Coding · Together AI

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

Llama 3.1 Turbo scores 63.6 on SWE-bench Verified, placing it #245 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
63.6
0–100 scale
Rank
#245
of 600
Percentile
59th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
18.1
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3.1 Turbo 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 sits at #245 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 18.1 points. The median model in this index scores 61.0, which puts Llama 3.1 Turbo above the midpoint.

Together AI models average 52.1 on this benchmark. Llama 3.1 Turbo is 11.5 points above that provider line, so it is a stronger result than the average Together AI model.

Among multimodal models, the average is 66.4. Llama 3.1 Turbo is 2.8 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 63.6 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 $4.17 per million input tokens, Llama 3.1 Turbo 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
Yi-Large 01.AI 32768 $3.40 73.5
Gemini 2.5 Pro Mini Google DeepMind 128000 $10.07 74.2
Llama 3 Modal Modal 32768 $3.82 74.9
Llama 3 Modal Instruct Modal 128000 $3.84 75.2
Llama 3.1 Turbo Together AI 200000 $4.17 76.8
Hunyuan-Turbo Tencent 128000 $10.55 75.8
o3 Mini OpenAI 128000 $10.68 76.2

Other benchmarks for Llama 3.1 Turbo

BenchmarkScoreRankPercentileRelative
MMLU-Pro 79.8 188 69th
GPQA Diamond 72.0 220 63th
HumanEval 80.1 230 62th
MATH-500 79.8 215 64th
AIME 2025 72.2 214 64th
LiveCodeBench 69.9 193 68th
IFEval 83.6 225 63th
TruthfulQA 81.5 141 77th
HellaSwag 87.9 145 76th
MMMU 74.1 187 69th
Arena Elo 1392.6 238 60th

FAQ

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

Llama 3.1 Turbo scores 63.6 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 rank on SWE-bench Verified?

It ranks #245 of 600 models, in the 59th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3.1 Turbo 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 cost?

Llama 3.1 Turbo costs $4.17 per million input tokens and $21.10 per million output tokens.

Related