Model comparison
Llama 3.1-70B vs Llama-3.3-70B-Instruct
Llama 3.1-70B and Llama-3.3-70B-Instruct score almost the same on the Noometry Index (29.4 vs 30.3), so choose on price, context window or the category you care about most.
Last verified . 28 shared benchmarks.
MetaSummary
- They share 28 benchmarks with published results for both. Llama 3.1-70B scores higher in 2 categories and Llama-3.3-70B-Instruct in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Llama-3.3-70B-Instruct leads 47.8 to 35.6.
- The biggest single-benchmark swing is WeirdML: 9% for Llama 3.1-70B and 14.4% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
Side by side
| Llama 3.1-70B | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Meta | Meta |
| Noometry Index | 29.4 | 30.3 |
| Released | 2024-07-23 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 128K | 128K |
| Max output | 4K | 4K |
| Input $ / M tokens | $0.40 | $0.10 |
| Output $ / M tokens | $0.40 | $0.32 |
| Results tracked | 35 | 43 |
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Category by category
Coding Too close to call
Llama 3.1-70B: 29.8 (#292), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| WeirdML | 9% | 14.4% |
| BigCodeBench Instruct | 46.1% | 46.9% |
| LMArena Coding | 1260 | 1268 |
| BigCodeBench Complete | 54.8% | 57.5% |
| SciCode | — | 26% |
| LiveBench Coding | — | 36.6% |
Agentic & Tool Use Too close to call
Llama 3.1-70B: 24.9 (#104), Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| BALROG | 27.9% | 23% |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| TheAgentCompany | 6.9% | — |
Reasoning Llama 3.1-70B leads
Llama 3.1-70B: 20.8 (#215), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1257 |
| DTBench | 60% | 59.5% |
| LMCA | 14.8% | 17.5% |
| Epoch Capabilities Index | 125.91 | 127.32 |
| SimpleBench | — | 19.9% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| LiveBench Data Analysis | — | 49.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math Llama-3.3-70B-Instruct leads
Llama 3.1-70B: 12.7 (#298), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | 5.1% |
| LMArena Math | 1252 | 1268 |
| MATH Level 5 | 36.7% | 41.6% |
| Omni-MATH | 21% | — |
| LiveBench Math | — | 42.2% |
Knowledge Llama-3.3-70B-Instruct leads
Llama 3.1-70B: 24.1 (#264), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 44.2% | 47.4% |
| LMArena Expert | 1209 | 1225 |
| MMLU | 80.1% | 86.3% |
| MMLU-Pro | 65.3% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| GPQA (HELM) | 42.6% | — |
Multilingual Llama-3.3-70B-Instruct leads
Llama 3.1-70B: 38.9 (#220), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1219 | 1236 |
| LMArena Chinese | 1215 | 1217 |
| LMArena French | 1260 | 1280 |
| LMArena German | 1222 | 1251 |
| LMArena Japanese | 1132 | 1149 |
| LMArena Korean | 1140 | 1142 |
| LMArena Russian | 1234 | 1252 |
| LMArena Spanish | 1253 | 1269 |
Instruction Following Llama-3.3-70B-Instruct leads
Llama 3.1-70B: 65.5 (#218), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1231 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
| IFEval | 82.1% | — |
Long Context Llama 3.1-70B leads
Llama 3.1-70B: 37.6 (#208), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1241 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Llama-3.3-70B-Instruct leads
Llama 3.1-70B: 35.6 (#262), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | Llama 3.1-70B | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1261 | 1274 |
| LMArena Creative Writing | 1232 | 1250 |
| LMArena Multi-Turn | 1256 | 1279 |
| EQ-Bench Creative Writing | 784 | — |
| WildBench | 75.8% | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Llama 3.1-70B better than Llama-3.3-70B-Instruct?
Llama 3.1-70B and Llama-3.3-70B-Instruct score almost the same on the Noometry Index (29.4 vs 30.3), so choose on price, context window or the category you care about most.
Which is cheaper, Llama 3.1-70B 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; Llama 3.1-70B lists at $0.40 and $0.40.
Is Llama 3.1-70B or Llama-3.3-70B-Instruct better for coding?
They score almost the same on coding (29.8 vs 30.6); test both on your own repository before choosing.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do Llama 3.1-70B and Llama-3.3-70B-Instruct share?
28 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and Llama-3.3-70B-Instruct has 43.