Llama 3 Modal Instruct on HellaSwag: score, rank and what it means
Llama 3 Modal Instruct scores 83.5 on HellaSwag, placing it #305 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.
Score distribution
The chart shows the top 10 models on HellaSwag, with Llama 3 Modal Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 3 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 4 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
| 8 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 9 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 10 | o4 Pro | OpenAI | 1000000 | $19.37 | 87.3 |
What this score means
On HellaSwag, Llama 3 Modal Instruct sits at #305 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 12.6 points. The median model in this index scores 83.6, which puts Llama 3 Modal Instruct below the midpoint.
Modal models average 77.7 on this benchmark. Llama 3 Modal Instruct is 5.8 points above that provider line, so it is a stronger result than the average Modal model.
Among multimodal models, the average is 86.8. Llama 3 Modal Instruct is 3.3 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 83.5 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.
Price context
At $3.84 per million input tokens, Llama 3 Modal Instruct is below the median input price of $4.00 across the 600 models scored on HellaSwag. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Sonar Turbo | Perplexity | 65536 | $10.71 | 75.7 |
| Auto Max | OpenRouter | 8192 | $8.41 | 71.2 |
| Fusion Pro | OpenRouter | 65536 | $8.69 | 72.2 |
| Hunyuan-Large Base | Tencent | 200000 | $3.64 | 74.0 |
| Llama 3 Modal Instruct | Modal | 128000 | $3.84 | 75.2 |
| Llama 3 Novita Chat | Novita AI | 65536 | $3.50 | 74.2 |
| Seed-OSS | ByteDance | 32768 | $3.74 | 75.3 |
Other benchmarks for Llama 3 Modal Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 80.6 | 166 | 72th | |
| GPQA Diamond | 71.9 | 221 | 63th | |
| SWE-bench Verified | 63.6 | 244 | 59th | |
| HumanEval | 79.5 | 248 | 59th | |
| MATH-500 | 81.3 | 183 | 70th | |
| AIME 2025 | 68.7 | 285 | 53th | |
| LiveCodeBench | 67.0 | 251 | 58th | |
| IFEval | 83.7 | 219 | 64th | |
| TruthfulQA | 78.6 | 233 | 61th | |
| MMMU | 69.2 | 323 | 46th | |
| Arena Elo | 1407.2 | 189 | 69th |
FAQ
What score does Llama 3 Modal Instruct get on HellaSwag?
Llama 3 Modal Instruct scores 83.5 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.
How does Llama 3 Modal Instruct rank on HellaSwag?
It ranks #305 of 600 models, in the 49th percentile. The leader is GPT-5.2 Max at 96.1.
Is Llama 3 Modal Instruct good at HellaSwag?
It is below the median for this benchmark, so test it carefully before relying on it for this capability. The result should be read alongside its price and context window.
How is the HellaSwag score calculated?
HellaSwag is a commonsense benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.
What does Llama 3 Modal Instruct cost?
Llama 3 Modal Instruct costs $3.84 per million input tokens and $18.84 per million output tokens.