Coding · Together AI

Llama 3.1 Turbo Base on SWE-bench Verified: score, rank and what it means

Llama 3.1 Turbo Base scores 62.2 on SWE-bench Verified, placing it #275 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
62.2
0–100 scale
Rank
#275
of 600
Percentile
54th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
19.5
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3.1 Turbo 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 3.1 Turbo Base sits at #275 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 19.5 points. The median model in this index scores 61.0, which puts Llama 3.1 Turbo Base above the midpoint.

Together AI models average 52.1 on this benchmark. Llama 3.1 Turbo Base is 10.1 points above that provider line, so it is a stronger result than the average Together AI model.

Among open-weight models, the average is 55.5. Llama 3.1 Turbo Base is 6.7 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 62.2 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 $3.30 per million input tokens, Llama 3.1 Turbo 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
Nemotron 4 Chat NVIDIA 65536 $3.99 75.0
o4 Nano OpenAI 65536 $8.61 72.0
Claude Opus 4.5 Nano Anthropic 32768 $9.38 73.3
Llama 3 Replicate Base Replicate 65536 $3.15 72.1
Llama 3.1 Turbo Base Together AI 16384 $3.30 73.0
MiniMax-Text Base MiniMax 128000 $3.66 74.9
Mistral Small 3 Instruct Mistral AI 32768 $3.97 75.1

Other benchmarks for Llama 3.1 Turbo Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 75.8 310 48th
GPQA Diamond 65.6 350 42th
HumanEval 79.4 252 58th
MATH-500 76.8 284 53th
AIME 2025 68.3 290 52th
LiveCodeBench 66.5 264 56th
IFEval 79.6 342 43th
TruthfulQA 77.2 275 54th
HellaSwag 84.5 267 56th
MMMU 67.0 358 40th
Arena Elo 1381.0 271 55th

FAQ

What score does Llama 3.1 Turbo Base get on SWE-bench Verified?

Llama 3.1 Turbo Base scores 62.2 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Llama 3.1 Turbo Base rank on SWE-bench Verified?

It ranks #275 of 600 models, in the 54th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3.1 Turbo Base 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 3.1 Turbo Base cost?

Llama 3.1 Turbo Base costs $3.30 per million input tokens and $13.59 per million output tokens.

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