Coding · Groq

Llama 3.3 Groq Instruct on SWE-bench Verified: score, rank and what it means

Llama 3.3 Groq Instruct scores 65.1 on SWE-bench Verified, placing it #212 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
65.1
0–100 scale
Rank
#212
of 600
Percentile
65th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
16.6
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3.3 Groq Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 81.7
Claude Opus 4.5 Pro 81.1
GPT-5.2 Max 80.5
Claude Sonnet 4.5 Max 80.2
GPT-5.2 80.2
GPT-5.1 Pro 80.1
o4 Max 79.8
Claude Sonnet 4.5 Reasoning 79.6
Claude Opus 4.5 Max 79.5
Claude Opus 4.5 Reasoning 79.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg 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 Instruct sits at #212 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 16.6 points. The median model in this index scores 61.0, which puts Llama 3.3 Groq Instruct above the midpoint.

Groq models average 51.3 on this benchmark. Llama 3.3 Groq Instruct is 13.8 points above that provider line, so it is a stronger result than the average Groq model.

Among multimodal models, the average is 66.4. Llama 3.3 Groq Instruct is 1.3 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 65.1 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 $4.08 per million input tokens, Llama 3.3 Groq Instruct is above 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.

ModelProviderContextInput / 1MAvg score
Llama 4 Scout Chat Meta AI 32768 $4.66 78.0
Yi-Lightning Instruct 01.AI 128000 $4.16 76.5
Gemma 3 Chat Google DeepMind 65536 $3.74 74.5
QwQ Base Alibaba Cloud 65536 $3.68 74.1
Llama 3.3 Groq Instruct Groq 65536 $4.08 76.7
Llama 4 Scout Base Meta AI 32768 $3.95 75.3
Llama 3.1 Cerebras Cerebras 128000 $4.01 76.0

Other benchmarks for Llama 3.3 Groq Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 81.2 142 76th
GPQA Diamond 74.0 169 72th
HumanEval 79.6 246 59th
MATH-500 79.3 227 62th
AIME 2025 74.7 160 73th
LiveCodeBench 66.5 265 56th
IFEval 85.7 167 72th
TruthfulQA 79.7 195 68th
HellaSwag 84.8 257 57th
MMMU 73.5 205 66th
Arena Elo 1394.0 234 61th

FAQ

What score does Llama 3.3 Groq Instruct get on SWE-bench Verified?

Llama 3.3 Groq Instruct scores 65.1 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 Instruct rank on SWE-bench Verified?

It ranks #212 of 600 models, in the 65th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3.3 Groq Instruct 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 Instruct cost?

Llama 3.3 Groq Instruct costs $4.08 per million input tokens and $22.27 per million output tokens.

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