Llama 3.1 Turbo 1.5B on GPQA Diamond: score, rank and what it means
Llama 3.1 Turbo 1.5B scores 43.7 on GPQA Diamond, placing it #572 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.
Score distribution
The chart shows the top 10 models on GPQA Diamond, with Llama 3.1 Turbo 1.5B highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 2 | GPT-5.1 Max | OpenAI | 2000000 | $20.50 | 87.9 |
| 3 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 4 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 7 | o4 Turbo | OpenAI | 2000000 | $19.34 | 86.6 |
| 8 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 9 | Grok 4 Turbo | xAI | 1000000 | $19.21 | 86.7 |
| 10 | Claude Sonnet 4.5 Max | Anthropic | 1000000 | $19.90 | 87.2 |
What this score means
On GPQA Diamond, Llama 3.1 Turbo 1.5B sits at #572 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 43.3 points. The median model in this index scores 68.8, which puts Llama 3.1 Turbo 1.5B below the midpoint.
Together AI models average 60.8 on this benchmark. Llama 3.1 Turbo 1.5B is 17.1 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 49.3. Llama 3.1 Turbo 1.5B is 5.6 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 43.7 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 GPQA Diamond. 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 |
|---|---|---|---|---|
| Llama 3 Baseten 1.5B | Baseten | 4096 | $0.42 | 48.2 |
| Mistral Small 3 1.5B | Mistral AI | 32768 | $0.42 | 49.2 |
| Gemma 3 1.5B | Google DeepMind | 32768 | $0.40 | 48.9 |
| Command R+ 1.5B | Cohere | 32768 | $0.51 | 50.0 |
| Llama 3.1 Turbo 1.5B | Together AI | 16384 | $0.46 | 49.8 |
| DBRX 1.5B | Databricks | 16384 | $0.32 | 47.0 |
| Llama 3 DeepInfra 1.5B | DeepInfra | 32768 | $0.48 | 48.8 |
Other benchmarks for Llama 3.1 Turbo 1.5B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 57.8 | 551 | 8th | |
| 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 | |
| IFEval | 63.7 | 541 | 10th | |
| 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 GPQA Diamond?
Llama 3.1 Turbo 1.5B scores 43.7 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.
How does Llama 3.1 Turbo 1.5B rank on GPQA Diamond?
It ranks #572 of 600 models, in the 5th percentile. The leader is GPT-5.2 Reasoning at 87.0.
Is Llama 3.1 Turbo 1.5B good at GPQA Diamond?
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 GPQA Diamond score calculated?
GPQA Diamond is a reasoning 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.