LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

GLM 5.3 Flash vs Qwen2.5 Coder 32B Instruct

Updated October 21, 2026. 26 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 5.3 Flash Replace Qwen2.5 Coder 32B Instruct
GLM 5.3 FlashZ.ai
Coding
71.0
Overall
41.8
Output $/M
$0.50
26
Qwen2.5 Coder 32B InstructQwen
Coding
30.5
Overall
6.7
Output $/M
$1.00
0
vs boards won of 26 shared

Decision reading

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 30.5 for Qwen2.5 Coder 32B Instruct.

Qwen2.5 Coder 32B Instruct is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Model AGLM 5.3 FlashZ.ai71.0/1002 independent benchmarks$0.02 per solved taskModel BQwen2.5 Coder 32B InstructQwen30.5/1002 independent benchmarksNo cost per solved task
Shared results
26
GLM 5.3 Flash only
76
Qwen2.5 Coder 32B Instruct only
9
Like-for-like categories
6 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 5.3 Flash 71.0 from 2 benchmarks, Qwen2.5 Coder 32B Instruct 30.5 from 2 benchmarks.
Widest category
General, 42.8 points apart on average over 11 shared benchmarks.
Widest single gap
LMArena Text: Chinese: GLM 5.3 Flash 1531 against Qwen2.5 Coder 32B Instruct 1222, 51.5 points apart on a 0 to 100 scale.
Output price
GLM 5.3 Flash $0.50 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 2.0x apart.
Context window
GLM 5.3 Flash takes 1.05M tokens, Qwen2.5 Coder 32B 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 5.3 FlashBarsQwen2.5 Coder 32B InstructTies
Coding20
Intelligence20
Reasoning50
Math20
Knowledge40
General110
All shared boards260
CodingLike-for-likeFour of these boards feed the Vibe Coding Index20
Coding benchmarks for GLM 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B InstructLeader
Aider polyglotNot measured16.4%Only Qwen2.5 Coder 32B Instruct measured
SWE-bench VerifiedNot measured47.0%Only Qwen2.5 Coder 32B Instruct measured
LiveBench coding80.6%Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev1607Not measuredOnly GLM 5.3 Flash measured
AA Coding Index71.5Not measuredOnly GLM 5.3 Flash measured
LiveCodeBench (Vals AI)80.5%Not measuredOnly GLM 5.3 Flash measured
SWE-bench Verified (Vals AI)92.0%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench 4 (Vals AI)37.4%Not measuredOnly GLM 5.3 Flash measured
Vibe Code Bench (Vals AI)30.8%Not measuredOnly GLM 5.3 Flash measured
Code migration (Vals AI)20.5%Not measuredOnly GLM 5.3 Flash measured
ProgramBench (Vals AI)0.0%Not measuredOnly GLM 5.3 Flash measured
IOI (Vals AI)52.5%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench 2.1 (Vals AI)62.9%Not measuredOnly GLM 5.3 Flash measured
Vibe Code Bench 1-100 (Vals AI)16.0%Not measuredOnly GLM 5.3 Flash measured
SkillsBench (Vals AI)40.2%Not measuredOnly GLM 5.3 Flash measured
CyberBench (Vals AI)67.9%Not measuredOnly GLM 5.3 Flash measured
LiveCodeBench (Artificial Analysis run)Not measured29.5%Only Qwen2.5 Coder 32B Instruct measured
SciCode51.6%Not measuredOnly GLM 5.3 Flash measured
FrontierCode31.8%Not measuredOnly GLM 5.3 Flash measured
FrontierSWE18.1%Not measuredOnly GLM 5.3 Flash measured
ALE-Bench303.6Not measuredOnly GLM 5.3 Flash measured
LMArena Text: coding15071276GLM 5.3 Flash
LMArena Text: software and IT15001261GLM 5.3 Flash
LMArena WebDev: React1624Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev: HTML1577Not measuredOnly GLM 5.3 Flash measured
LMArena WebDev: from an image1578Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 12 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GLM 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B InstructLeader
AA Intelligence Index41.86.7GLM 5.3 Flash
Epoch Capabilities Index153.1119.5GLM 5.3 Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
Agentic work benchmarks for GLM 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B InstructLeader
AA Agentic Index50.9Not measuredOnly GLM 5.3 Flash measured
LiveBench research70.1%Not measuredOnly GLM 5.3 Flash measured
LiveBench browser84.0%Not measuredOnly GLM 5.3 Flash measured
LiveBench long agentic28.4%Not measuredOnly GLM 5.3 Flash measured
SREBench (Vals AI)0.8%Not measuredOnly GLM 5.3 Flash measured
Finance Agent (Vals AI)57.9%Not measuredOnly GLM 5.3 Flash measured
Tax Agent Bench (Vals AI)31.6%Not measuredOnly GLM 5.3 Flash measured
Legal Research Bench (Vals AI)45.2%Not measuredOnly GLM 5.3 Flash measured
Harvey's Legal Agent Benchmark (Vals AI)10.4%Not measuredOnly GLM 5.3 Flash measured
Excel Modeling Benchmark (Vals AI)55.9%Not measuredOnly GLM 5.3 Flash measured
Terminal-Bench-Science (Vals AI)2.9%Not measuredOnly GLM 5.3 Flash measured
APEX-Agents52.8%Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 27 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B InstructLeader
GPQA Diamond86.4%Not measuredOnly GLM 5.3 Flash measured
LiveBench reasoning77.6%Not measuredOnly GLM 5.3 Flash measured
MMMU Pro (Vals AI)86.0%Not measuredOnly GLM 5.3 Flash measured
