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GLM 5.1 vs Qwen3 30B A3B Instruct 2507

GLM 5.1 leads Qwen3 30B A3B Instruct 2507 on the Artificial Analysis Intelligence Index, 26.1 to 7.5, and Qwen3 30B A3B Instruct 2507 costs 14.7x less per output token ($0.30 vs $4.40 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace GLM 5.1 Replace Qwen3 30B A3B Instruct 2507
GLM 5.1Z.ai
Coding
49.2
Overall
57.5
Output $/M
$4.40
38
Qwen3 30B A3B Instruct 2507Qwen
Coding
No data
Overall
41.5
Output $/M
$0.30
0
vs boards won of 38 shared

GLM 5.1 or Qwen3 30B A3B Instruct 2507: which to pick

Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; Qwen3 30B A3B Instruct 2507 has none, so the two cannot be ranked on coding.

Pick Qwen3 30B A3B Instruct 2507 for output tokens 14.7x cheaper ($0.30 vs $4.40 per million) and a larger context window (262K against 205K tokens).

Model AGLM 5.1Z.ai49.2/1003 independent benchmarks$0.60 per solved taskModel BQwen3 30B A3B Instruct 2507QwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
38
GLM 5.1 only
51
Qwen3 30B A3B Instruct 2507 only
7
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 5.1 49.2 from 3 benchmarks, Qwen3 30B A3B Instruct 2507 not benchmarked.
Widest category
Agentic work, 69.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 5.1 97.7% against Qwen3 30B A3B Instruct 2507 28.1%, 69.6 points apart on a 0 to 100 scale.
Output price
GLM 5.1 $4.40 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 14.7x apart.
Context window
GLM 5.1 takes 205K tokens, Qwen3 30B A3B Instruct 2507 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 5.1BarsQwen3 30B A3B Instruct 2507Ties
Coding40
Intelligence10
Agentic work10
Reasoning60
Math30
Knowledge40
General190
All shared boards380
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
LMArena WebDev1508Not measuredOnly GLM 5.1 measured
AA Coding Index55.8Not measuredOnly GLM 5.1 measured
LiveCodeBench (Vals AI)81.4%Not measuredOnly GLM 5.1 measured
SWE-bench Verified (Vals AI)76.4%Not measuredOnly GLM 5.1 measured
Vibe Code Bench (Vals AI)31.5%Not measuredOnly GLM 5.1 measured
Code migration (Vals AI)25.8%Not measuredOnly GLM 5.1 measured
ProgramBench (Vals AI)0.0%Not measuredOnly GLM 5.1 measured
Terminal-Bench 2.1 (Vals AI)56.9%Not measuredOnly GLM 5.1 measured
LiveCodeBench (Artificial Analysis run)Not measured70.7%Only Qwen3 30B A3B Instruct 2507 measured
SciCode44.8%33.0%GLM 5.1
Terminal-Bench Hard (Artificial Analysis run)43.2%6.1%GLM 5.1
WeirdML57.1%Not measuredOnly GLM 5.1 measured
ALE-Bench887.1Not measuredOnly GLM 5.1 measured
LMArena Text: coding14851416GLM 5.1
LMArena Text: software and IT14841417GLM 5.1
LMArena WebDev: React1505Not measuredOnly GLM 5.1 measured
LMArena WebDev: HTML1508Not measuredOnly GLM 5.1 measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
AA Intelligence Index26.17.5GLM 5.1
Epoch Capabilities Index149.8Not measuredOnly GLM 5.1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
AA Agentic Index23.9Not measuredOnly GLM 5.1 measured
Design Arena: agents building web apps1175Not measuredOnly GLM 5.1 measured
Design Arena: agents building full-stack apps1167Not measuredOnly GLM 5.1 measured
Design Arena: agents building mobile apps1161Not measuredOnly GLM 5.1 measured
Design Arena: agents building Android apps1182Not measuredOnly GLM 5.1 measured
Design Arena: agents building Godot games1053Not measuredOnly GLM 5.1 measured
Design Arena: agents building games1164Not measuredOnly GLM 5.1 measured
Design Arena: HTML slides1120Not measuredOnly GLM 5.1 measured
Design Arena: PowerPoint slides1165Not measuredOnly GLM 5.1 measured
Design Arena: spreadsheets1315Not measuredOnly GLM 5.1 measured
Finance Agent (Vals AI)44.8%Not measuredOnly GLM 5.1 measured
Legal Research Bench (Vals AI)27.9%Not measuredOnly GLM 5.1 measured
τ²-Bench Telecom97.7%28.1%GLM 5.1
APEX-Agents40.9%Not measuredOnly GLM 5.1 measured
Vending-Bench 2$5,634Not measuredOnly GLM 5.1 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured41.4%Only Qwen3 30B A3B Instruct 2507 measured
Neither model has a result on the other 25 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
GPQA Diamond84.5%Not measuredOnly GLM 5.1 measured
GPQA Diamond (Artificial Analysis run)86.8%70.7%GLM 5.1
Humanity's Last Exam (Artificial Analysis run)30.1%10.3%GLM 5.1
GPQA Diamond (Epoch AI run)89.9%55.6%GLM 5.1
SimpleBench55.1%Not measuredOnly GLM 5.1 measured
Chess puzzles20.0%Not measuredOnly GLM 5.1 measured
LMArena Text: hard prompts14731398GLM 5.1
LMArena Text: expert14781397GLM 5.1
LMArena Text: hard prompts in English14821406GLM 5.1
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
AIME91.9%Not measuredOnly GLM 5.1 measured
AA Math IndexNot measured66.3Only Qwen3 30B A3B Instruct 2507 measured
AIME 2025 (Artificial Analysis run)Not measured66.3%Only Qwen3 30B A3B Instruct 2507 measured
OTIS Mock AIME93.3%62.2%GLM 5.1
FrontierMath, tiers 1 to 336.8%Not measuredOnly GLM 5.1 measured
ProofBench22.2%Not measuredOnly GLM 5.1 measured
LMArena Text: math14731394GLM 5.1
LMArena Text: mathematical work14701411GLM 5.1
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
MMLU Pro86.9%Not measuredOnly GLM 5.1 measured
LegalBench (Vals AI)84.4%Not measuredOnly GLM 5.1 measured
CaseLaw (Vals AI)51.5%Not measuredOnly GLM 5.1 measured
CorpFin (Vals AI)64.5%Not measuredOnly GLM 5.1 measured
TaxEval (Vals AI)71.2%Not measuredOnly GLM 5.1 measured
MedCode (Vals AI)41.6%Not measuredOnly GLM 5.1 measured
MMLU Pro (Artificial Analysis run)Not measured80.5%Only Qwen3 30B A3B Instruct 2507 measured
SimpleQA Verified34.0%Not measuredOnly GLM 5.1 measured
LMArena Text: science14781393GLM 5.1
LMArena Text: medicine and healthcare14741392GLM 5.1
LMArena Text: legal and government14651387GLM 5.1
LMArena Text: business and finance14521389GLM 5.1
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
Design Arena: websites1290Not measuredOnly GLM 5.1 measured
Design Arena: UI components1292Not measuredOnly GLM 5.1 measured
Design Arena: game dev1264Not measuredOnly GLM 5.1 measured
Design Arena: data viz1366Not measuredOnly GLM 5.1 measured
Design Arena: 3D1336Not measuredOnly GLM 5.1 measured
Design Arena: all code categories1268Not measuredOnly GLM 5.1 measured
Design Arena: SVG1217Not measuredOnly GLM 5.1 measured
Design Arena: ASCII art1141Not measuredOnly GLM 5.1 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
Public Benefits Bench (Vals AI)61.8%Not measuredOnly GLM 5.1 measured
MedScribe (Vals AI)72.3%Not measuredOnly GLM 5.1 measured
IFBench76.3%50.7%GLM 5.1
Long context reasoning (AA-LCR)73.7%61.3%GLM 5.1
DTBenchNot measured67.2%Only Qwen3 30B A3B Instruct 2507 measured
LMCANot measured22.4%Only Qwen3 30B A3B Instruct 2507 measured
ExploitBench18.1%Not measuredOnly GLM 5.1 measured
LMArena Text14631384GLM 5.1
LMArena Text: instruction following14531363GLM 5.1
LMArena Text: creative writing14531317GLM 5.1
LMArena Text: English14761391GLM 5.1
LMArena Text: non-English14481372GLM 5.1
LMArena Text: Chinese15151432GLM 5.1
LMArena Text: German14671379GLM 5.1
LMArena Text: Japanese14351337GLM 5.1
LMArena Text: Korean14241330GLM 5.1
LMArena Text: French14701419GLM 5.1
LMArena Text: Spanish14681404GLM 5.1
LMArena Text: Russian14541369GLM 5.1
LMArena Text: Polish14491353GLM 5.1
LMArena Text: multi-turn14731378GLM 5.1
LMArena Text: longer queries14671378GLM 5.1
LMArena Text: writing and language14541336GLM 5.1
LMArena Text: entertainment and media14421334GLM 5.1
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGLM 5.1Qwen3 30B A3B Instruct 2507Leader
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 5.1$0.0036Fits in one request
Qwen3 30B A3B Instruct 2507$0.0002Fits in one request

