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GLM 4.7 Flash vs Qwen2.5 Coder 32B Instruct

Updated October 21, 2026. 25 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.7 Flash Replace Qwen2.5 Coder 32B Instruct
GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
25
Qwen2.5 Coder 32B InstructQwen
Coding
No data
Overall
6.7
Output $/M
$1.00
0
vs boards won of 25 shared

Decision reading

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

GLM 4.7 Flash has the edge where output tokens cost 2.5x less, and where the context window is larger, 200K against 33K tokens.

Model AGLM 4.7 FlashZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 Coder 32B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
25
GLM 4.7 Flash only
18
Qwen2.5 Coder 32B Instruct only
10
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 4.7 Flash not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
Widest category
Knowledge, 21.4 points apart on average over 4 shared benchmarks.
Widest single gap
LMArena Text: Chinese: GLM 4.7 Flash 1403 against Qwen2.5 Coder 32B Instruct 1222, 30.1 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 2.5x apart.
Context window
GLM 4.7 Flash takes 200K 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 4.7 FlashBarsQwen2.5 Coder 32B InstructTies
Coding20
Intelligence10
Reasoning50
Math20
Knowledge40
General110
All shared boards250
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 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
LiveCodeBench (Artificial Analysis run)Not measured29.5%Only Qwen2.5 Coder 32B Instruct measured
Terminal-Bench Hard (Artificial Analysis run)22.0%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: coding13821276GLM 4.7 Flash
LMArena Text: software and IT13891261GLM 4.7 Flash
Neither model has a result on the other 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 FlashQwen2.5 Coder 32B InstructLeader
AA Intelligence Index14.96.7GLM 4.7 Flash
Epoch Capabilities IndexNot measured119.5Only Qwen2.5 Coder 32B Instruct measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 FlashQwen2.5 Coder 32B InstructLeader
τ²-Bench Telecom98.8%Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 FlashQwen2.5 Coder 32B InstructLeader
GPQA Diamond (Artificial Analysis run)58.1%41.7%GLM 4.7 Flash
Humanity's Last Exam (Artificial Analysis run)7.6%3.5%GLM 4.7 Flash
GPQA Diamond (Epoch AI run)60.5%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: hard prompts13561251GLM 4.7 Flash
LMArena Text: expert13571221GLM 4.7 Flash
LMArena Text: hard prompts in English13691269GLM 4.7 Flash
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 FlashQwen2.5 Coder 32B InstructLeader
OTIS Mock AIME58.3%Not measuredOnly GLM 4.7 Flash measured
GSM8KNot measured93.0%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: math13551251GLM 4.7 Flash
LMArena Text: mathematical work13691243GLM 4.7 Flash
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 FlashQwen2.5 Coder 32B InstructLeader
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: science13581221GLM 4.7 Flash
LMArena Text: medicine and healthcare13431187GLM 4.7 Flash
LMArena Text: legal and government13571257GLM 4.7 Flash
LMArena Text: business and finance13431224GLM 4.7 Flash
Neither model has a result on the other 10 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 FlashQwen2.5 Coder 32B InstructLeader
Design Arena: websites1194Not measuredOnly GLM 4.7 Flash measured
Design Arena: UI components1208Not measuredOnly GLM 4.7 Flash measured
Design Arena: game dev1135Not measuredOnly GLM 4.7 Flash measured
Design Arena: data viz1123Not measuredOnly GLM 4.7 Flash measured
Design Arena: 3D1121Not measuredOnly GLM 4.7 Flash measured
Design Arena: all code categories1178Not measuredOnly GLM 4.7 Flash measured
Design Arena: SVG1038Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
General benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 FlashQwen2.5 Coder 32B InstructLeader
IFBench60.8%Not measuredOnly GLM 4.7 Flash measured
Long context reasoning (AA-LCR)41.7%Not measuredOnly GLM 4.7 Flash measured
HellaSwagNot measured83.0%Only Qwen2.5 Coder 32B Instruct measured
WinoGrandeNot measured80.8%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text13511230GLM 4.7 Flash
LMArena Text: instruction following13271223GLM 4.7 Flash
LMArena Text: creative writing12961174GLM 4.7 Flash
LMArena Text: English13731248GLM 4.7 Flash
LMArena Text: non-English13301205GLM 4.7 Flash
LMArena Text: Chinese14031222GLM 4.7 Flash
LMArena Text: German1336Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Korean1283Not measuredOnly GLM 4.7 Flash measured
LMArena Text: French1332Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Spanish1350Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Russian13331228GLM 4.7 Flash
LMArena Text: Polish1291Not measuredOnly GLM 4.7 Flash measured
LMArena Text: multi-turn13421222GLM 4.7 Flash
LMArena Text: longer queries13451251GLM 4.7 Flash
LMArena Text: writing and language13201191GLM 4.7 Flash
LMArena Text: entertainment and media13041150GLM 4.7 Flash
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 Flash and Qwen2.5 Coder 32B Instruct
BenchmarkGLM 4.7 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 4.7 Flash$0.0003Fits in one request
Qwen2.5 Coder 32B Instruct$0.0012Fits in one request

GLM 4.7 Flash costs less

Repository review

50K input + 3K output tokens

GLM 4.7 Flash$0.0045Fits in one request
Qwen2.5 Coder 32B Instruct$0.036Exceeds the context window

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
Qwen2.5 Coder 32B Instruct$0.142Exceeds the context window

GLM 4.7 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 4.7 Flash and Qwen2.5 Coder 32B Instruct
SpecificationZ.ai GLM 4.7 FlashQwen2.5 Coder 32B Instruct
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026November 11, 2024
Context window200K33K
Input $/Mtok$0.0653$0.66
Output $/Mtok$0.40$1.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 Flash better than Qwen2.5 Coder 32B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where output tokens cost 2.5x less, and where the context window is larger, 200K against 33K tokens.

Which is cheaper, GLM 4.7 Flash or Qwen2.5 Coder 32B Instruct?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.7 Flash or Qwen2.5 Coder 32B Instruct?

GLM 4.7 Flash, with 200K tokens against 33K.

How many benchmarks is the GLM 4.7 Flash vs Qwen2.5 Coder 32B Instruct comparison based on?

25 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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