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GLM 5.1 vs Hy3

Hy3 costs 13.3x less per output token than GLM 5.1 ($0.33 vs $4.40 per million). Updated October 21, 2026; 44 benchmarks have a result for both.

Replace GLM 5.1 Replace Hy3
GLM 5.1Z.ai
Coding
49.2
Overall
57.5
Output $/M
$4.40
36
Hy3Tencent
Coding
No data
Overall
No data
Output $/M
$0.33
8
vs boards won of 44 shared

GLM 5.1 or Hy3: which to pick

Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; Hy3 has none, so the two cannot be ranked on coding.

Pick Hy3 for output tokens 13.3x cheaper ($0.33 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 BHy3TencentNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
44
GLM 5.1 only
45
Hy3 only
0
Like-for-like categories
6 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, Hy3 not benchmarked.
Widest category
Web design, 9.0 points apart on average over 8 shared benchmarks.
Widest single gap
Design Arena: data viz: GLM 5.1 1366 against Hy3 1163, 25.4 points apart on a 0 to 100 scale.
Output price
GLM 5.1 $4.40 against Hy3 $0.33 per million output tokens, 13.3x apart.
Context window
GLM 5.1 takes 205K tokens, Hy3 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 5.1BarsHy3Ties
Coding52
Reasoning32
Math02
Knowledge40
Web design71
General171
All shared boards368
CodingLike-for-likeSix of these boards feed the Vibe Coding Index52
Coding benchmarks for GLM 5.1 and Hy3
BenchmarkGLM 5.1Hy3Leader
LMArena WebDev15081507GLM 5.1
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
SciCode44.8%48.6%Hy3
Terminal-Bench Hard (Artificial Analysis run)43.2%Not measuredOnly GLM 5.1 measured
WeirdML57.1%Not measuredOnly GLM 5.1 measured
ALE-Bench887.1838.6GLM 5.1
LMArena Text: coding14851466GLM 5.1
LMArena Text: software and IT14841463GLM 5.1
LMArena WebDev: React15051511Hy3
LMArena WebDev: HTML15081487GLM 5.1
Neither model has a result on the other 23 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for GLM 5.1 and Hy3
BenchmarkGLM 5.1Hy3Leader
AA Intelligence Index26.1Not measuredOnly GLM 5.1 measured
Epoch Capabilities Index149.8Not measuredOnly GLM 5.1 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
Agentic work benchmarks for GLM 5.1 and Hy3
BenchmarkGLM 5.1Hy3Leader
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 apps1162Not measuredOnly GLM 5.1 measured
Design Arena: agents building Android apps1180Not 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 slides1170Not measuredOnly GLM 5.1 measured
Design Arena: PowerPoint slides1188Not 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%Not measuredOnly GLM 5.1 measured
APEX-Agents40.9%Not measuredOnly GLM 5.1 measured
Vending-Bench 2$5,634Not measuredOnly GLM 5.1 measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
Reasoning benchmarks for GLM 5.1 and Hy3
BenchmarkGLM 5.1Hy3Leader
GPQA Diamond84.5%Not measuredOnly GLM 5.1 measured
GPQA Diamond (Artificial Analysis run)86.8%89.7%Hy3
Humanity's Last Exam (Artificial Analysis run)30.1%33.5%Hy3
GPQA Diamond (Epoch AI run)89.9%Not measuredOnly GLM 5.1 measured
SimpleBench55.1%Not measuredOnly GLM 5.1 measured
Chess puzzles20.0%Not measuredOnly GLM 5.1 measured
LMArena Text: hard prompts14731447GLM 5.1
LMArena Text: expert14781461GLM 5.1
LMArena Text: hard prompts in English14821455GLM 5.1
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 5.1 and Hy3
BenchmarkGLM 5.1Hy3Leader
AIME91.9%Not measuredOnly GLM 5.1 measured
OTIS Mock AIME93.3%Not measuredOnly GLM 5.1 measured
FrontierMath, tiers 1 to 336.8%Not measuredOnly GLM 5.1 measured
ProofBench22.2%Not measuredOnly GLM 5.1 measured
LMArena Text: math14731473Hy3
LMArena Text: mathematical work14701482Hy3
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for GLM 5.1 and Hy3
BenchmarkGLM 5.1Hy3Leader
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
SimpleQA Verified34.0%Not measuredOnly GLM 5.1 measured
LMArena Text: science14781457GLM 5.1
LMArena Text: medicine and healthcare14741452GLM 5.1
LMArena Text: legal and government14651422GLM 5.1
LMArena Text: business and finance14521424GLM 5.1
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index71
Web design benchmarks for GLM 5.1 and Hy3
BenchmarkGLM 5.1Hy3Leader
Design Arena: websites12901230GLM 5.1
Design Arena: UI components12921207GLM 5.1
Design Arena: game dev12651208GLM 5.1
Design Arena: data viz13661163GLM 5.1
Design Arena: 3D13361233GLM 5.1
Design Arena: all code categories12681227GLM 5.1
Design Arena: SVG12181200GLM 5.1
Design Arena: ASCII art11421152Hy3
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index171
General benchmarks for GLM 5.1 and Hy3
BenchmarkGLM 5.1Hy3Leader
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%Not measuredOnly GLM 5.1 measured
Long context reasoning (AA-LCR)73.7%79.0%Hy3
ExploitBench18.1%Not measuredOnly GLM 5.1 measured
LMArena Text14631438GLM 5.1
LMArena Text: instruction following14531426GLM 5.1
LMArena Text: creative writing14531403GLM 5.1
LMArena Text: English14761448GLM 5.1
LMArena Text: non-English14481426GLM 5.1
LMArena Text: Chinese15151485GLM 5.1
LMArena Text: German14671449GLM 5.1
LMArena Text: Japanese14351397GLM 5.1
LMArena Text: Korean14241396GLM 5.1
LMArena Text: French14701456GLM 5.1
LMArena Text: Spanish14681457GLM 5.1
LMArena Text: Russian14541432GLM 5.1
LMArena Text: Polish14491447GLM 5.1
LMArena Text: multi-turn14731436GLM 5.1
LMArena Text: longer queries14671442GLM 5.1
LMArena Text: writing and language14541414GLM 5.1
LMArena Text: entertainment and media14421400GLM 5.1
Neither model has a result on the other 24 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 Hy3
BenchmarkGLM 5.1Hy3Leader
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
Hy3$0.0002Fits in one request

