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GLM 4.7 Flash vs Qwen3 Coder Next

GLM 4.7 Flash leads Qwen3 Coder Next on the Artificial Analysis Intelligence Index, 14.9 to 9.2, and GLM 4.7 Flash costs 2.0x less per output token ($0.40 vs $0.80 per million). Updated October 21, 2026; 7 benchmarks have a result for both.

GLM 4.7 FlashZ.ai
Coding
No data
Overall
14.9
Output $/M
$0.40
4
Qwen3 Coder NextQwen
Coding
No data
Overall
9.2
Output $/M
$0.80
3
vs boards won of 7 shared

GLM 4.7 Flash or Qwen3 Coder Next: which to pick

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

Pick GLM 4.7 Flash for output tokens 2.0x cheaper ($0.40 vs $0.80 per million) and work measured by GPQA Diamond (Artificial Analysis run) or Humanity's Last Exam (Artificial Analysis run), where it leads.

Shared results
7
GLM 4.7 Flash only
36
Qwen3 Coder Next only
4
Like-for-like categories
5 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 4.7 Flash not benchmarked, Qwen3 Coder Next not benchmarked.
Widest category
Agentic work, 19.3 points apart on average over 1 shared benchmark.
Widest single gap
IFBench: GLM 4.7 Flash 60.8% against Qwen3 Coder Next 35.2%, 25.6 points apart on a 0 to 100 scale.
Output price
GLM 4.7 Flash $0.40 against Qwen3 Coder Next $0.80 per million output tokens, 2.0x apart.
Context window
GLM 4.7 Flash takes 200K tokens, Qwen3 Coder Next 262K.

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 FlashBarsQwen3 Coder NextTies
Coding10
Intelligence10
Agentic work10
Reasoning02
General11
All shared boards43
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
Coding benchmarks for GLM 4.7 Flash and Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
AA Coding IndexNot measured36.2Only Qwen3 Coder Next measured
SciCodeNot measured36.2%Only Qwen3 Coder Next measured
Terminal-Bench Hard (Artificial Analysis run)22.0%18.2%GLM 4.7 Flash
WeirdMLNot measured34.4%Only Qwen3 Coder Next measured
LMArena Text: coding1382Not measuredOnly GLM 4.7 Flash measured
LMArena Text: software and IT1389Not measuredOnly GLM 4.7 Flash measured
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 Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
AA Intelligence Index14.99.2GLM 4.7 Flash
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 4.7 Flash and Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
AA Agentic IndexNot measured0.9Only Qwen3 Coder Next measured
τ²-Bench Telecom98.8%79.5%GLM 4.7 Flash
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GLM 4.7 Flash and Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
GPQA Diamond (Artificial Analysis run)58.1%73.7%Qwen3 Coder Next
Humanity's Last Exam (Artificial Analysis run)7.6%10.1%Qwen3 Coder Next
GPQA Diamond (Epoch AI run)60.5%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: hard prompts1356Not measuredOnly GLM 4.7 Flash measured
LMArena Text: expert1357Not measuredOnly GLM 4.7 Flash measured
LMArena Text: hard prompts in English1369Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Math benchmarks for GLM 4.7 Flash and Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
OTIS Mock AIME58.3%Not measuredOnly GLM 4.7 Flash measured
LMArena Text: math1355Not measuredOnly GLM 4.7 Flash measured
LMArena Text: mathematical work1369Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 10 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Knowledge benchmarks for GLM 4.7 Flash and Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
LMArena Text: science1358Not measuredOnly GLM 4.7 Flash measured
LMArena Text: medicine and healthcare1343Not measuredOnly GLM 4.7 Flash measured
LMArena Text: legal and government1357Not measuredOnly GLM 4.7 Flash measured
LMArena Text: business and finance1343Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 13 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 Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
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 index11
General benchmarks for GLM 4.7 Flash and Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
IFBench60.8%35.2%GLM 4.7 Flash
Long context reasoning (AA-LCR)41.7%47.0%Qwen3 Coder Next
LMArena Text1351Not measuredOnly GLM 4.7 Flash measured
LMArena Text: instruction following1327Not measuredOnly GLM 4.7 Flash measured
LMArena Text: creative writing1296Not measuredOnly GLM 4.7 Flash measured
LMArena Text: English1373Not measuredOnly GLM 4.7 Flash measured
LMArena Text: non-English1330Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Chinese1403Not measuredOnly GLM 4.7 Flash measured
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: Russian1333Not measuredOnly GLM 4.7 Flash measured
LMArena Text: Polish1291Not measuredOnly GLM 4.7 Flash measured
LMArena Text: multi-turn1342Not measuredOnly GLM 4.7 Flash measured
LMArena Text: longer queries1345Not measuredOnly GLM 4.7 Flash measured
LMArena Text: writing and language1320Not measuredOnly GLM 4.7 Flash measured
LMArena Text: entertainment and media1304Not measuredOnly GLM 4.7 Flash measured
Neither model has a result on the other 28 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 Qwen3 Coder Next
BenchmarkGLM 4.7 FlashQwen3 Coder NextLeader
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
Qwen3 Coder Next$0.0005Fits 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
Qwen3 Coder Next$0.0084Fits in one request

GLM 4.7 Flash costs less

Agent loop

200K input + 10K output tokens

GLM 4.7 Flash$0.017Exceeds the context window
Qwen3 Coder Next$0.032Fits in one request

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 Qwen3 Coder Next
SpecificationZ.ai GLM 4.7 FlashQwen3 Coder Next
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJanuary 19, 2026February 4, 2026
Context window200K262K
Input $/Mtok$0.0653$0.12
Output $/Mtok$0.40$0.80
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 Qwen3 Coder Next for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 2.0x cheaper ($0.40 vs $0.80 per million) and work measured by GPQA Diamond (Artificial Analysis run) or Humanity's Last Exam (Artificial Analysis run), where it leads.

Which is cheaper, GLM 4.7 Flash or Qwen3 Coder Next?

GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; Qwen3 Coder Next costs $0.12 and $0.80. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.7 Flash or Qwen3 Coder Next?

Qwen3 Coder Next, with 262K tokens against 200K.

How many benchmarks is the GLM 4.7 Flash vs Qwen3 Coder Next comparison based on?

7 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 Qwen3 Coder Next's on 0. Where only one model has a result, the page says so instead of naming a winner.

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