Coding · Baseten

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

Llama 3 Baseten Instruct scores 62.8 on SWE-bench Verified, placing it #257 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
62.8
0–100 scale
Rank
#257
of 600
Percentile
57th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
18.9
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3 Baseten 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 Baseten Instruct sits at #257 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 18.9 points. The median model in this index scores 61.0, which puts Llama 3 Baseten Instruct above the midpoint.

Baseten models average 53.3 on this benchmark. Llama 3 Baseten Instruct is 9.5 points above that provider line, so it is a stronger result than the average Baseten model.

Among multimodal models, the average is 66.4. Llama 3 Baseten Instruct is 3.6 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 62.8 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 $3.91 per million input tokens, Llama 3 Baseten 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
Sonar Max Perplexity 32768 $11.60 77.8
Llama 3 Baseten Base Baseten 16384 $3.16 72.4
Llama 3 Baseten Chat Baseten 200000 $3.55 73.2
Codestral Chat Mistral AI 32768 $3.89 75.8
Llama 3 Baseten Instruct Baseten 200000 $3.91 76.2
Llama 3 Lambda Lambda 65536 $3.90 75.6
MiniMax-M2 Base MiniMax 65536 $4.20 76.4

Other benchmarks for Llama 3 Baseten Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.0 182 70th
GPQA Diamond 73.9 171 72th
HumanEval 78.6 273 55th
MATH-500 77.4 269 55th
AIME 2025 74.7 159 74th
LiveCodeBench 70.1 189 69th
IFEval 83.1 241 60th
TruthfulQA 79.3 205 66th
HellaSwag 86.8 184 69th
MMMU 72.0 244 59th
Arena Elo 1397.8 214 64th

FAQ

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

Llama 3 Baseten Instruct scores 62.8 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

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

It ranks #257 of 600 models, in the 57th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3 Baseten 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 Baseten Instruct cost?

Llama 3 Baseten Instruct costs $3.91 per million input tokens and $20.18 per million output tokens.

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