GPQA Diamond (Artificial Analysis run)91.2%41.7%GLM 5.3 Flash
Humanity's Last Exam (Artificial Analysis run)39.9%3.5%GLM 5.3 Flash
GPQA Diamond (Epoch AI run)90.2%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI91.0%Not measuredOnly GLM 5.3 Flash measured
ARC-AGI-265.8%Not measuredOnly GLM 5.3 Flash measured
Chess puzzles21.0%Not measuredOnly GLM 5.3 Flash measured
Mystery game puzzles8.0%Not measuredOnly GLM 5.3 Flash measured
LMArena Text: hard prompts14901251GLM 5.3 Flash
LMArena Text: expert15111221GLM 5.3 Flash
LMArena Text: hard prompts in English14881269GLM 5.3 Flash
LMArena Vision: diagrams1317Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: homework1318Not measuredOnly GLM 5.3 Flash measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B InstructLeader
OTIS Mock AIME93.9%Not measuredOnly GLM 5.3 Flash measured
FrontierMath, tiers 1 to 355.8%Not measuredOnly GLM 5.3 Flash measured
FrontierMath, tier 417.1%Not measuredOnly GLM 5.3 Flash measured
ProofBench21.0%Not measuredOnly GLM 5.3 Flash measured
GSM8KNot measured93.0%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: math15031251GLM 5.3 Flash
LMArena Text: mathematical work14971243GLM 5.3 Flash
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 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B InstructLeader
MMLU Pro86.1%Not measuredOnly GLM 5.3 Flash measured
LegalBench (Vals AI)83.9%Not measuredOnly GLM 5.3 Flash measured
TaxEval (Vals AI)75.6%Not measuredOnly GLM 5.3 Flash measured
MMLU Pro (Artificial Analysis run)Not measured63.5%Only Qwen2.5 Coder 32B Instruct measured
MMLUNot measured79.1%Only Qwen2.5 Coder 32B Instruct measured
AI2 Reasoning ChallengeNot measured70.5%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: science14841221GLM 5.3 Flash
LMArena Text: medicine and healthcare14751187GLM 5.3 Flash
LMArena Text: legal and government14731257GLM 5.3 Flash
LMArena Text: business and finance14601224GLM 5.3 Flash
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GLM 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B InstructLeader
Design Arena: websites1272Not measuredOnly GLM 5.3 Flash measured
Design Arena: UI components1316Not measuredOnly GLM 5.3 Flash measured
Design Arena: game dev1279Not measuredOnly GLM 5.3 Flash measured
Design Arena: data viz1262Not measuredOnly GLM 5.3 Flash measured
Design Arena: 3D1316Not measuredOnly GLM 5.3 Flash measured
Design Arena: all code categories1282Not measuredOnly GLM 5.3 Flash measured
Design Arena: SVG1284Not measuredOnly GLM 5.3 Flash measured
Design Arena: ASCII art1269Not measuredOnly GLM 5.3 Flash measured
LiveBench design77.2%Not measuredOnly GLM 5.3 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
General benchmarks for GLM 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B InstructLeader
Vals Index50.9%Not measuredOnly GLM 5.3 Flash measured
LiveBench data analysis76.4%Not measuredOnly GLM 5.3 Flash measured
MedScribe (Vals AI)88.9%Not measuredOnly GLM 5.3 Flash measured
Long context reasoning (AA-LCR)80.0%Not measuredOnly GLM 5.3 Flash measured
Surface Evolver Bench52.5%Not measuredOnly GLM 5.3 Flash measured
HellaSwagNot measured83.0%Only Qwen2.5 Coder 32B Instruct measured
WinoGrandeNot measured80.8%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text14711230GLM 5.3 Flash
LMArena Text: instruction following14781223GLM 5.3 Flash
LMArena Text: creative writing14411174GLM 5.3 Flash
LMArena Vision1294Not measuredOnly GLM 5.3 Flash measured
LMArena Text: English14741248GLM 5.3 Flash
LMArena Text: non-English14621205GLM 5.3 Flash
LMArena Text: Chinese15311222GLM 5.3 Flash
LMArena Text: German1462Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Japanese1444Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Korean1436Not measuredOnly GLM 5.3 Flash measured
LMArena Text: French1491Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Spanish1475Not measuredOnly GLM 5.3 Flash measured
LMArena Text: Russian14651228GLM 5.3 Flash
LMArena Text: Polish1450Not measuredOnly GLM 5.3 Flash measured
LMArena Text: multi-turn14691222GLM 5.3 Flash
LMArena Text: longer queries14811251GLM 5.3 Flash
LMArena Text: writing and language14551191GLM 5.3 Flash
LMArena Text: entertainment and media14401150GLM 5.3 Flash
LMArena Vision: English1295Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: Chinese1363Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: reading text1299Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: humor1279Not measuredOnly GLM 5.3 Flash measured
LMArena Vision: creative writing1270Not measuredOnly GLM 5.3 Flash 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 5.3 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 5.3 FlashQwen2.5 Coder 32B 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 5.3 Flash$0.0004Fits in one request
Qwen2.5 Coder 32B Instruct$0.0012Fits in one request

