Llama 3 Hyperbolic on SWE-bench Verified: score, rank and what it means
Llama 3 Hyperbolic scores 64.4 on SWE-bench Verified, placing it #223 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.
Score distribution
The chart shows the top 10 models on SWE-bench Verified, with Llama 3 Hyperbolic highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 2 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 3 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 4 | Claude Sonnet 4.5 Max | Anthropic | 1000000 | $19.90 | 87.2 |
| 5 | GPT-5.2 | OpenAI | 2000000 | $19.71 | 88.0 |
| 6 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 7 | o4 Max | OpenAI | 2000000 | $19.51 | 86.2 |
| 8 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 9 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 10 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
What this score means
On SWE-bench Verified, Llama 3 Hyperbolic sits at #223 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 17.3 points. The median model in this index scores 61.0, which puts Llama 3 Hyperbolic above the midpoint.
Hyperbolic models average 52.7 on this benchmark. Llama 3 Hyperbolic is 11.7 points above that provider line, so it is a stronger result than the average Hyperbolic model.
Among multimodal models, the average is 66.4. Llama 3 Hyperbolic is 2.0 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 64.4 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 $3.86 per million input tokens, Llama 3 Hyperbolic is below the median input price of $4.00 across the 600 models scored on SWE-bench Verified. 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 3.1 Cerebras Chat | Cerebras | 32768 | $4.07 | 76.0 |
| Sonar Reasoning | Perplexity | 200000 | $11.48 | 76.2 |
| DeepSeek-Coder Chat | DeepSeek | 200000 | $4.20 | 77.3 |
| Magistral Chat | Mistral AI | 128000 | $4.33 | 76.8 |
| Llama 3 Hyperbolic | Hyperbolic | 65536 | $3.86 | 74.5 |
| Nemotron 4 Instruct | NVIDIA | 65536 | $4.10 | 76.4 |
| Codestral Instruct | Mistral AI | 65536 | $4.01 | 75.1 |
Other benchmarks for Llama 3 Hyperbolic
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 78.2 | 238 | 60th | |
| GPQA Diamond | 72.6 | 206 | 66th | |
| HumanEval | 79.3 | 255 | 58th | |
| MATH-500 | 76.3 | 292 | 51th | |
| AIME 2025 | 66.8 | 316 | 47th | |
| LiveCodeBench | 64.0 | 318 | 47th | |
| IFEval | 83.7 | 218 | 64th | |
| TruthfulQA | 75.9 | 309 | 49th | |
| HellaSwag | 85.3 | 231 | 62th | |
| MMMU | 73.0 | 219 | 64th | |
| Arena Elo | 1391.3 | 245 | 59th |
FAQ
What score does Llama 3 Hyperbolic get on SWE-bench Verified?
Llama 3 Hyperbolic scores 64.4 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does Llama 3 Hyperbolic rank on SWE-bench Verified?
It ranks #223 of 600 models, in the 63rd percentile. The leader is GPT-5.2 Pro at 81.7.
Is Llama 3 Hyperbolic good at SWE-bench Verified?
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 SWE-bench Verified score calculated?
SWE-bench Verified is a coding 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 Hyperbolic cost?
Llama 3 Hyperbolic costs $3.86 per million input tokens and $19.72 per million output tokens.