GLM 4.5V vs Hy3
Hy3 costs 5.5x less per output token than GLM 4.5V ($0.33 vs $1.80 per million). Updated October 21, 2026; 27 benchmarks have a result for both.
GLM 4.5V or Hy3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5V for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 27
- GLM 4.5V only
- 14
- Hy3 only
- 17
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GLM 4.5V has a AA Intelligence Index result, 7.6.
Directional only
Web design
Design Arena: websites
No call
Only Hy3 has a Design Arena: websites result, 1230.
Directional only
Long documents
Listed context window
Hy3
Hy3 takes 262K tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Hy3
GLM 4.5V $0.0015, Hy3 $0.0002 per turn.
List prices
At a glance
- Coding index
- GLM 4.5V not benchmarked, Hy3 not benchmarked.
- Widest category
- General, 23.9 points apart on average over 14 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.5V 0.0% against Hy3 79.0%, 79.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5V $1.80 against Hy3 $0.33 per million output tokens, 5.5x apart.
- Context window
- GLM 4.5V takes 66K 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.
| Category | GLM 4.5V | Bars | Hy3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 14 | ||
| All shared boards | 0 | 27 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GLM 4.5V | Hy3 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1507 | Only Hy3 measured |
| LiveCodeBench (Artificial Analysis run) | 60.4% | Not measured | Only GLM 4.5V measured |
| SciCode | Not measured | 48.6% | Only Hy3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only GLM 4.5V measured |
| ALE-Bench | Not measured | 838.6 | Only Hy3 measured |
| LMArena Text: coding | 1347 | 1466 | Hy3 |
| LMArena Text: software and IT | 1353 | 1463 | Hy3 |
| LMArena WebDev: React | Not measured | 1511 | Only Hy3 measured |
| LMArena WebDev: HTML | Not measured | 1487 | Only Hy3 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.5V | Hy3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.5V | Hy3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 22.5% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GLM 4.5V | Hy3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 68.4% | 89.7% | Hy3 |
| Humanity's Last Exam (Artificial Analysis run) | 6.3% | 33.5% | Hy3 |
| LMArena Text: hard prompts | 1335 | 1447 | Hy3 |
| LMArena Text: expert | 1353 | 1461 | Hy3 |
| LMArena Text: hard prompts in English | 1357 | 1455 | Hy3 |
| LMArena Vision: diagrams | 1151 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: homework | 1163 | Not measured | Only GLM 4.5V measured |
| Kagi LLM Benchmark | 59.8% | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.5V | Hy3 | Leader |
|---|---|---|---|
| AA Math Index | 73.0 | Not measured | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | 73.0% | Not measured | Only GLM 4.5V measured |
| LMArena Text: math | 1354 | 1473 | Hy3 |
| LMArena Text: mathematical work | 1336 | 1482 | Hy3 |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.5V | Hy3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 78.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: science | 1330 | 1457 | Hy3 |
| LMArena Text: medicine and healthcare | 1349 | 1452 | Hy3 |
| LMArena Text: legal and government | 1338 | 1422 | Hy3 |
| LMArena Text: business and finance | 1309 | 1424 | Hy3 |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.5V | Hy3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1230 | Only Hy3 measured |
| Design Arena: UI components | Not measured | 1207 | Only Hy3 measured |
| Design Arena: game dev | Not measured | 1208 | Only Hy3 measured |
| Design Arena: data viz | Not measured | 1163 | Only Hy3 measured |
| Design Arena: 3D | Not measured | 1233 | Only Hy3 measured |
| Design Arena: all code categories | Not measured | 1227 | Only Hy3 measured |
| Design Arena: SVG | Not measured | 1200 | Only Hy3 measured |
| Design Arena: ASCII art | Not measured | 1152 | Only Hy3 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
| Benchmark | GLM 4.5V | Hy3 | Leader |
|---|---|---|---|
| IFBench | 34.2% | Not measured | Only GLM 4.5V measured |
| Long context reasoning (AA-LCR) | 0.0% | 79.0% | Hy3 |
| LMArena Text | 1333 | 1438 | Hy3 |
| LMArena Text: instruction following | 1311 | 1426 | Hy3 |
| LMArena Text: creative writing | 1294 | 1403 | Hy3 |
| LMArena Vision | 1154 | Not measured | Only GLM 4.5V measured |
| LMArena Text: English | 1362 | 1448 | Hy3 |
| LMArena Text: non-English | 1303 | 1426 | Hy3 |
| LMArena Text: Chinese | 1337 | 1485 | Hy3 |
| LMArena Text: German | Not measured | 1449 | Only Hy3 measured |
| LMArena Text: Japanese | Not measured | 1397 | Only Hy3 measured |
| LMArena Text: Korean | Not measured | 1396 | Only Hy3 measured |
| LMArena Text: French | Not measured | 1456 | Only Hy3 measured |
| LMArena Text: Spanish | 1336 | 1457 | Hy3 |
| LMArena Text: Russian | 1298 | 1432 | Hy3 |
| LMArena Text: Polish | 1302 | 1447 | Hy3 |
| LMArena Text: multi-turn | 1333 | 1436 | Hy3 |
| LMArena Text: longer queries | 1305 | 1442 | Hy3 |
| LMArena Text: writing and language | 1302 | 1414 | Hy3 |
| LMArena Text: entertainment and media | 1303 | 1400 | Hy3 |
| LMArena Vision: English | 1183 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: reading text | 1151 | Not measured | Only GLM 4.5V 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 4.5V | Hy3 | 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
Hy3 costs less
Repository review
50K input + 3K output tokens
Hy3 costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Z.ai GLM 4.5V | Tencent Hy3 |
|---|---|---|
| Maker | Z.ai | Tencent |
| Weights | Open weights | Open weights |
| Released | August 11, 2025 | July 6, 2026 |
| Context window | 66K | 262K |
| Input $/Mtok | $0.60 | $0.0825 |
| Output $/Mtok | $1.80 | $0.33 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5V better than Hy3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5V for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GLM 4.5V or Hy3?
GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Hy3 costs $0.0825 and $0.33. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5V or Hy3?
Hy3, with 262K tokens against 66K.
How many benchmarks is the GLM 4.5V vs Hy3 comparison based on?
27 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Hy3's on 1. Where only one model has a result, the page says so instead of naming a winner.