GLM 5.3 Flash costs less

Repository review

50K input + 3K output tokens

GLM 5.3 Flash$0.0090Fits in one request
Qwen2.5 Coder 32B Instruct$0.036Exceeds the context window

GLM 5.3 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 5.3 Flash$0.035Fits in one request
Qwen2.5 Coder 32B Instruct$0.142Exceeds the context window

GLM 5.3 Flash 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 5.3 Flash and Qwen2.5 Coder 32B Instruct
SpecificationZ.ai GLM 5.3 FlashQwen2.5 Coder 32B Instruct
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedAugust 26, 2026November 11, 2024
Context window1.05M33K
Input $/Mtok$0.15$0.66
Output $/Mtok$0.50$1.00
Coding index71.030.5
Independent measurements behind it22
Cost per solved task$0.02Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.3 Flash better than Qwen2.5 Coder 32B Instruct for coding?

Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 30.5 for Qwen2.5 Coder 32B Instruct. Qwen2.5 Coder 32B Instruct is neither cheaper nor larger in context, and leads no benchmark both models were measured on.

Which is cheaper, GLM 5.3 Flash or Qwen2.5 Coder 32B Instruct?

GLM 5.3 Flash costs $0.15 per million input tokens and $0.50 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for GLM 5.3 Flash only.

Which has the bigger context window, GLM 5.3 Flash or Qwen2.5 Coder 32B Instruct?

GLM 5.3 Flash, with 1.05M tokens against 33K.

How many benchmarks is the GLM 5.3 Flash vs Qwen2.5 Coder 32B Instruct comparison based on?

26 benchmarks have a result for both models as of October 21, 2026. GLM 5.3 Flash's coding index rests on 2 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 2. Where only one model has a result, the page says so instead of naming a winner.

Compare next

Embed this comparison on your site

Free under CC BY 4.0, while the source link stays visible. Preview the frame · Terms and other embeds

<iframe src="https://llmbenchmark.ai/embed/compare/glm-5-3-flash-vs-qwen-2-5-coder-32b-instruct" title="GLM 5.3 Flash vs Qwen2.5 Coder 32B Instruct, LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/glm-5-3-flash-vs-qwen-2-5-coder-32b-instruct">LLMBenchmark.ai, GLM 5.3 Flash vs Qwen2.5 Coder 32B Instruct</a></p>