Instruction following · DeepSeek

DeepSeek-V3.2 Chat on IFEval: score, rank and what it means

DeepSeek-V3.2 Chat scores 88.6 on IFEval, placing it #91 of 600 models in the index. The benchmark average is 78.6 and the leader is Claude Opus 4.5 Pro at 96.1.

Score
88.6
0–100 scale
Rank
#91
of 600
Percentile
85th
within the index
Benchmark avg
78.6
all models
Leader
96.1
Claude Opus 4.5 Pro
Gap to leader
7.5
points

Score distribution

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

Claude Opus 4.5 Pro 96.1
Claude Opus 4.5 Reasoning 95.9
GPT-5.2 Pro 95.6
GPT-5.2 Max 95.2
GPT-5.2 Reasoning 95.0
GPT-5.1 Pro 94.8
Gemini 3 Pro Max 94.7
GPT-5.1 Reasoning 94.6
Claude Opus 4.5 Max 94.5
o4 Reasoning 94.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
2 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9
8 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 o4 Reasoning OpenAI 1000000 $19.69 87.5

What this score means

On IFEval, DeepSeek-V3.2 Chat sits at #91 of 600. The leader, Claude Opus 4.5 Pro, scores 96.1, so the gap to the top is 7.5 points. The median model in this index scores 81.2, which puts DeepSeek-V3.2 Chat above the midpoint.

DeepSeek models average 80.3 on this benchmark. DeepSeek-V3.2 Chat is 8.3 points above that provider line, so it is a stronger result than the average DeepSeek model.

Among multimodal models, the average is 85.2. DeepSeek-V3.2 Chat is 3.4 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 88.6 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 $5.80 per million input tokens, DeepSeek-V3.2 Chat is above the median input price of $4.00 across the 600 models scored on IFEval. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Doubao 1.5 Pro Reasoning ByteDance 128000 $14.56 81.6
ERNIE 4.5 Reasoning Baidu 1000000 $14.36 79.8
GPT-4.1 Vision OpenAI 1000000 $14.88 82.0
o3 OpenAI 400000 $16.80 84.5
DeepSeek-V3.2 Chat DeepSeek 1000000 $5.80 82.5
GPT-5.2 Mini OpenAI 200000 $13.34 80.4
Grok 3 Pro xAI 400000 $16.61 83.8

Other benchmarks for DeepSeek-V3.2 Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 82.5 114 81th
GPQA Diamond 78.0 85 86th
SWE-bench Verified 69.6 120 80th
HumanEval 87.9 74 88th
MATH-500 90.2 46 92th
AIME 2025 81.0 75 88th
LiveCodeBench 77.9 65 89th
TruthfulQA 86.3 45 93th
HellaSwag 89.2 107 82th
MMMU 76.0 140 77th
Arena Elo 1462.2 48 92th

FAQ

What score does DeepSeek-V3.2 Chat get on IFEval?

DeepSeek-V3.2 Chat scores 88.6 on IFEval in this index. The benchmark average is 78.6 and the top score is 96.1.

How does DeepSeek-V3.2 Chat rank on IFEval?

It ranks #91 of 600 models, in the 85th percentile. The leader is Claude Opus 4.5 Pro at 96.1.

Is DeepSeek-V3.2 Chat good at IFEval?

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 IFEval score calculated?

IFEval is a instruction following 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 Chat cost?

DeepSeek-V3.2 Chat costs $5.80 per million input tokens and $26.43 per million output tokens.

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