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GLM 5 vs Qwen2.5 72B Instruct

Updated October 21, 2026. 37 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5 Replace Qwen2.5 72B Instruct
GLM 5Z.ai
Coding
41.7
Overall
27.9
Output $/M
$3.20
37
Qwen2.5 72B InstructQwen
Coding
No data
Overall
7.7
Output $/M
$0.40
0
vs boards won of 37 shared

Decision reading

Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; Qwen2.5 72B Instruct has none, so the two cannot be ranked on coding.

Qwen2.5 72B Instruct has the edge where output tokens cost 8.0x less.

Model AGLM 5Z.ai41.7/1003 independent benchmarks$0.63 per solved taskModel BQwen2.5 72B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
37
GLM 5 only
38
Qwen2.5 72B Instruct only
16
Like-for-like categories
7 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 5 41.7 from 3 benchmarks, Qwen2.5 72B Instruct not benchmarked.
Widest category
Agentic work, 63.7 points apart on average over 1 shared benchmark.
Widest single gap
OTIS Mock AIME: GLM 5 80.0% against Qwen2.5 72B Instruct 8.1%, 71.9 points apart on a 0 to 100 scale.
Output price
GLM 5 $3.20 against Qwen2.5 72B Instruct $0.40 per million output tokens, 8.0x apart.
Context window
GLM 5 takes 205K 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 5BarsQwen2.5 72B InstructTies
Coding40
Intelligence20
Agentic work10
Reasoning60
Math30
Knowledge40
General170
All shared boards370
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 5 and Qwen2.5 72B Instruct
BenchmarkGLM 5Qwen2.5 72B InstructLeader
SWE-bench Verified72.8%Not measuredOnly GLM 5 measured
SWE-bench Multilingual69.7%Not measuredOnly GLM 5 measured
LMArena WebDev1435Not measuredOnly GLM 5 measured
LiveCodeBench (Vals AI)81.9%Not measuredOnly GLM 5 measured
SWE-bench Verified (Vals AI)71.4%Not measuredOnly GLM 5 measured
Vibe Code Bench (Vals AI)23.4%Not measuredOnly GLM 5 measured
LiveCodeBench (Artificial Analysis run)Not measured27.6%Only Qwen2.5 72B Instruct measured
Terminal-Bench Hard (Artificial Analysis run)43.2%4.5%GLM 5
Terminal-Bench52.4%Not measuredOnly GLM 5 measured
WeirdML48.2%16.0%GLM 5
ALE-Bench765.6Not measuredOnly GLM 5 measured
LMArena Text: coding14611292GLM 5
LMArena Text: software and IT14601292GLM 5
LMArena WebDev: React1424Not measuredOnly GLM 5 measured
LMArena WebDev: HTML1450Not measuredOnly GLM 5 measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5 and Qwen2.5 72B Instruct
BenchmarkGLM 5Qwen2.5 72B InstructLeader
AA Intelligence Index27.97.7GLM 5
Epoch Capabilities Index145.8129.0GLM 5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 5 and Qwen2.5 72B Instruct
BenchmarkGLM 5Qwen2.5 72B InstructLeader
Design Arena: agents building full-stack apps1123Not measuredOnly GLM 5 measured
Design Arena: agents building mobile apps1136Not measuredOnly GLM 5 measured
Design Arena: agents building Android apps1153Not measuredOnly GLM 5 measured
Design Arena: agents building Godot games1144Not measuredOnly GLM 5 measured
Design Arena: HTML slides1148Not measuredOnly GLM 5 measured
τ²-Bench Telecom98.2%34.5%GLM 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
Vending-Bench 2$4,432Not measuredOnly GLM 5 measured
Neither model has a result on the other 29 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 5 and Qwen2.5 72B Instruct
BenchmarkGLM 5Qwen2.5 72B InstructLeader
GPQA Diamond83.3%Not measuredOnly GLM 5 measured
GPQA Diamond (Artificial Analysis run)82.0%49.1%GLM 5
Humanity's Last Exam (Artificial Analysis run)29.3%3.6%GLM 5
GPQA Diamond (Epoch AI run)87.8%49.1%GLM 5
ARC-AGI44.7%Not measuredOnly GLM 5 measured
ARC-AGI-24.9%Not measuredOnly GLM 5 measured
SimpleBench53.2%Not measuredOnly GLM 5 measured
BIG-Bench HardNot measured79.8%Only Qwen2.5 72B Instruct measured
LMArena Text: hard prompts14521271GLM 5
LMArena Text: expert14541245GLM 5
LMArena Text: hard prompts in English14601279GLM 5
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5 and Qwen2.5 72B Instruct
BenchmarkGLM 5Qwen2.5 72B InstructLeader
AIME91.7%Not measuredOnly GLM 5 measured
AA Math IndexNot measured14.0Only Qwen2.5 72B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured14.0%Only Qwen2.5 72B Instruct measured
OTIS Mock AIME80.0%8.1%GLM 5
