Llama 3 Anyscale Chat on IFEval: score, rank and what it means
Llama 3 Anyscale Chat scores 84.2 on IFEval, placing it #210 of 600 models in the index. The benchmark average is 78.6 and the leader is Claude Opus 4.5 Pro at 96.1.
Score distribution
The chart shows the top 10 models on IFEval, with Llama 3 Anyscale Chat highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 2 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 3 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 4 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 5 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 6 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 7 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
| 8 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 9 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 10 | o4 Reasoning | OpenAI | 1000000 | $19.69 | 87.5 |
What this score means
On IFEval, Llama 3 Anyscale Chat sits at #210 of 600. The leader, Claude Opus 4.5 Pro, scores 96.1, so the gap to the top is 11.9 points. The median model in this index scores 81.2, which puts Llama 3 Anyscale Chat above the midpoint.
Anyscale models average 76.1 on this benchmark. Llama 3 Anyscale Chat is 8.1 points above that provider line, so it is a stronger result than the average Anyscale model.
Among multimodal models, the average is 85.2. Llama 3 Anyscale Chat is 1.0 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 84.2 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 $3.67 per million input tokens, Llama 3 Anyscale Chat is below the median input price of $4.00 across the 600 models scored on IFEval. 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 |
|---|---|---|---|---|
| Hunyuan-Large Chat | Tencent | 200000 | $4.35 | 78.1 |
| QwQ | Alibaba Cloud | 128000 | $4.57 | 78.6 |
| Reka Flash Turbo | Reka AI | 128000 | $10.19 | 75.1 |
| Codestral Instruct | Mistral AI | 65536 | $4.01 | 75.1 |
| Llama 3 Anyscale Chat | Anyscale | 65536 | $3.67 | 74.6 |
| Llama 3 Lambda Instruct | Lambda | 32768 | $3.92 | 75.8 |
| Reka Flash Max | Reka AI | 32768 | $11.17 | 75.9 |
Other benchmarks for Llama 3 Anyscale Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 77.5 | 258 | 57th | |
| GPQA Diamond | 68.8 | 302 | 50th | |
| SWE-bench Verified | 61.8 | 283 | 53th | |
| HumanEval | 79.5 | 247 | 59th | |
| MATH-500 | 76.0 | 299 | 50th | |
| AIME 2025 | 72.9 | 205 | 66th | |
| LiveCodeBench | 66.3 | 268 | 55th | |
| TruthfulQA | 75.9 | 308 | 49th | |
| HellaSwag | 85.1 | 238 | 60th | |
| MMMU | 72.6 | 230 | 62th | |
| Arena Elo | 1373.6 | 302 | 50th |
FAQ
What score does Llama 3 Anyscale Chat get on IFEval?
Llama 3 Anyscale Chat scores 84.2 on IFEval in this index. The benchmark average is 78.6 and the top score is 96.1.
How does Llama 3 Anyscale Chat rank on IFEval?
It ranks #210 of 600 models, in the 65th percentile. The leader is Claude Opus 4.5 Pro at 96.1.
Is Llama 3 Anyscale Chat good at IFEval?
Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.
How is the IFEval score calculated?
IFEval is a instruction following 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 Anyscale Chat cost?
Llama 3 Anyscale Chat costs $3.67 per million input tokens and $14.56 per million output tokens.