Llama 3.1 Turbo 1.5B on IFEval: score, rank and what it means
Llama 3.1 Turbo 1.5B scores 63.7 on IFEval, placing it #541 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 3.1 Turbo 1.5B 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 3.1 Turbo 1.5B sits at #541 of 600. The leader, Claude Opus 4.5 Pro, scores 96.1, so the gap to the top is 32.4 points. The median model in this index scores 81.2, which puts Llama 3.1 Turbo 1.5B below the midpoint.
Together AI models average 74.4 on this benchmark. Llama 3.1 Turbo 1.5B is 10.7 points below that provider line, so it is a weaker result than the average Together AI model.
Among small open-weight models, the average is 64.9. Llama 3.1 Turbo 1.5B is 1.2 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 63.7 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 $0.46 per million input tokens, Llama 3.1 Turbo 1.5B 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 |
|---|---|---|---|---|
| Arctic 8B | Snowflake | 8192 | $0.65 | 52.4 |
| Llama 4 Scout 1.5B | Meta AI | 8192 | $0.66 | 53.0 |
| Command R+ 1.5B | Cohere | 32768 | $0.51 | 50.0 |
| Qwen3-Coder 1.5B | Alibaba Cloud | 32768 | $0.76 | 52.8 |
| Llama 3.1 Turbo 1.5B | Together AI | 16384 | $0.46 | 49.8 |
| Hunyuan-Large 1.5B | Tencent | 8192 | $0.54 | 50.5 |
| MiniMax-Text 1.5B | MiniMax | 32768 | $0.58 | 51.5 |
Other benchmarks for Llama 3.1 Turbo 1.5B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 57.8 | 551 | 8th | |
| GPQA Diamond | 43.7 | 572 | 5th | |
| SWE-bench Verified | 34.7 | 560 | 7th | |
| HumanEval | 50.4 | 571 | 5th | |
| MATH-500 | 49.9 | 558 | 7th | |
| AIME 2025 | 33.2 | 576 | 4th | |
| LiveCodeBench | 38.1 | 549 | 9th | |
| TruthfulQA | 56.6 | 557 | 7th | |
| HellaSwag | 69.4 | 543 | 10th | |
| MMMU | 50.0 | 554 | 8th | |
| Arena Elo | 1184.1 | 570 | 5th |
FAQ
What score does Llama 3.1 Turbo 1.5B get on IFEval?
Llama 3.1 Turbo 1.5B scores 63.7 on IFEval in this index. The benchmark average is 78.6 and the top score is 96.1.
How does Llama 3.1 Turbo 1.5B rank on IFEval?
It ranks #541 of 600 models, in the 10th percentile. The leader is Claude Opus 4.5 Pro at 96.1.
Is Llama 3.1 Turbo 1.5B good at IFEval?
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 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 3.1 Turbo 1.5B cost?
Llama 3.1 Turbo 1.5B costs $0.46 per million input tokens and $2.24 per million output tokens.