Llama 3.3 Groq 7B on SWE-bench Verified: score, rank and what it means
Llama 3.3 Groq 7B scores 40.9 on SWE-bench Verified, placing it #511 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 7B 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 7B sits at #511 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 40.8 points. The median model in this index scores 61.0, which puts Llama 3.3 Groq 7B below the midpoint.
Groq models average 51.3 on this benchmark. Llama 3.3 Groq 7B is 10.4 points below that provider line, so it is a weaker result than the average Groq model.
Among small open-weight models, the average is 39.0. Llama 3.3 Groq 7B is 1.9 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 40.9 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 $1.01 per million input tokens, Llama 3.3 Groq 7B 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 |
|---|---|---|---|---|
| DeepSeek-V3.2 1.5B | DeepSeek | 4096 | $1.02 | 56.9 |
| Mixtral Turbo 7B | Together AI | 8192 | $0.68 | 53.2 |
| Mixtral Turbo 8B | Together AI | 4096 | $0.75 | 54.1 |
| Mixtral 8B | Mistral AI | 8192 | $0.91 | 55.4 |
| Llama 3.3 Groq 7B | Groq | 16384 | $1.01 | 56.7 |
| Nemotron 4 8B | NVIDIA | 4096 | $1.06 | 57.9 |
| Llama 3 Modal 7B | Modal | 4096 | $0.88 | 55.2 |
Other benchmarks for Llama 3.3 Groq 7B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 64.0 | 472 | 21th | |
| GPQA Diamond | 55.0 | 463 | 23th | |
| HumanEval | 61.1 | 485 | 19th | |
| MATH-500 | 56.5 | 502 | 16th | |
| AIME 2025 | 45.3 | 497 | 17th | |
| LiveCodeBench | 44.7 | 502 | 16th | |
| IFEval | 65.7 | 524 | 13th | |
| TruthfulQA | 64.0 | 469 | 22th | |
| HellaSwag | 70.5 | 529 | 12th | |
| MMMU | 56.0 | 488 | 19th | |
| Arena Elo | 1240.6 | 511 | 15th |
FAQ
What score does Llama 3.3 Groq 7B get on SWE-bench Verified?
Llama 3.3 Groq 7B scores 40.9 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 7B rank on SWE-bench Verified?
It ranks #511 of 600 models, in the 15th percentile. The leader is GPT-5.2 Pro at 81.7.
Is Llama 3.3 Groq 7B good at SWE-bench Verified?
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 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 7B cost?
Llama 3.3 Groq 7B costs $1.01 per million input tokens and $3.60 per million output tokens.