Qwen3 30B A3B Instruct 2507 costs less

Repository review

50K input + 3K output tokens

GLM 5.1$0.083Fits in one request
Qwen3 30B A3B Instruct 2507$0.0054Fits in one request

Qwen3 30B A3B Instruct 2507 costs less

Agent loop

200K input + 10K output tokens

GLM 5.1$0.324Exceeds the context window
Qwen3 30B A3B Instruct 2507$0.021Fits in one request

Qwen3 30B A3B Instruct 2507 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 5.1 and Qwen3 30B A3B Instruct 2507
SpecificationZ.ai GLM 5.1Qwen3 30B A3B Instruct 2507
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedApril 7, 2026July 29, 2025
Context window205K262K
Input $/Mtok$1.40$0.09
Output $/Mtok$4.40$0.30
Coding index49.2Not benchmarked
Independent measurements behind it30
Cost per solved task$0.60Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.1 better than Qwen3 30B A3B Instruct 2507 for coding?

Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; Qwen3 30B A3B Instruct 2507 has none, so the two cannot be ranked on coding. Pick Qwen3 30B A3B Instruct 2507 for output tokens 14.7x cheaper ($0.30 vs $4.40 per million) and a larger context window (262K against 205K tokens).

Which is cheaper, GLM 5.1 or Qwen3 30B A3B Instruct 2507?

GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3 30B A3B Instruct 2507 costs $0.09 and $0.30. Cost per solved task is known for GLM 5.1 only.

Which has the bigger context window, GLM 5.1 or Qwen3 30B A3B Instruct 2507?

Qwen3 30B A3B Instruct 2507, with 262K tokens against 205K.

How many benchmarks is the GLM 5.1 vs Qwen3 30B A3B Instruct 2507 comparison based on?

38 benchmarks have a result for both models as of October 22, 2026. GLM 5.1's coding index rests on 3 independent benchmarks and Qwen3 30B A3B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.

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