Coding · Lambda

Llama 3 Lambda Instruct on SWE-bench Verified: score, rank and what it means

Llama 3 Lambda Instruct scores 64.3 on SWE-bench Verified, placing it #226 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
64.3
0–100 scale
Rank
#226
of 600
Percentile
62th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
17.4
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3 Lambda 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 Lambda Instruct sits at #226 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 17.4 points. The median model in this index scores 61.0, which puts Llama 3 Lambda Instruct above the midpoint.

Lambda models average 52.1 on this benchmark. Llama 3 Lambda Instruct is 12.2 points above that provider line, so it is a stronger result than the average Lambda model.

Among multimodal models, the average is 66.4. Llama 3 Lambda Instruct is 2.1 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 64.3 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 $3.92 per million input tokens, Llama 3 Lambda Instruct 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.

ModelProviderContextInput / 1MAvg score
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
Llama 3 Lambda Instruct Lambda 32768 $3.92 75.8
Mistral Large 3 Base Mistral AI 65536 $4.47 78.2
Llama 3 Lambda Chat Lambda 32768 $3.73 74.7

Other benchmarks for Llama 3 Lambda Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.8 154 74th
GPQA Diamond 69.1 293 51th
HumanEval 78.6 274 54th
MATH-500 78.6 247 59th
AIME 2025 74.8 158 74th
LiveCodeBench 70.5 181 70th
IFEval 84.2 211 65th
TruthfulQA 76.0 305 49th
HellaSwag 87.0 178 70th
MMMU 70.0 303 50th
Arena Elo 1382.5 268 55th

FAQ

What score does Llama 3 Lambda Instruct get on SWE-bench Verified?

Llama 3 Lambda Instruct scores 64.3 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Llama 3 Lambda Instruct rank on SWE-bench Verified?

It ranks #226 of 600 models, in the 62nd percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3 Lambda 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 Lambda Instruct cost?

Llama 3 Lambda Instruct costs $3.92 per million input tokens and $19.48 per million output tokens.

Related