GLM 4.7 vs Hy3 preview
Hy3 preview leads GLM 4.7 on the Artificial Analysis Intelligence Index, 25.3 to 22.2, and Hy3 preview costs 3.7x less per output token ($0.60 vs $2.20 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
GLM 4.7 or Hy3 preview: which to pick
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Hy3 preview has none, so the two cannot be ranked on coding.
Pick Hy3 preview for output tokens 3.7x cheaper ($0.60 vs $2.20 per million) and a larger context window (262K against 205K tokens).
- Shared results
- 35
- GLM 4.7 only
- 46
- Hy3 preview only
- 1
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GLM 4.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Hy3 preview
Hy3 preview leads, 25.3 against 22.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Hy3 preview has a AA Agentic Index result, 24.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 4.7 has a Design Arena: websites result, 1225.
Directional only
Long documents
Listed context window
Hy3 preview
Hy3 preview takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Hy3 preview
GLM 4.7 $0.0017, Hy3 preview $0.0005 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 55.1 from 2 benchmarks, Hy3 preview not benchmarked.
- Widest category
- Coding, 9.2 points apart on average over 7 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: GLM 4.7 1447 against Hy3 preview 1356, 15.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $2.20 against Hy3 preview $0.60 per million output tokens, 3.7x apart.
- Context window
- GLM 4.7 takes 205K tokens, Hy3 preview 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 4.7 | Bars | Hy3 preview | Ties |
|---|---|---|---|---|
| Coding | 5 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 3 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 4 | 0 | ||
| General | 15 | 0 | ||
| All shared boards | 28 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index52
| Benchmark | GLM 4.7 | Hy3 preview | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | 1357 | GLM 4.7 |
| AA Coding Index | 45.3 | 58.8 | Hy3 preview |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 34.1% | Hy3 preview |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | Not measured | Only GLM 4.7 measured |
| LMArena Text: coding | 1454 | 1421 | GLM 4.7 |
| LMArena Text: software and IT | 1453 | 1423 | GLM 4.7 |
| LMArena WebDev: React | 1420 | 1349 | GLM 4.7 |
| LMArena WebDev: HTML | 1447 | 1356 | GLM 4.7 |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 | Hy3 preview | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 25.3 | Hy3 preview |
| Epoch Capabilities Index | 143.6 | Not measured | Only GLM 4.7 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | Hy3 preview | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 24.1 | Only Hy3 preview measured |
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1112 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| τ²-Bench Telecom | 95.9% | 92.7% | GLM 4.7 |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| BrowseComp-Plus | 64.7% | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GLM 4.7 | Hy3 preview | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 86.7% | Hy3 preview |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 27.8% | Hy3 preview |
| GPQA Diamond (Epoch AI run) | 83.3% | Not measured | Only GLM 4.7 measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | 1413 | GLM 4.7 |
| LMArena Text: expert | 1424 | 1422 | GLM 4.7 |
| LMArena Text: hard prompts in English | 1452 | 1416 | GLM 4.7 |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 | Hy3 preview | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | Not measured | Only GLM 4.7 measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | 1424 | Hy3 preview |
| LMArena Text: mathematical work | 1411 | 1442 | Hy3 preview |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.7 | Hy3 preview | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| LMArena Text: science | 1457 | 1416 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1405 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1430 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1397 | GLM 4.7 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.7 | Hy3 preview | Leader |
|---|---|---|---|
| Design Arena: websites | 1225 | Not measured | Only GLM 4.7 measured |
| Design Arena: UI components | 1196 | Not measured | Only GLM 4.7 measured |
| Design Arena: game dev | 1188 | Not measured | Only GLM 4.7 measured |
| Design Arena: data viz | 1195 | Not measured | Only GLM 4.7 measured |
| Design Arena: 3D | 1190 | Not measured | Only GLM 4.7 measured |
| Design Arena: all code categories | 1216 | Not measured | Only GLM 4.7 measured |
| Design Arena: SVG | 1142 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index150
| Benchmark | GLM 4.7 | Hy3 preview | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | 63.1% | GLM 4.7 |
| Long context reasoning (AA-LCR) | 71.0% | 64.7% | GLM 4.7 |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | 1401 | GLM 4.7 |
| LMArena Text: instruction following | 1412 | 1383 | GLM 4.7 |
| LMArena Text: creative writing | 1401 | 1339 | GLM 4.7 |
| LMArena Text: English | 1453 | 1409 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1390 | GLM 4.7 |
| LMArena Text: Chinese | 1495 | 1435 | GLM 4.7 |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1432 | 1425 | GLM 4.7 |
| LMArena Text: Spanish | 1434 | 1399 | GLM 4.7 |
| LMArena Text: Russian | 1423 | 1399 | GLM 4.7 |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | 1411 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1402 | GLM 4.7 |
| LMArena Text: writing and language | 1411 | 1370 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1352 | GLM 4.7 |
| 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 4.7 | Hy3 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
Hy3 preview costs less
Repository review
50K input + 3K output tokens
Hy3 preview costs less
Agent loop
200K input + 10K output tokens
Hy3 preview 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 4.7 | Tencent Hy3 preview |
|---|---|---|
| Maker | Z.ai | Tencent |
| Weights | Open weights | Open weights |
| Released | December 22, 2025 | April 22, 2026 |
| Context window | 205K | 262K |
| Input $/Mtok | $0.60 | $0.18 |
| Output $/Mtok | $2.20 | $0.60 |
| Coding index | 55.1 | Not benchmarked |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Hy3 preview for coding?
Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; Hy3 preview has none, so the two cannot be ranked on coding. Pick Hy3 preview for output tokens 3.7x cheaper ($0.60 vs $2.20 per million) and a larger context window (262K against 205K tokens).
Which is cheaper, GLM 4.7 or Hy3 preview?
GLM 4.7 costs $0.60 per million input tokens and $2.20 per million output tokens; Hy3 preview costs $0.18 and $0.60. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or Hy3 preview?
Hy3 preview, with 262K tokens against 205K.
How many benchmarks is the GLM 4.7 vs Hy3 preview comparison based on?
35 benchmarks have a result for both models as of October 22, 2026. GLM 4.7's coding index rests on 2 independent benchmarks and Hy3 preview's on 1. Where only one model has a result, the page says so instead of naming a winner.