Mathematics · Moonshot AI

Kimi K1.5 7B on MATH-500: score, rank and what it means

Kimi K1.5 7B scores 59.7 on MATH-500, placing it #469 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
59.7
0–100 scale
Rank
#469
of 600
Percentile
22th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
37.0
points

Score distribution

The chart shows the top 10 models on MATH-500, with Kimi K1.5 7B 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, Kimi K1.5 7B sits at #469 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 37.0 points. The median model in this index scores 76.0, which puts Kimi K1.5 7B below the midpoint.

Moonshot AI models average 74.0 on this benchmark. Kimi K1.5 7B is 14.3 points below that provider line, so it is a weaker result than the average Moonshot AI model.

Among small open-weight models, the average is 53.7. Kimi K1.5 7B is 6.0 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 59.7 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 $1.35 per million input tokens, Kimi K1.5 7B 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
Nemotron 4 8B NVIDIA 4096 $1.06 57.9
Yi-Lightning 7B 01.AI 32768 $1.00 55.9
MiniMax-Text 7B MiniMax 16384 $1.10 57.7
Fusion Nano OpenRouter 8192 $2.96 56.8
Kimi K1.5 7B Moonshot AI 8192 $1.35 59.9
Llama 3 Modal 8B Modal 32768 $0.98 56.6
Firesearch Mini Fireworks AI 32768 $3.94 61.5

Other benchmarks for Kimi K1.5 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 66.3 437 27th
GPQA Diamond 56.0 449 25th
SWE-bench Verified 45.6 455 24th
HumanEval 62.3 466 22th
AIME 2025 48.0 470 22th
LiveCodeBench 52.3 424 29th
IFEval 72.4 427 29th
TruthfulQA 65.2 457 24th
HellaSwag 74.3 451 25th
MMMU 57.2 467 22th
Arena Elo 1269.9 459 24th

FAQ

What score does Kimi K1.5 7B get on MATH-500?

Kimi K1.5 7B scores 59.7 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Kimi K1.5 7B rank on MATH-500?

It ranks #469 of 600 models, in the 22nd percentile. The leader is GPT-5.2 Max at 96.7.

Is Kimi K1.5 7B good at MATH-500?

It is below the median for this benchmark, so test it carefully before relying on it for this capability. 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 Kimi K1.5 7B cost?

Kimi K1.5 7B costs $1.35 per million input tokens and $4.98 per million output tokens.

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