Model comparison
Claude 3.5 Haiku vs Llama-3.3-70B-Instruct
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.6 to 29.2 on the Noometry Index.
Last verified . 37 shared benchmarks.
Summary
- They share 37 benchmarks with published results for both. Claude 3.5 Haiku scores higher in 5 categories and Llama-3.3-70B-Instruct in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama-3.3-70B-Instruct leads 30.6 to 18.7.
- The biggest single-benchmark swing is LiveBench Reasoning: 28.1% for Claude 3.5 Haiku and 50.8% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Claude 3.5 Haiku | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | Anthropic | Meta |
| Noometry Index | 29.2 | 30.6 |
| Released | 2024-10-22 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | — | 128K |
| Max output | — | 4K |
| Input $ / M tokens | — | $0.10 |
| Output $ / M tokens | — | $0.32 |
| Results tracked | 49 | 43 |
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Category by category
Coding Claude 3.5 Haiku leads
Claude 3.5 Haiku: 32.9 (#265), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 27.4% | 26% |
| WeirdML | 30.7% | 14.4% |
| BigCodeBench Instruct | 46.1% | 46.9% |
| LiveBench Coding | 51.4% | 36.6% |
| LMArena Coding | 1286 | 1268 |
| BigCodeBench Complete | 59% | 57.5% |
| Aider Polyglot | 28% | — |
| CadEval | 32% | — |
Agentic & Tool Use Claude 3.5 Haiku leads
Claude 3.5 Haiku: 28.0 (#95), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| BALROG | 19.3% | 23% |
| Berkeley Function Calling Leaderboard | — | 31.9% |
Reasoning Claude 3.5 Haiku leads
Claude 3.5 Haiku: 17.7 (#290), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 0% | 0% |
| LiveBench Reasoning | 28.1% | 50.8% |
| LMArena Hard Prompts | 1251 | 1257 |
| DTBench | 56.7% | 59.5% |
| LiveBench Data Analysis | 48.5% | 49.5% |
| Epoch Capabilities Index | 127.15 | 127.33 |
| LiveBench | 43.5% | 50.2% |
| SimpleBench | — | 19.9% |
| LMCA | — | 17.5% |
| ForecastBench | — | 58.6 |
Math Too close to call
Claude 3.5 Haiku: 14.7 (#300), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 4.3% | 5.1% |
| LiveBench Math | 35.5% | 42.2% |
| LMArena Math | 1244 | 1267 |
| MATH Level 5 | 46.4% | 41.6% |
| Omni-MATH | 22.4% | — |
| FrontierMath (Feb 2025 set) | 0.3% | — |
Knowledge Llama-3.3-70B-Instruct leads
Claude 3.5 Haiku: 18.7 (#281), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 38.1% | 47.4% |
| Confabulations | 36.7% | 22.8% |
| LMArena Expert | 1208 | 1225 |
| MMLU | 74.3% | 86.3% |
| MMLU-Pro | 60.5% | — |
| Vectara Hallucination Rate | — | 4.1% |
| GPQA (HELM) | 36.3% | — |
Multimodal Not comparable
Claude 3.5 Haiku: 26.8 (#117), Llama-3.3-70B-Instruct: —
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1092 | — |
| GeoBench | 34% | — |
Multilingual Too close to call
Claude 3.5 Haiku: 40.0 (#218), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1238 | 1236 |
| LMArena Chinese | 1229 | 1217 |
| LMArena French | 1264 | 1281 |
| LMArena German | 1237 | 1251 |
| LMArena Japanese | 1175 | 1150 |
| LMArena Korean | 1173 | 1143 |
| LMArena Russian | 1253 | 1252 |
| LMArena Spanish | 1261 | 1270 |
Instruction Following Llama-3.3-70B-Instruct leads
Claude 3.5 Haiku: 62.9 (#234), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| LiveBench Instruction Following | 61.9% | 82.7% |
| LMArena Instruction Following | 1241 | 1242 |
| IFEval | 79.2% | — |
Long Context Claude 3.5 Haiku leads
Claude 3.5 Haiku: 38.3 (#200), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1261 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Llama-3.3-70B-Instruct leads
Claude 3.5 Haiku: 42.7 (#234), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | Claude 3.5 Haiku | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1255 | 1274 |
| LMArena Creative Writing | 1233 | 1250 |
| LMArena Multi-Turn | 1265 | 1280 |
| LiveBench Language | 35.4% | 39.2% |
| Short-Story Creative Writing | 73.5% | — |
| EQ-Bench Creative Writing | 1146 | — |
| WildBench | 76% | — |
Frequently asked questions
Is Claude 3.5 Haiku better than Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.6 to 29.2 on the Noometry Index.
Is Claude 3.5 Haiku or Llama-3.3-70B-Instruct better for coding?
Claude 3.5 Haiku scores higher on coding benchmarks: 32.9 versus 31.0 in the Noometry coding category.
How many benchmarks do Claude 3.5 Haiku and Llama-3.3-70B-Instruct share?
37 benchmarks have published results for both models. Claude 3.5 Haiku has 49 scored results on Noometry and Llama-3.3-70B-Instruct has 43.