in one request

GPT-5 Nano costs less

Repository review

50K input + 3K output tokens

GLM 5.2$0.083Fits in one request
GPT-5 Nano$0.0037Fits in one request

GPT-5 Nano costs less

Agent loop

200K input + 10K output tokens

GLM 5.2$0.324Fits in one request
GPT-5 Nano$0.014Fits in one request

GPT-5 Nano 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 5.2 and GPT-5 Nano
SpecificationZ.ai GLM 5.2OpenAI GPT-5 Nano
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedJune 16, 2026August 7, 2025
Context window1.05M400K
Input $/Mtok$1.40$0.05
Output $/Mtok$4.40$0.40
Coding index61.1Not benchmarked
Independent measurements behind it40
Cost per solved task$0.86$0.109
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.2 better than GPT-5 Nano for coding?

Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; GPT-5 Nano has none, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 11.0x cheaper ($0.40 vs $4.40 per million) and a lower cost per solved task ($0.109 against $0.86).

Which is cheaper, GLM 5.2 or GPT-5 Nano?

GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; GPT-5 Nano costs $0.05 and $0.40. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and GPT-5 Nano $0.109.

Which has the bigger context window, GLM 5.2 or GPT-5 Nano?

GLM 5.2, with 1.05M tokens against 400K.

How many benchmarks is the GLM 5.2 vs GPT-5 Nano comparison based on?

62 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and GPT-5 Nano's on 0. Where only one model has a result, the page says so instead of naming a winner.

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