MATH level 5Not measured63.2%Only Qwen2.5 72B Instruct measured
LMArena Text: math14401283GLM 5
LMArena Text: mathematical work14511290GLM 5
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 5 and Qwen2.5 72B Instruct
BenchmarkGLM 5Qwen2.5 72B InstructLeader
MMLU Pro86.0%Not measuredOnly GLM 5 measured
LegalBench (Vals AI)84.1%Not measuredOnly GLM 5 measured
CaseLaw (Vals AI)52.5%Not measuredOnly GLM 5 measured
CorpFin (Vals AI)62.9%Not measuredOnly GLM 5 measured
TaxEval (Vals AI)70.0%Not measuredOnly GLM 5 measured
MedQA (Vals AI)94.3%Not measuredOnly GLM 5 measured
MMLU Pro (Artificial Analysis run)Not measured72.0%Only Qwen2.5 72B Instruct measured
MMLUNot measured85.3%Only Qwen2.5 72B Instruct measured
AI2 Reasoning ChallengeNot measured94.5%Only Qwen2.5 72B Instruct measured
LMArena Text: science14601275GLM 5
LMArena Text: medicine and healthcare14551258GLM 5
LMArena Text: legal and government14531288GLM 5
LMArena Text: business and finance14301253GLM 5
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 5 and Qwen2.5 72B Instruct
BenchmarkGLM 5Qwen2.5 72B InstructLeader
Design Arena: websites1247Not measuredOnly GLM 5 measured
Design Arena: UI components1230Not measuredOnly GLM 5 measured
Design Arena: game dev1234Not measuredOnly GLM 5 measured
Design Arena: data viz1228Not measuredOnly GLM 5 measured
Design Arena: 3D1229Not measuredOnly GLM 5 measured
Design Arena: all code categories1246Not measuredOnly GLM 5 measured
Design Arena: SVG1168Not measuredOnly GLM 5 measured
Design Arena: ASCII art1146Not measuredOnly GLM 5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
General benchmarks for GLM 5 and Qwen2.5 72B Instruct
BenchmarkGLM 5Qwen2.5 72B InstructLeader
IFBench72.3%36.9%GLM 5
Long context reasoning (AA-LCR)75.7%Not measuredOnly GLM 5 measured
DTBenchNot measured62.9%Only Qwen2.5 72B Instruct measured
LMCANot measured13.4%Only Qwen2.5 72B Instruct measured
CL-bench18.7%Not measuredOnly GLM 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 Text14461269GLM 5
LMArena Text: instruction following14281254GLM 5
LMArena Text: creative writing14381221GLM 5
LMArena Text: English14581283GLM 5
LMArena Text: non-English14301252GLM 5
LMArena Text: Chinese15111273GLM 5
LMArena Text: German14471234GLM 5
LMArena Text: Japanese14161180GLM 5
LMArena Text: Korean14231188GLM 5
LMArena Text: French14561280GLM 5
LMArena Text: Spanish14541255GLM 5
LMArena Text: Russian14361264GLM 5
LMArena Text: Polish1434Not measuredOnly GLM 5 measured
LMArena Text: multi-turn14561272GLM 5
LMArena Text: longer queries14461281GLM 5
LMArena Text: writing and language14371247GLM 5
LMArena Text: entertainment and media14261211GLM 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 5 and Qwen2.5 72B Instruct
BenchmarkGLM 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 5$0.0026Fits 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 5$0.060Fits 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 5$0.232Exceeds 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 5 and Qwen2.5 72B Instruct
SpecificationZ.ai GLM 5Qwen2.5 72B Instruct
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedFebruary 11, 2026September 19, 2024
Context window205K33K
Input $/Mtok$1.00$0.36
Output $/Mtok$3.20$0.40
Coding index41.7Not benchmarked
Independent measurements behind it30
Cost per solved task$0.63Not computable
SourceOpenRouter listingOpenRouter listing

Questions

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

Only GLM 5 has a coding score, 41.7 on the Vibe Coding Index; Qwen2.5 72B Instruct has none, so the two cannot be ranked on coding. Qwen2.5 72B Instruct has the edge where output tokens cost 8.0x less.

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

GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; Qwen2.5 72B Instruct costs $0.36 and $0.40. Cost per solved task is known for GLM 5 only.

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

GLM 5, with 205K tokens against 33K.

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

37 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 3 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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