Preference · DeepSeek

DeepSeek-V3.2 Instruct on Arena Elo: score, rank and what it means

DeepSeek-V3.2 Instruct scores 1454.4 on Arena Elo, placing it #64 of 600 models in the index. The benchmark average is 1349.9 and the leader is Claude Opus 4.5 Max at 1501.9.

Score
1454.4
0–100 scale
Rank
#64
of 600
Percentile
89th
within the index
Benchmark avg
1349.9
all models
Leader
1501.9
Claude Opus 4.5 Max
Gap to leader
47.5
points

Score distribution

The chart shows the top 10 models on Arena Elo, with DeepSeek-V3.2 Instruct highlighted. Longer bars mean higher scores.

Claude Opus 4.5 Max 1501.9
GPT-5.2 Reasoning 1500.4
GPT-5.2 Turbo 1500.3
Claude Opus 4.5 Reasoning 1497.5
GPT-5.2 Max 1495.3
Gemini 3 Pro Turbo 1494.9
Grok 4 Max 1491.4
Claude Opus 4.5 1491.2
GPT-5.1 Pro 1490.0
Grok 4 Reasoning 1487.6

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
2 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
3 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
6 Gemini 3 Pro Turbo Google DeepMind 400000 $19.62 86.4
7 Grok 4 Max xAI 2000000 $19.70 86.6
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
10 Grok 4 Reasoning xAI 2000000 $19.67 87.8

What this score means

On Arena Elo, DeepSeek-V3.2 Instruct sits at #64 of 600. The leader, Claude Opus 4.5 Max, scores 1501.9, so the gap to the top is 47.5 points. The median model in this index scores 1373.8, which puts DeepSeek-V3.2 Instruct above the midpoint.

DeepSeek models average 1363.3 on this benchmark. DeepSeek-V3.2 Instruct is 91.1 points above that provider line, so it is a stronger result than the average DeepSeek model.

Among frontier models, the average is 1469.8. DeepSeek-V3.2 Instruct is 15.4 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 1454.4 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 $5.91 per million input tokens, DeepSeek-V3.2 Instruct is above the median input price of $4.00 across the 600 models scored on Arena Elo. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
o3 Reasoning OpenAI 200000 $18.07 85.3
o3 Turbo OpenAI 200000 $16.63 83.4
DeepSeek-V3.2 DeepSeek 1000000 $5.88 84.0
Gemini 2.5 Pro Google DeepMind 200000 $15.20 81.6
DeepSeek-V3.2 Instruct DeepSeek 128000 $5.91 83.7
DeepSeek-R1 DeepSeek 128000 $5.83 82.8
Claude Opus 4.5 Vision Anthropic 200000 $17.70 85.9

Other benchmarks for DeepSeek-V3.2 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 84.6 68 89th
GPQA Diamond 80.9 44 93th
SWE-bench Verified 75.2 39 94th
HumanEval 90.2 42 93th
MATH-500 86.3 87 86th
AIME 2025 83.9 49 92th
LiveCodeBench 75.2 93 85th
IFEval 91.3 46 92th
TruthfulQA 84.3 78 87th
HellaSwag 88.8 116 81th
MMMU 80.4 53 91th

FAQ

What score does DeepSeek-V3.2 Instruct get on Arena Elo?

DeepSeek-V3.2 Instruct scores 1454.4 on Arena Elo in this index. The benchmark average is 1349.9 and the top score is 1501.9.

How does DeepSeek-V3.2 Instruct rank on Arena Elo?

It ranks #64 of 600 models, in the 89th percentile. The leader is Claude Opus 4.5 Max at 1501.9.

Is DeepSeek-V3.2 Instruct good at Arena Elo?

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 Arena Elo score calculated?

Arena Elo is a preference 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 DeepSeek-V3.2 Instruct cost?

DeepSeek-V3.2 Instruct costs $5.91 per million input tokens and $29.01 per million output tokens.

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