Coding · Lambda

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

Llama 3 Lambda Base scores 59.0 on SWE-bench Verified, placing it #342 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
59.0
0–100 scale
Rank
#342
of 600
Percentile
43th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
22.7
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3 Lambda Base 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 Base sits at #342 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 22.7 points. The median model in this index scores 61.0, which puts Llama 3 Lambda Base below the midpoint.

Lambda models average 52.1 on this benchmark. Llama 3 Lambda Base is 6.9 points above that provider line, so it is a stronger result than the average Lambda model.

Among open-weight models, the average is 55.5. Llama 3 Lambda Base is 3.5 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 59.0 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 $3.19 per million input tokens, Llama 3 Lambda 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.

ModelProviderContextInput / 1MAvg score
Yi-Large Chat 01.AI 65536 $3.49 73.5
Qwen3-Max Nano Alibaba Cloud 65536 $8.31 71.7
Fusion Max OpenRouter 65536 $9.14 72.5
Fusion OpenRouter 16384 $8.41 71.1
Llama 3 Lambda Base Lambda 8192 $3.19 72.9
Nova Pro Mini Amazon 16384 $7.74 70.6
Claude 3.7 Sonnet Nano Anthropic 65536 $6.93 68.9

Other benchmarks for Llama 3 Lambda Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.0 242 60th
GPQA Diamond 70.0 262 56th
HumanEval 75.3 339 44th
MATH-500 76.6 289 52th
AIME 2025 64.6 345 43th
LiveCodeBench 65.7 280 53th
IFEval 82.3 266 56th
TruthfulQA 74.4 344 43th
HellaSwag 85.2 234 61th
MMMU 71.2 266 56th
Arena Elo 1383.4 267 56th

FAQ

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

Llama 3 Lambda Base scores 59.0 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

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

It ranks #342 of 600 models, in the 43rd percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3 Lambda Base 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 Lambda Base cost?

Llama 3 Lambda Base costs $3.19 per million input tokens and $14.51 per million output tokens.

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