Qwen3.6 27B vs Hy3 preview
Hy3 preview leads Qwen3.6 27B on the Artificial Analysis Intelligence Index, 25.3 to 21.4, and Hy3 preview costs 4.5x less per output token ($0.60 vs $2.70 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
Qwen3.6 27B or Hy3 preview: which to pick
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Hy3 preview has none, so the two cannot be ranked on coding.
Pick Hy3 preview for output tokens 4.5x cheaper ($0.60 vs $2.70 per million) and work measured by AA Coding Index or AA Intelligence Index, where it leads.
- Shared results
- 9
- Qwen3.6 27B only
- 15
- Hy3 preview only
- 27
- 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
Only Qwen3.6 27B 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 21.4.
Same board, both measured
Agentic work
AA Agentic Index
Hy3 preview
Hy3 preview leads, 24.1 against 18.5.
Same board, both measured
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Hy3 preview
Qwen3.6 27B $0.0018, Hy3 preview $0.0005 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 27B has the number, $1.30.
Directional only
At a glance
- Coding index
- Qwen3.6 27B 36.8 from 2 benchmarks, Hy3 preview not benchmarked.
- Widest category
- General, 8.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): Qwen3.6 27B 77.3% against Hy3 preview 64.7%, 12.7 points apart on a 0 to 100 scale.
- Output price
- Qwen3.6 27B $2.70 against Hy3 preview $0.60 per million output tokens, 4.5x apart.
- Context window
- Both take 262K tokens.
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 | Qwen3.6 27B | Bars | Hy3 preview | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 2 | 0 | ||
| All shared boards | 4 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | Qwen3.6 27B | Hy3 preview | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1357 | Only Hy3 preview measured |
| AA Coding Index | 53.7 | 58.8 | Hy3 preview |
| SWE-bench Verified (Vals AI) | 70.0% | Not measured | Only Qwen3.6 27B measured |
| Vibe Code Bench (Vals AI) | 11.9% | Not measured | Only Qwen3.6 27B measured |
| SciCode | 42.8% | Not measured | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | 34.1% | Qwen3.6 27B |
| LMArena Text: coding | Not measured | 1421 | Only Hy3 preview measured |
| LMArena Text: software and IT | Not measured | 1423 | Only Hy3 preview measured |
| LMArena WebDev: React | Not measured | 1349 | Only Hy3 preview measured |
| LMArena WebDev: HTML | Not measured | 1356 | Only Hy3 preview measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3.6 27B | Hy3 preview | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.4 | 25.3 | Hy3 preview |
| Epoch Capabilities Index | 146.2 | Not measured | Only Qwen3.6 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Qwen3.6 27B | Hy3 preview | Leader |
|---|---|---|---|
| AA Agentic Index | 18.5 | 24.1 | Hy3 preview |
| τ²-Bench Telecom | 94.2% | 92.7% | Qwen3.6 27B |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3.6 27B | Hy3 preview | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.2% | 86.7% | Hy3 preview |
| Humanity's Last Exam (Artificial Analysis run) | 23.1% | 27.8% | Hy3 preview |
| GPQA Diamond (Epoch AI run) | 85.9% | Not measured | Only Qwen3.6 27B measured |
| Mystery game puzzles | 7.0% | Not measured | Only Qwen3.6 27B measured |
| LMArena Text: hard prompts | Not measured | 1413 | Only Hy3 preview measured |
| LMArena Text: expert | Not measured | 1422 | Only Hy3 preview measured |
| LMArena Text: hard prompts in English | Not measured | 1416 | Only Hy3 preview measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Qwen3.6 27B | Hy3 preview | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | Not measured | Only Qwen3.6 27B measured |
| FrontierMath, tiers 1 to 3 | 35.1% | Not measured | Only Qwen3.6 27B measured |
| LMArena Text: math | Not measured | 1424 | Only Hy3 preview measured |
| LMArena Text: mathematical work | Not measured | 1442 | Only Hy3 preview measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Qwen3.6 27B | Hy3 preview | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 53.2% | Not measured | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | 62.3% | Not measured | Only Qwen3.6 27B measured |
| TaxEval (Vals AI) | 71.3% | Not measured | Only Qwen3.6 27B measured |
| MortgageTax (Vals AI) | 68.3% | Not measured | Only Qwen3.6 27B measured |
| LMArena Text: science | Not measured | 1416 | Only Hy3 preview measured |
| LMArena Text: medicine and healthcare | Not measured | 1405 | Only Hy3 preview measured |
| LMArena Text: legal and government | Not measured | 1430 | Only Hy3 preview measured |
| LMArena Text: business and finance | Not measured | 1397 | Only Hy3 preview measured |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.6 27B | Hy3 preview | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Qwen3.6 27B | Hy3 preview | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 45.6% | Not measured | Only Qwen3.6 27B measured |
| IFBench | 67.6% | 63.1% | Qwen3.6 27B |
| Long context reasoning (AA-LCR) | 77.3% | 64.7% | Qwen3.6 27B |
| DTBench | 78.1% | Not measured | Only Qwen3.6 27B measured |
| LMCA | 34.5% | Not measured | Only Qwen3.6 27B measured |
| LMArena Text | Not measured | 1401 | Only Hy3 preview measured |
| LMArena Text: instruction following | Not measured | 1383 | Only Hy3 preview measured |
| LMArena Text: creative writing | Not measured | 1339 | Only Hy3 preview measured |
| LMArena Text: English | Not measured | 1409 | Only Hy3 preview measured |
| LMArena Text: non-English | Not measured | 1390 | Only Hy3 preview measured |
| LMArena Text: Chinese | Not measured | 1435 | Only Hy3 preview measured |
| LMArena Text: French | Not measured | 1425 | Only Hy3 preview measured |
| LMArena Text: Spanish | Not measured | 1399 | Only Hy3 preview measured |
| LMArena Text: Russian | Not measured | 1399 | Only Hy3 preview measured |
| LMArena Text: multi-turn | Not measured | 1411 | Only Hy3 preview measured |
| LMArena Text: longer queries | Not measured | 1402 | Only Hy3 preview measured |
| LMArena Text: writing and language | Not measured | 1370 | Only Hy3 preview measured |
| LMArena Text: entertainment and media | Not measured | 1352 | Only Hy3 preview measured |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.6 27B | 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 | Qwen3.6 27B | Tencent Hy3 preview |
|---|---|---|
| Maker | Qwen | Tencent |
| Weights | Open weights | Open weights |
| Released | April 27, 2026 | April 22, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.45 | $0.18 |
| Output $/Mtok | $2.70 | $0.60 |
| Coding index | 36.8 | Not benchmarked |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $1.30 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.6 27B better than Hy3 preview for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Hy3 preview has none, so the two cannot be ranked on coding. Pick Hy3 preview for output tokens 4.5x cheaper ($0.60 vs $2.70 per million) and work measured by AA Coding Index or AA Intelligence Index, where it leads.
Which is cheaper, Qwen3.6 27B or Hy3 preview?
Qwen3.6 27B costs $0.45 per million input tokens and $2.70 per million output tokens; Hy3 preview costs $0.18 and $0.60. Cost per solved task is known for Qwen3.6 27B only.
Which has the bigger context window, Qwen3.6 27B or Hy3 preview?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3.6 27B vs Hy3 preview comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. Qwen3.6 27B'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.