Model comparison
Llama-3.3-70B-Instruct vs Solar Pro4
Solar Pro4 is the stronger model overall, scoring 42.1 to 30.6 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.
Last verified . 17 shared benchmarks.
Meta
UpstageSummary
- 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 38.8 to 15.3.
- 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.
Side by side
| Llama-3.3-70B-Instruct | Solar Pro4 | |
|---|---|---|
| Provider | Meta | Upstage |
| Noometry Index | 30.6 | 42.1 |
| Released | 2024-12-06 | 2026-08-06 |
| Weights | Open | Proprietary |
| Context window | 128K | 524K |
| Max output | 4K | 131K |
| Input $ / M tokens | $0.10 | $0.30 |
| Output $ / M tokens | $0.32 | $1.20 |
| Results tracked | 43 | 18 |
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Category by category
Coding Solar Pro4 leads
Llama-3.3-70B-Instruct: 31.0 (#290), Solar Pro4: 40.1 (#149)
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Coding | 1268 | 1437 |
| LMArena WebDev | — | 1371 |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.8 (#105), Solar Pro4: —
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
Reasoning Solar Pro4 leads
Llama-3.3-70B-Instruct: 14.1 (#327), Solar Pro4: 28.5 (#104)
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1399 |
| SimpleBench | 19.9% | — |
| CritPt | 0% | — |
| LiveBench Reasoning | 50.8% | — |
| DTBench | 59.5% | — |
| LiveBench Data Analysis | 49.5% | — |
| LMCA | 17.5% | — |
| Epoch Capabilities Index | 127.33 | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Solar Pro4 leads
Llama-3.3-70B-Instruct: 15.3 (#298), Solar Pro4: 38.8 (#128)
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Math | 1267 | 1416 |
| OTIS Mock AIME 2024-2025 | 5.1% | — |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |
Knowledge Solar Pro4 leads
Llama-3.3-70B-Instruct: 30.6 (#226), Solar Pro4: 39.8 (#129)
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Expert | 1225 | 1427 |
| GPQA Diamond | 47.4% | — |
| Confabulations | 22.8% | — |
| Vectara Hallucination Rate | 4.1% | — |
| MMLU | 86.3% | — |
Multilingual Solar Pro4 leads
Llama-3.3-70B-Instruct: 39.9 (#220), Solar Pro4: 48.7 (#139)
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Non-English | 1236 | 1361 |
| LMArena Chinese | 1217 | 1415 |
| LMArena French | 1281 | 1397 |
| LMArena German | 1251 | 1364 |
| LMArena Japanese | 1150 | 1309 |
| LMArena Korean | 1143 | 1382 |
| LMArena Russian | 1252 | 1360 |
| LMArena Spanish | 1270 | 1401 |
Instruction Following Solar Pro4 leads
Llama-3.3-70B-Instruct: 71.1 (#157), Solar Pro4: 72.7 (#132)
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Instruction Following | 1242 | 1377 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Solar Pro4 leads
Llama-3.3-70B-Instruct: 26.4 (#295), Solar Pro4: 42.1 (#130)
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Longer Query | 1256 | 1381 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Solar Pro4 leads
Llama-3.3-70B-Instruct: 47.6 (#207), Solar Pro4: 56.5 (#138)
| Benchmark | Llama-3.3-70B-Instruct | Solar Pro4 |
|---|---|---|
| LMArena Text | 1274 | 1386 |
| LMArena Creative Writing | 1250 | 1316 |
| LMArena Multi-Turn | 1280 | 1385 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Solar Pro4?
Solar Pro4 is the stronger model overall, scoring 42.1 to 30.6 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: 40.1 versus 31.0 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.