Hy3 vs ReMM SLERP 13B
Hy3 costs 1.2x less per output token than ReMM SLERP 13B ($0.528 vs $0.65 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.528
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.65
Hy3 or ReMM SLERP 13B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Hy3 for output tokens 1.2x cheaper ($0.528 vs $0.65 per million) and a larger context window (262K against 6K tokens).
No benchmark has tested both yet. Hy3 has results on LMArena WebDev, SciCode and ALE-Bench; ReMM SLERP 13B has none yet. Compare them on price and context instead.
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
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 6K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Hy3
Hy3 $0.0004, ReMM SLERP 13B $0.0007 per turn.
List prices
At a glance
- Coding index
- Hy3 not benchmarked, ReMM SLERP 13B not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Hy3 $0.528 against ReMM SLERP 13B $0.65 per million output tokens, 1.2x apart.
- Context window
- Hy3 takes 262K tokens, ReMM SLERP 13B 6K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards measured
| Benchmark | Hy3 | ReMM SLERP 13B | Leader |
|---|---|---|---|
| LMArena WebDev | 1507 | Not measured | Only Hy3 measured |
| SciCode | 48.6% | Not measured | Only Hy3 measured |
| ALE-Bench | 838.6 | Not measured | Only Hy3 measured |
| LMArena Text: coding | 1466 | Not measured | Only Hy3 measured |
| LMArena Text: software and IT | 1463 | Not measured | Only Hy3 measured |
| LMArena WebDev: React | 1511 | Not measured | Only Hy3 measured |
| LMArena WebDev: HTML | 1487 | Not measured | Only Hy3 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Hy3 | ReMM SLERP 13B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.7% | Not measured | Only Hy3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 33.5% | Not measured | Only Hy3 measured |
| LMArena Text: hard prompts | 1447 | Not measured | Only Hy3 measured |
| LMArena Text: expert | 1461 | Not measured | Only Hy3 measured |
| LMArena Text: hard prompts in English | 1455 | Not measured | Only Hy3 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Hy3 | ReMM SLERP 13B | Leader |
|---|---|---|---|
| LMArena Text: math | 1473 | Not measured | Only Hy3 measured |
| LMArena Text: mathematical work | 1482 | Not measured | Only Hy3 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Hy3 | ReMM SLERP 13B | Leader |
|---|---|---|---|
| LMArena Text: science | 1457 | Not measured | Only Hy3 measured |
| LMArena Text: medicine and healthcare | 1452 | Not measured | Only Hy3 measured |
| LMArena Text: legal and government | 1422 | Not measured | Only Hy3 measured |
| LMArena Text: business and finance | 1424 | Not measured | Only Hy3 measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | Hy3 | ReMM SLERP 13B | Leader |
|---|---|---|---|
| Design Arena: websites | 1230 | Not measured | Only Hy3 measured |
| Design Arena: UI components | 1204 | Not measured | Only Hy3 measured |
| Design Arena: game dev | 1208 | Not measured | Only Hy3 measured |
| Design Arena: data viz | 1165 | Not measured | Only Hy3 measured |
| Design Arena: 3D | 1232 | Not measured | Only Hy3 measured |
| Design Arena: all code categories | 1227 | Not measured | Only Hy3 measured |
| Design Arena: SVG | 1201 | Not measured | Only Hy3 measured |
| Design Arena: ASCII art | 1152 | Not measured | 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 measured
| Benchmark | Hy3 | ReMM SLERP 13B | Leader |
|---|---|---|---|
| Long context reasoning (AA-LCR) | 79.0% | Not measured | Only Hy3 measured |
| LMArena Text | 1438 | Not measured | Only Hy3 measured |
| LMArena Text: instruction following | 1426 | Not measured | Only Hy3 measured |
| LMArena Text: creative writing | 1403 | Not measured | Only Hy3 measured |
| LMArena Text: English | 1448 | Not measured | Only Hy3 measured |
| LMArena Text: non-English | 1426 | Not measured | Only Hy3 measured |
| LMArena Text: Chinese | 1485 | Not measured | Only Hy3 measured |
| LMArena Text: German | 1449 | Not measured | Only Hy3 measured |
| LMArena Text: Japanese | 1397 | Not measured | Only Hy3 measured |
| LMArena Text: Korean | 1396 | Not measured | Only Hy3 measured |
| LMArena Text: French | 1456 | Not measured | Only Hy3 measured |
| LMArena Text: Spanish | 1457 | Not measured | Only Hy3 measured |
| LMArena Text: Russian | 1432 | Not measured | Only Hy3 measured |
| LMArena Text: Polish | 1447 | Not measured | Only Hy3 measured |
| LMArena Text: multi-turn | 1436 | Not measured | Only Hy3 measured |
| LMArena Text: longer queries | 1442 | Not measured | Only Hy3 measured |
| LMArena Text: writing and language | 1414 | Not measured | Only Hy3 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only Hy3 measured |
| Neither model has a result on the other 30 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 | Tencent Hy3 | ReMM SLERP 13B |
|---|---|---|
| Maker | Tencent | Undi95 |
| Weights | Open weights | Open weights |
| Released | July 6, 2026 | July 22, 2023 |
| Context window | 262K | 6K |
| Input $/Mtok | $0.132 | $0.35 |
| Output $/Mtok | $0.528 | $0.65 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Hy3 better than ReMM SLERP 13B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Hy3 for output tokens 1.2x cheaper ($0.528 vs $0.65 per million) and a larger context window (262K against 6K tokens).
Which is cheaper, Hy3 or ReMM SLERP 13B?
Hy3 costs $0.132 per million input tokens and $0.528 per million output tokens; ReMM SLERP 13B costs $0.35 and $0.65. Cost per solved task is known for neither of them.
Which has the bigger context window, Hy3 or ReMM SLERP 13B?
Hy3, with 262K tokens against 6K.
Has any benchmark tested both Hy3 and ReMM SLERP 13B?
No benchmark has tested both yet. Hy3 has results on LMArena WebDev, SciCode and ALE-Bench; ReMM SLERP 13B has none yet. Compare them on price and context instead. Data as of October 22, 2026.