Mathematics · Microsoft

Phi-4 Instruct on MATH-500: score, rank and what it means

Phi-4 Instruct scores 80.0 on MATH-500, placing it #207 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
80.0
0–100 scale
Rank
#207
of 600
Percentile
66th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
16.7
points

Score distribution

The chart shows the top 10 models on MATH-500, with Phi-4 Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
3 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, Phi-4 Instruct sits at #207 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 16.7 points. The median model in this index scores 76.0, which puts Phi-4 Instruct above the midpoint.

Microsoft models average 64.4 on this benchmark. Phi-4 Instruct is 15.6 points above that provider line, so it is a stronger result than the average Microsoft model.

Among multimodal models, the average is 81.3. Phi-4 Instruct is 1.3 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 80.0 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 $3.79 per million input tokens, Phi-4 Instruct is below the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 3.1 Turbo Instruct Together AI 32768 $4.21 76.7
Llama 4 Scout Chat Meta AI 32768 $4.66 78.0
Yi-Lightning 01.AI 32768 $4.22 77.2
Llama 3 Replicate Replicate 200000 $3.75 75.7
Phi-4 Instruct Microsoft 128000 $3.79 75.3
QwQ Alibaba Cloud 128000 $4.57 78.6
DeepSeek-Coder DeepSeek 32768 $4.55 78.1

Other benchmarks for Phi-4 Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.6 289 52th
GPQA Diamond 73.5 186 69th
SWE-bench Verified 62.7 263 56th
HumanEval 77.8 292 51th
AIME 2025 74.0 182 70th
LiveCodeBench 69.8 195 68th
IFEval 80.9 314 48th
TruthfulQA 78.2 251 58th
HellaSwag 83.4 310 48th
MMMU 71.6 253 58th
Arena Elo 1401.7 205 66th

FAQ

What score does Phi-4 Instruct get on MATH-500?

Phi-4 Instruct scores 80.0 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Phi-4 Instruct rank on MATH-500?

It ranks #207 of 600 models, in the 66th percentile. The leader is GPT-5.2 Max at 96.7.

Is Phi-4 Instruct good at MATH-500?

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 MATH-500 score calculated?

MATH-500 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 Phi-4 Instruct cost?

Phi-4 Instruct costs $3.79 per million input tokens and $17.11 per million output tokens.

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