GLM 4.6 vs Hy4 preview
Hy4 preview leads GLM 4.6 on coding, 44.3 to 8.8 on the Vibe Coding Index, at about the same output price ($2.2509 vs $2.20 per million). Updated October 21, 2026; 4 benchmarks have a result for both.
GLM 4.6 or Hy4 preview: which to pick
Pick Hy4 preview for coding: it scores 44.3 on the Vibe Coding Index against 8.8 for GLM 4.6.
Pick GLM 4.6 for output tokens 1.0x cheaper ($2.20 vs $2.2509 per million).
- Shared results
- 4
- GLM 4.6 only
- 65
- Hy4 preview only
- 20
- Like-for-like categories
- 2 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.
Coding
Vibe Coding Index, six public coding benchmarks
Hy4 preview
Hy4 preview scores 44.3 against 8.8.
2 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GLM 4.6 has a AA Intelligence Index result, 18.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.6 has a Design Arena: websites result, 1174.
Directional only
General
Vals Index
No call
Only Hy4 preview has a Vals Index result, 53.2%.
Directional only
Long documents
Listed context window
Hy4 preview
Hy4 preview takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.6
GLM 4.6 $0.0017, Hy4 preview $0.0019 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 8.8 from 2 benchmarks, Hy4 preview 44.3 from 3 benchmarks.
- Widest category
- Coding, 52.5 points apart on average over 3 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 4.6 3.1% against Hy4 preview 77.5%, 74.4 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against Hy4 preview $2.2509 per million output tokens, 1.0x apart.
- Context window
- GLM 4.6 takes 205K tokens, Hy4 preview 1.05M.
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.
| Category | GLM 4.6 | Bars | Hy4 preview | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Knowledge | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | 1631 | Hy4 preview |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 81.0% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 8.1% | Only Hy4 preview measured |
| Vibe Code Bench (Vals AI) | 3.1% | 77.5% | Hy4 preview |
| Code migration (Vals AI) | Not measured | 47.4% | Only Hy4 preview measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Hy4 preview measured |
| IOI (Vals AI) | Not measured | 59.3% | Only Hy4 preview measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 55.1% | Only Hy4 preview measured |
| CyberBench (Vals AI) | Not measured | 65.4% | Only Hy4 preview measured |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| ALE-Bench | 340.8 | Not measured | Only GLM 4.6 measured |
| LMArena Text: coding | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: software and IT | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena WebDev: React | Not measured | 1653 | Only Hy4 preview measured |
| LMArena WebDev: HTML | 1350 | 1620 | Hy4 preview |
| Neither model has a result on the other 20 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 measured |
| SREBench (Vals AI) | Not measured | 2.3% | Only Hy4 preview measured |
| Finance Agent (Vals AI) | Not measured | 55.1% | Only Hy4 preview measured |
| Tax Agent Bench (Vals AI) | Not measured | 32.6% | Only Hy4 preview measured |
| Legal Research Bench (Vals AI) | Not measured | 45.2% | Only Hy4 preview measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 8.8% | Only Hy4 preview measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 58.1% | Only Hy4 preview measured |
| BioMysteryBench (Vals AI) | Not measured | 69.3% | Only Hy4 preview measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 1.4% | Only Hy4 preview measured |
| τ²-Bench Telecom | 76.9% | Not measured | Only GLM 4.6 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 72.4% | Not measured | Only GLM 4.6 measured |
| BrowseComp-Plus | 48.2% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | Not measured | Only GLM 4.6 measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | Not measured | Only GLM 4.6 measured |
| LMArena Text: hard prompts | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: expert | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: hard prompts in English | 1445 | Not measured | Only GLM 4.6 measured |
| Kagi LLM Benchmark | 45.7% | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| AIME | 92.7% | Not measured | Only GLM 4.6 measured |
| MGSM (Vals AI) | 89.8% | Not measured | Only GLM 4.6 measured |
| AA Math Index | 86.0 | Not measured | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | 86.0% | Not measured | Only GLM 4.6 measured |
