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GLM 4.5 vs Qwen2.5 72B Instruct

GLM 4.5 leads Qwen2.5 72B Instruct on the Artificial Analysis Intelligence Index, 12.8 to 7.7, and Qwen2.5 72B Instruct costs 5.5x less per output token ($0.40 vs $2.20 per million). Updated October 21, 2026; 38 benchmarks have a result for both.

Replace GLM 4.5 Replace Qwen2.5 72B Instruct
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
38
Qwen2.5 72B InstructQwen
Coding
No data
Overall
26.5
Output $/M
$0.40
0
vs boards won of 38 shared

GLM 4.5 or Qwen2.5 72B Instruct: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GLM 4.5 for a larger context window (131K against 33K tokens).

Model AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 72B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
38
GLM 4.5 only
23
Qwen2.5 72B Instruct only
15
Like-for-like categories
7 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 4.5 not benchmarked, Qwen2.5 72B Instruct not benchmarked.
Widest category
Math, 41.7 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: GLM 4.5 73.7 against Qwen2.5 72B Instruct 14.0, 59.7 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Qwen2.5 72B Instruct $0.40 per million output tokens, 5.5x apart.
Context window
GLM 4.5 takes 131K tokens, Qwen2.5 72B Instruct 33K.

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 4.5BarsQwen2.5 72B InstructTies
Coding50
Intelligence10
Agentic work10
Reasoning50
Math40
Knowledge50
General170
All shared boards380
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
Coding benchmarks for GLM 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%27.6%GLM 4.5
Terminal-Bench Hard (Artificial Analysis run)22.0%4.5%GLM 4.5
WeirdML40.6%16.0%GLM 4.5
ALE-Bench344.8Not measuredOnly GLM 4.5 measured
LMArena Text: coding14341292GLM 4.5
LMArena Text: software and IT14401292GLM 4.5
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
AA Intelligence Index12.87.7GLM 4.5
Epoch Capabilities IndexNot measured129.0Only Qwen2.5 72B Instruct measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
τ²-Bench Telecom43.0%34.5%GLM 4.5
METR time horizonsNot measured35.8%Only Qwen2.5 72B Instruct measured
BALROGNot measured16.2%Only Qwen2.5 72B Instruct measured
TheAgentCompanyNot measured5.7%Only Qwen2.5 72B Instruct measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%49.1%GLM 4.5
Humanity's Last Exam (Artificial Analysis run)13.0%3.6%GLM 4.5
GPQA Diamond (Epoch AI run)Not measured49.1%Only Qwen2.5 72B Instruct measured
BIG-Bench HardNot measured79.8%Only Qwen2.5 72B Instruct measured
LMArena Text: hard prompts14291271GLM 4.5
LMArena Text: expert14331245GLM 4.5
LMArena Text: hard prompts in English14291279GLM 4.5
Kagi LLM Benchmark57.9%Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for GLM 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
AIME86.7%Not measuredOnly GLM 4.5 measured
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.714.0GLM 4.5
AIME 2025 (Artificial Analysis run)73.7%14.0%GLM 4.5
OTIS Mock AIMENot measured8.1%Only Qwen2.5 72B Instruct measured
MATH level 5Not measured63.2%Only Qwen2.5 72B Instruct measured
LMArena Text: math14271283GLM 4.5
LMArena Text: mathematical work14301290GLM 4.5
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GLM 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
MMLU Pro81.2%Not measuredOnly GLM 4.5 measured
LegalBench (Vals AI)75.6%Not measuredOnly GLM 4.5 measured
CorpFin (Vals AI)61.0%Not measuredOnly GLM 4.5 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 4.5 measured
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MMLU Pro (Artificial Analysis run)83.5%72.0%GLM 4.5
MMLUNot measured85.3%Only Qwen2.5 72B Instruct measured
AI2 Reasoning ChallengeNot measured94.5%Only Qwen2.5 72B Instruct measured
LMArena Text: science14441275GLM 4.5
LMArena Text: medicine and healthcare14551258GLM 4.5
LMArena Text: legal and government14381288GLM 4.5
LMArena Text: business and finance14201253GLM 4.5
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
Design Arena: websites1170Not measuredOnly GLM 4.5 measured
Design Arena: UI components1144Not measuredOnly GLM 4.5 measured
Design Arena: game dev1145Not measuredOnly GLM 4.5 measured
Design Arena: data viz1159Not measuredOnly GLM 4.5 measured
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories1165Not measuredOnly GLM 4.5 measured
Design Arena: SVG1096Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
General benchmarks for GLM 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
IFBench44.1%36.9%GLM 4.5
Long context reasoning (AA-LCR)52.7%Not measuredOnly GLM 4.5 measured
DTBenchNot measured62.9%Only Qwen2.5 72B Instruct measured
LMCANot measured13.4%Only Qwen2.5 72B Instruct measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
HellaSwagNot measured84.8%Only Qwen2.5 72B Instruct measured
WinoGrandeNot measured82.3%Only Qwen2.5 72B Instruct measured
PIQANot measured82.6%Only Qwen2.5 72B Instruct measured
LMArena Text14301269GLM 4.5
LMArena Text: instruction following14041254GLM 4.5
LMArena Text: creative writing13951221GLM 4.5
LMArena Text: English14371283GLM 4.5
LMArena Text: non-English14171252GLM 4.5
LMArena Text: Chinese14651273GLM 4.5
LMArena Text: German14071234GLM 4.5
LMArena Text: Japanese14151180GLM 4.5
LMArena Text: Korean13801188GLM 4.5
LMArena Text: French14181280GLM 4.5
LMArena Text: Spanish14541255GLM 4.5
LMArena Text: Russian14131264GLM 4.5
LMArena Text: Polish1422Not measuredOnly GLM 4.5 measured
LMArena Text: multi-turn14151272GLM 4.5
LMArena Text: longer queries14121281GLM 4.5
LMArena Text: writing and language14031247GLM 4.5
LMArena Text: entertainment and media14041211GLM 4.5
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 4.5 and Qwen2.5 72B Instruct
BenchmarkGLM 4.5Qwen2.5 72B InstructLeader
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 4.5$0.0017Fits in one request
Qwen2.5 72B Instruct$0.0006Fits in one request

Qwen2.5 72B Instruct costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Qwen2.5 72B Instruct$0.019Exceeds the context window

Qwen2.5 72B Instruct costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
Qwen2.5 72B Instruct$0.076Exceeds the context window

Qwen2.5 72B Instruct 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 4.5 and Qwen2.5 72B Instruct
SpecificationZ.ai GLM 4.5Qwen2.5 72B Instruct
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025September 19, 2024
Context window131K33K
Input $/Mtok$0.60$0.36
Output $/Mtok$2.20$0.40
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Qwen2.5 72B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for a larger context window (131K against 33K tokens).

Which is cheaper, GLM 4.5 or Qwen2.5 72B Instruct?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5 72B Instruct costs $0.36 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5 or Qwen2.5 72B Instruct?

GLM 4.5, with 131K tokens against 33K.

How many benchmarks is the GLM 4.5 vs Qwen2.5 72B Instruct comparison based on?

38 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and Qwen2.5 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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