Hy3 costs less

Repository review

50K input + 3K output tokens

GLM 5.1$0.083Fits in one request
Hy3$0.0051Fits in one request

Hy3 costs less

Agent loop

200K input + 10K output tokens

GLM 5.1$0.324Exceeds the context window
Hy3$0.020Fits in one request

Hy3 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 Hy3
SpecificationZ.ai GLM 5.1Tencent Hy3
MakerZ.aiTencent
WeightsOpen weightsOpen weights
ReleasedApril 7, 2026July 6, 2026
Context window205K262K
Input $/Mtok$1.40$0.0825
Output $/Mtok$4.40$0.33
Coding index49.2Not benchmarked
Independent measurements behind it31
Cost per solved task$0.60Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5.1 better than Hy3 for coding?

Only GLM 5.1 has a coding score, 49.2 on the Vibe Coding Index; Hy3 has none, so the two cannot be ranked on coding. Pick Hy3 for output tokens 13.3x cheaper ($0.33 vs $4.40 per million) and a larger context window (262K against 205K tokens).

Which is cheaper, GLM 5.1 or Hy3?

GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; Hy3 costs $0.0825 and $0.33. Cost per solved task is known for GLM 5.1 only.

Which has the bigger context window, GLM 5.1 or Hy3?

Hy3, with 262K tokens against 205K.

How many benchmarks is the GLM 5.1 vs Hy3 comparison based on?

44 benchmarks have a result for both models as of October 21, 2026. GLM 5.1's coding index rests on 3 independent benchmarks and Hy3's on 1. Where only one model has a result, the page says so instead of naming a winner.

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