| ProofBench | Not measured | 75.0% | Only Hy4 preview measured |
| LMArena Text: math | 1432 | Not measured | Only GLM 4.6 measured |
| LMArena Text: mathematical work | 1447 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | Not measured | Only GLM 4.6 measured |
| LegalBench (Vals AI) | 79.6% | 83.8% | Hy4 preview |
| CorpFin (Vals AI) | 56.8% | Not measured | Only GLM 4.6 measured |
| TaxEval (Vals AI) | 66.2% | Not measured | Only GLM 4.6 measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MedCode (Vals AI) | Not measured | 43.3% | Only Hy4 preview measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | Not measured | Only GLM 4.6 measured |
| LMArena Text: science | 1456 | Not measured | Only GLM 4.6 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 4.6 measured |
| LMArena Text: legal and government | 1448 | Not measured | Only GLM 4.6 measured |
| LMArena Text: business and finance | 1416 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | Not measured | Only GLM 4.6 measured |
| Design Arena: UI components | 1148 | Not measured | Only GLM 4.6 measured |
| Design Arena: game dev | 1151 | Not measured | Only GLM 4.6 measured |
| Design Arena: data viz | 1161 | Not measured | Only GLM 4.6 measured |
| Design Arena: 3D | 1129 | Not measured | Only GLM 4.6 measured |
| Design Arena: all code categories | 1166 | Not measured | Only GLM 4.6 measured |
| Design Arena: SVG | 1109 | Not measured | Only GLM 4.6 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index22 boards, none shared
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| Vals Index | Not measured | 53.2% | Only Hy4 preview measured |
| Public Benefits Bench (Vals AI) | Not measured | 68.6% | Only Hy4 preview measured |
| MedScribe (Vals AI) | Not measured | 83.6% | Only Hy4 preview measured |
| IFBench | 43.4% | Not measured | Only GLM 4.6 measured |
| Long context reasoning (AA-LCR) | 54.0% | Not measured | Only GLM 4.6 measured |
| LMArena Text | 1440 | Not measured | Only GLM 4.6 measured |
| LMArena Text: instruction following | 1410 | Not measured | Only GLM 4.6 measured |
| LMArena Text: creative writing | 1412 | Not measured | Only GLM 4.6 measured |
| LMArena Text: English | 1449 | Not measured | Only GLM 4.6 measured |
| LMArena Text: non-English | 1426 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Chinese | 1499 | Not measured | Only GLM 4.6 measured |
| LMArena Text: German | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Japanese | 1393 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Korean | 1400 | Not measured | Only GLM 4.6 measured |
| LMArena Text: French | 1459 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Russian | 1418 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | Not measured | Only GLM 4.6 measured |
| LMArena Text: longer queries | 1422 | Not measured | Only GLM 4.6 measured |
| LMArena Text: writing and language | 1415 | Not measured | Only GLM 4.6 measured |
| LMArena Text: entertainment and media | 1409 | Not measured | Only GLM 4.6 measured |
| 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
| Benchmark | GLM 4.6 | Hy4 preview | Leader |
|---|---|---|---|
| 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 4.6 costs less
Repository review
50K input + 3K output tokens
GLM 4.6 costs less
Agent loop
200K input + 10K output tokens
GLM 4.6 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.
| Specification | Z.ai GLM 4.6 | Tencent Hy4 preview |
|---|---|---|
| Maker | Z.ai | Tencent |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | August 28, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.60 | $0.7506 |
| Output $/Mtok | $2.20 | $2.2509 |
| Coding index | 8.8 | 44.3 |
| Independent measurements behind it | 2 | 3 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than Hy4 preview for coding?
Pick Hy4 preview for coding: it scores 44.3 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for output tokens 1.0x cheaper ($2.20 vs $2.2509 per million).
Which is cheaper, GLM 4.6 or Hy4 preview?
GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; Hy4 preview costs $0.7506 and $2.2509. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.6 or Hy4 preview?
Hy4 preview, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.6 vs Hy4 preview comparison based on?
4 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and Hy4 preview's on 3. Where only one model has a result, the page says so instead of naming a winner.