Model comparison
Hy3 vs Llama-3.3-70B-Instruct
Hy3 is the stronger model overall, scoring 43.6 to 30.3 on the Noometry Index.
Last verified . 15 shared benchmarks.
Tencent
MetaSummary
- They share 15 benchmarks with published results for both. Hy3 scores higher in 8 categories and Llama-3.3-70B-Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Hy3 leads 37.4 to 14.3.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.13 / $0.53 for Hy3.
- Hy3 accepts more context: 262K tokens versus 128K.
Side by side
| Hy3 | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Tencent | Meta |
| Noometry Index | 43.6 | 30.3 |
| Released | 2026-07-06 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 262K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.13 | $0.10 |
| Output $ / M tokens | $0.53 | $0.32 |
| Results tracked | 17 | 43 |
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Category by category
Coding Hy3 leads
Hy3: 46.6 (#60), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Coding | 1465 | 1268 |
| LMArena WebDev | 1509 | — |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Not comparable
Hy3: —, Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
Reasoning Hy3 leads
Hy3: 25.0 (#134), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1449 | 1257 |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 41.2% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.32 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math Hy3 leads
Hy3: 37.4 (#98), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1475 | 1268 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge Hy3 leads
Hy3: 40.2 (#108), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Expert | 1461 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multilingual Hy3 leads
Hy3: 53.7 (#60), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1427 | 1236 |
| LMArena Chinese | 1496 | 1217 |
| LMArena French | 1461 | 1280 |
| LMArena Korean | 1390 | 1142 |
| LMArena Russian | 1435 | 1252 |
| LMArena Spanish | 1448 | 1269 |
| LMArena German | — | 1251 |
| LMArena Japanese | — | 1149 |
Instruction Following Hy3 leads
Hy3: 75.3 (#68), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1425 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context Hy3 leads
Hy3: 44.1 (#70), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1443 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Hy3 leads
Hy3: 62.5 (#75), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | Hy3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1442 | 1274 |
| LMArena Creative Writing | 1399 | 1250 |
| LMArena Multi-Turn | 1436 | 1279 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Hy3 better than Llama-3.3-70B-Instruct?
Hy3 is the stronger model overall, scoring 43.6 to 30.3 on the Noometry Index.
Which is cheaper, Hy3 or Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Hy3 lists at $0.13 and $0.53.
Is Hy3 or Llama-3.3-70B-Instruct better for coding?
Hy3 scores higher on coding benchmarks: 46.6 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Hy3 does, with 262K tokens against 128K.
How many benchmarks do Hy3 and Llama-3.3-70B-Instruct share?
15 benchmarks have published results for both models. Hy3 has 17 scored results on Noometry and Llama-3.3-70B-Instruct has 43.