# Llama-3.3-70B-Instruct vs Solar Pro4

> Solar Pro4 is the stronger model overall, scoring 41.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.4× less per token, which makes it the better buy when Solar Pro4's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/llama-3-3-70b-instruct-vs-solar-pro4
- Last updated: 2026-10-10
- Shared benchmarks: 17

## Summary

- They share 17 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and Solar Pro4 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Solar Pro4 leads 36.1 to 14.3.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.30 / $1.20 for Solar Pro4.
- Solar Pro4 accepts more context: 524K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| Provider | Meta | Upstage |
| Noometry Index | 30.3 | 41.5 |
| Rank | 283 | 123 |
| Context | 128K | 524K |
| Input $/M | $0.10 | $0.30 |
| Output $/M | $0.32 | $1.20 |
| Weights | Open | Proprietary |

## Coding

- Llama-3.3-70B-Instruct: 30.6 (#284)
- Solar Pro4: 39.9 (#135)

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Coding | 1268 | 1439 |
| LMArena WebDev | — | 1371 |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |

## Agentic & Tool Use

- Llama-3.3-70B-Instruct: 25.6 (#100)
- Solar Pro4: —

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |

## Reasoning

- Llama-3.3-70B-Instruct: 13.7 (#325)
- Solar Pro4: 27.3 (#107)

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1398 |
| SimpleBench | 19.9% | — |
| 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

- Llama-3.3-70B-Instruct: 14.3 (#292)
- Solar Pro4: 36.1 (#129)

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Math | 1268 | 1418 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |

## Knowledge

- Llama-3.3-70B-Instruct: 30.5 (#220)
- Solar Pro4: 39.0 (#128)

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Expert | 1225 | 1421 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |

## Multilingual

- Llama-3.3-70B-Instruct: 40.1 (#215)
- Solar Pro4: 49.0 (#134)

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Non-English | 1236 | 1362 |
| LMArena Chinese | 1217 | 1417 |
| LMArena French | 1280 | 1403 |
| LMArena German | 1251 | 1351 |
| LMArena Japanese | 1149 | 1300 |
| LMArena Korean | 1142 | 1383 |
| LMArena Russian | 1252 | 1360 |
| LMArena Spanish | 1269 | 1408 |

## Instruction Following

- Llama-3.3-70B-Instruct: 71.3 (#152)
- Solar Pro4: 72.8 (#128)

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Instruction Following | 1242 | 1376 |
| LiveBench Instruction Following | 82.7% | — |

## Long Context

- Llama-3.3-70B-Instruct: 26.4 (#290)
- Solar Pro4: 42.0 (#125)

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Longer Query | 1256 | 1379 |
| Fiction.LiveBench | 33.3% | — |

## Writing & Preference

- Llama-3.3-70B-Instruct: 47.8 (#203)
- Solar Pro4: 56.7 (#133)

| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Text | 1274 | 1386 |
| LMArena Creative Writing | 1250 | 1313 |
| LMArena Multi-Turn | 1279 | 1391 |
| LiveBench Language | 39.2% | — |

## FAQ

### Is Llama-3.3-70B-Instruct better than Solar Pro4?

Solar Pro4 is the stronger model overall, scoring 41.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 3.4× less per token, which makes it the better buy when Solar Pro4's lead doesn't matter for your workload.

### Which is cheaper, Llama-3.3-70B-Instruct or Solar Pro4?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Solar Pro4 lists at $0.30 and $1.20.

### Is Llama-3.3-70B-Instruct or Solar Pro4 better for coding?

Solar Pro4 scores higher on coding benchmarks: 39.9 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

Solar Pro4 does, with 524K tokens against 128K.

### How many benchmarks do Llama-3.3-70B-Instruct and Solar Pro4 share?

17 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Solar Pro4 has 18.