Knowledge · Reka AI

Reka Core Nano on MMLU-Pro: score, rank and what it means

Reka Core Nano scores 68.5 on MMLU-Pro, placing it #412 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
68.5
0–100 scale
Rank
#412
of 600
Percentile
31th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
23.1
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Reka Core Nano highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, Reka Core Nano sits at #412 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 23.1 points. The median model in this index scores 76.1, which puts Reka Core Nano below the midpoint.

Reka AI models average 76.2 on this benchmark. Reka Core Nano is 7.7 points below that provider line, so it is a weaker result than the average Reka AI model.

Among mid-range models, the average is 71.5. Reka Core Nano is 3.0 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 68.5 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 $5.45 per million input tokens, Reka Core Nano is above the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Zephyr Hugging Face 8192 $1.61 62.8
SmolLM3 Chat Hugging Face 16384 $1.61 62.1
Sonar Pro Nano Perplexity 32768 $5.50 66.1
Molmo Base Allen Institute for AI 4096 $2.02 65.0
Reka Core Nano Reka AI 16384 $5.45 65.1
Zephyr Chat Hugging Face 4096 $1.49 61.3
DeepSeek-V3.2 7B DeepSeek 4096 $1.75 63.2

Other benchmarks for Reka Core Nano

BenchmarkScoreRankPercentileRelative
GPQA Diamond 59.9 407 32th
SWE-bench Verified 50.2 421 30th
HumanEval 68.1 413 31th
MATH-500 69.3 385 36th
AIME 2025 57.3 404 33th
LiveCodeBench 58.7 381 37th
IFEval 74.8 401 33th
TruthfulQA 67.8 416 31th
HellaSwag 79.5 390 35th
MMMU 61.8 411 32th
Arena Elo 1310.8 407 32th

FAQ

What score does Reka Core Nano get on MMLU-Pro?

Reka Core Nano scores 68.5 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Reka Core Nano rank on MMLU-Pro?

It ranks #412 of 600 models, in the 31st percentile. The leader is GPT-5.2 Pro at 91.6.

Is Reka Core Nano good at MMLU-Pro?

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 MMLU-Pro score calculated?

MMLU-Pro is a knowledge 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 Reka Core Nano cost?

Reka Core Nano costs $5.45 per million input tokens and $25.25 per million output tokens.

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