Saba vs Hy3
Hy3 costs 1.8x less per output token than Saba ($0.33 vs $0.60 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
Saba or Hy3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Saba for work measured by GPQA Diamond (Artificial Analysis run) or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 2
- Saba only
- 2
- Hy3 only
- 42
- Like-for-like categories
- 1 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 Saba has a AA Intelligence Index result, 6.5.
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 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Hy3
Saba $0.0005, Hy3 $0.0002 per turn.
List prices
At a glance
- Coding index
- Saba not benchmarked, Hy3 not benchmarked.
- Widest category
- Reasoning, 38.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Saba 42.4% against Hy3 89.7%, 47.3 points apart on a 0 to 100 scale.
- Output price
- Saba $0.60 against Hy3 $0.33 per million output tokens, 1.8x apart.
- Context window
- Saba takes 33K 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 | Saba | Bars | Hy3 | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 2 | ||
| All shared boards | 0 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | Saba | Hy3 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1507 | Only Hy3 measured |
| SciCode | Not measured | 48.6% | Only Hy3 measured |
| ALE-Bench | Not measured | 838.6 | Only Hy3 measured |
| LMArena Text: coding | Not measured | 1466 | Only Hy3 measured |
| LMArena Text: software and IT | Not measured | 1463 | Only Hy3 measured |
| 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 32 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Saba | Hy3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.5 | Not measured | Only Saba measured |
| Neither model has a result on the other 1 board here | |||
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Saba | Hy3 | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Saba | Hy3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 42.4% | 89.7% | Hy3 |
| Humanity's Last Exam (Artificial Analysis run) | 4.3% | 33.5% | Hy3 |
| LMArena Text: hard prompts | Not measured | 1447 | Only Hy3 measured |
| LMArena Text: expert | Not measured | 1461 | Only Hy3 measured |
| LMArena Text: hard prompts in English | Not measured | 1455 | Only Hy3 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Saba | Hy3 | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1473 | Only Hy3 measured |
| LMArena Text: mathematical work | Not measured | 1482 | Only Hy3 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Saba | Hy3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 61.1% | Not measured | Only Saba measured |
| LMArena Text: science | Not measured | 1457 | Only Hy3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1452 | Only Hy3 measured |
| LMArena Text: legal and government | Not measured | 1422 | Only Hy3 measured |
| LMArena Text: business and finance | Not measured | 1424 | Only Hy3 measured |
| 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 | Saba | 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | Saba | Hy3 | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | Not measured | 79.0% | Only Hy3 measured |
| LMArena Text | Not measured | 1438 | Only Hy3 measured |
| LMArena Text: instruction following | Not measured | 1426 | Only Hy3 measured |
| LMArena Text: creative writing | Not measured | 1403 | Only Hy3 measured |
| LMArena Text: English | Not measured | 1448 | Only Hy3 measured |
| LMArena Text: non-English | Not measured | 1426 | Only Hy3 measured |
| LMArena Text: Chinese | Not measured | 1485 | Only Hy3 measured |
| 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 | Not measured | 1457 | Only Hy3 measured |
| LMArena Text: Russian | Not measured | 1432 | Only Hy3 measured |
| LMArena Text: Polish | Not measured | 1447 | Only Hy3 measured |
| LMArena Text: multi-turn | Not measured | 1436 | Only Hy3 measured |
| LMArena Text: longer queries | Not measured | 1442 | Only Hy3 measured |
| LMArena Text: writing and language | Not measured | 1414 | Only Hy3 measured |
| LMArena Text: entertainment and media | Not measured | 1400 | Only Hy3 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 | Saba | 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 | Mistral Saba | Tencent Hy3 |
|---|---|---|
| Maker | Mistral AI | Tencent |
| Weights | No public weights listed | Open weights |
| Released | February 17, 2025 | July 6, 2026 |
| Context window | 33K | 262K |
| Input $/Mtok | $0.20 | $0.0825 |
| Output $/Mtok | $0.60 | $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 Saba better than Hy3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Saba for work measured by GPQA Diamond (Artificial Analysis run) or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Saba or Hy3?
Saba costs $0.20 per million input tokens and $0.60 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, Saba or Hy3?
Hy3, with 262K tokens against 33K.
How many benchmarks is the Saba vs Hy3 comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. Saba'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.