Mathematics · Together AI

Llama 3.1 Turbo Instruct on AIME 2025: score, rank and what it means

Llama 3.1 Turbo Instruct scores 70.5 on AIME 2025, placing it #249 of 600 models in the index. The benchmark average is 63.6 and the leader is Claude Opus 4.5 Pro at 91.4.

Score
70.5
0–100 scale
Rank
#249
of 600
Percentile
59th
within the index
Benchmark avg
63.6
all models
Leader
91.4
Claude Opus 4.5 Pro
Gap to leader
20.9
points

Score distribution

The chart shows the top 10 models on AIME 2025, with Llama 3.1 Turbo Instruct highlighted. Longer bars mean higher scores.

Claude Opus 4.5 Pro 91.4
GPT-5.2 Pro 91.0
GPT-5.1 Pro 90.6
GPT-5.2 90.6
o4 Reasoning 90.3
o4 Pro 89.9
Claude Opus 4.5 89.8
Gemini 3 Pro Max 89.6
Claude Opus 4.5 Max 89.3
Claude Opus 4.5 Reasoning 88.9

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
2 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
3 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
4 GPT-5.2 OpenAI 2000000 $19.71 88.0
5 o4 Reasoning OpenAI 1000000 $19.69 87.5
6 o4 Pro OpenAI 1000000 $19.37 87.3
7 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
8 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
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 AIME 2025, Llama 3.1 Turbo Instruct sits at #249 of 600. The leader, Claude Opus 4.5 Pro, scores 91.4, so the gap to the top is 20.9 points. The median model in this index scores 67.6, which puts Llama 3.1 Turbo Instruct above the midpoint.

Together AI models average 56.2 on this benchmark. Llama 3.1 Turbo Instruct is 14.3 points above that provider line, so it is a stronger result than the average Together AI model.

Among multimodal models, the average is 73.9. Llama 3.1 Turbo Instruct is 3.4 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 70.5 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 $4.21 per million input tokens, Llama 3.1 Turbo Instruct is above the median input price of $4.00 across the 600 models scored on AIME 2025. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Mixtral Groq Instruct Groq 16384 $3.25 72.5
Yi-Lightning 01.AI 32768 $4.22 77.2
Yi-Lightning Base 01.AI 16384 $3.31 72.7
Firefunction Max Fireworks AI 32768 $9.12 73.2
Llama 3.1 Turbo Instruct Together AI 32768 $4.21 76.7
Hunyuan-Large Chat Tencent 200000 $4.35 78.1
Llama 3 Hyperbolic Instruct Hyperbolic 200000 $3.79 76.2

Other benchmarks for Llama 3.1 Turbo Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.3 176 71th
GPQA Diamond 73.4 191 68th
SWE-bench Verified 68.3 137 77th
HumanEval 79.2 260 57th
MATH-500 80.3 203 66th
LiveCodeBench 66.9 255 58th
IFEval 86.0 158 74th
TruthfulQA 81.0 156 74th
HellaSwag 86.8 185 69th
MMMU 71.3 265 56th
Arena Elo 1417.0 161 73th

FAQ

What score does Llama 3.1 Turbo Instruct get on AIME 2025?

Llama 3.1 Turbo Instruct scores 70.5 on AIME 2025 in this index. The benchmark average is 63.6 and the top score is 91.4.

How does Llama 3.1 Turbo Instruct rank on AIME 2025?

It ranks #249 of 600 models, in the 59th percentile. The leader is Claude Opus 4.5 Pro at 91.4.

Is Llama 3.1 Turbo Instruct good at AIME 2025?

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 AIME 2025 score calculated?

AIME 2025 is a mathematics 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 Instruct cost?

Llama 3.1 Turbo Instruct costs $4.21 per million input tokens and $17.73 per million output tokens.

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