Llama 3.3 Groq Base on SWE-bench Verified: score, rank and what it means
Llama 3.3 Groq Base scores 61.0 on SWE-bench Verified, placing it #298 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 3.3 Groq Base 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 3.3 Groq Base sits at #298 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 20.7 points. The median model in this index scores 61.0, which puts Llama 3.3 Groq Base above the midpoint.
Groq models average 51.3 on this benchmark. Llama 3.3 Groq Base is 9.7 points above that provider line, so it is a stronger result than the average Groq model.
Among open-weight models, the average is 55.5. Llama 3.3 Groq Base is 5.5 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.
Read a 61.0 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 $3.24 per million input tokens, Llama 3.3 Groq Base 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 |
|---|---|---|---|---|
| Mixtral Base | Mistral AI | 65536 | $2.95 | 71.1 |
| Claude Sonnet 4.5 Nano | Anthropic | 8192 | $8.93 | 72.0 |
| Gemini 3 Pro Nano | Google DeepMind | 8192 | $9.11 | 73.2 |
| Firesearch Max | Fireworks AI | 8192 | $7.80 | 69.9 |
| Llama 3.3 Groq Base | Groq | 16384 | $3.24 | 73.5 |
| Mixtral Instruct | Mistral AI | 65536 | $3.35 | 72.9 |
| OLMo 2 Instruct | Allen Institute for AI | 32768 | $3.03 | 71.7 |
Other benchmarks for Llama 3.3 Groq Base
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 75.3 | 324 | 46th | |
| GPQA Diamond | 65.6 | 351 | 42th | |
| HumanEval | 79.3 | 257 | 57th | |
| MATH-500 | 77.3 | 271 | 55th | |
| AIME 2025 | 71.0 | 235 | 61th | |
| LiveCodeBench | 66.8 | 257 | 57th | |
| IFEval | 81.0 | 310 | 48th | |
| TruthfulQA | 75.9 | 310 | 48th | |
| HellaSwag | 85.2 | 235 | 61th | |
| MMMU | 69.8 | 311 | 48th | |
| Arena Elo | 1375.9 | 288 | 52th |
FAQ
What score does Llama 3.3 Groq Base get on SWE-bench Verified?
Llama 3.3 Groq Base scores 61.0 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does Llama 3.3 Groq Base rank on SWE-bench Verified?
It ranks #298 of 600 models, in the 50th percentile. The leader is GPT-5.2 Pro at 81.7.
Is Llama 3.3 Groq Base 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 3.3 Groq Base cost?
Llama 3.3 Groq Base costs $3.24 per million input tokens and $12.28 per million output tokens.