Instruction following · Google DeepMind

Gemini 3 Flash Mini on IFEval: score, rank and what it means

Gemini 3 Flash Mini scores 83.6 on IFEval, placing it #222 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
83.6
0–100 scale
Rank
#222
of 600
Percentile
63th
within the index
Benchmark avg
78.6
all models
Leader
96.1
Claude Opus 4.5 Pro
Gap to leader
12.5
points

Score distribution

The chart shows the top 10 models on IFEval, with Gemini 3 Flash Mini 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, Gemini 3 Flash Mini sits at #222 of 600. The leader, Claude Opus 4.5 Pro, scores 96.1, so the gap to the top is 12.5 points. The median model in this index scores 81.2, which puts Gemini 3 Flash Mini above the midpoint.

Google DeepMind models average 85.7 on this benchmark. Gemini 3 Flash Mini is 2.1 points below that provider line, so it is a weaker result than the average Google DeepMind model.

Among multimodal models, the average is 85.2. Gemini 3 Flash Mini is 1.6 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 83.6 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 $10.71 per million input tokens, Gemini 3 Flash Mini 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
Llama 3 Hyperbolic Hyperbolic 65536 $3.86 74.5
Llama 3 Modal Instruct Modal 128000 $3.84 75.2
Llama 3 Replicate Instruct Replicate 128000 $3.71 75.1
Sonar Pro Perplexity 65536 $11.25 77.1
Gemini 3 Flash Mini Google DeepMind 200000 $10.71 76.0
Llama 3 Hyperbolic Instruct Hyperbolic 200000 $3.79 76.2
Llama 3 Replicate Replicate 200000 $3.75 75.7

Other benchmarks for Gemini 3 Flash Mini

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.8 216 64th
GPQA Diamond 73.8 174 71th
SWE-bench Verified 61.4 292 51th
HumanEval 80.1 229 62th
MATH-500 78.9 237 61th
AIME 2025 74.3 169 72th
LiveCodeBench 69.1 206 66th
TruthfulQA 79.8 186 69th
HellaSwag 84.1 275 54th
MMMU 72.6 229 62th
Arena Elo 1406.3 191 68th

FAQ

What score does Gemini 3 Flash Mini get on IFEval?

Gemini 3 Flash Mini scores 83.6 on IFEval in this index. The benchmark average is 78.6 and the top score is 96.1.

How does Gemini 3 Flash Mini rank on IFEval?

It ranks #222 of 600 models, in the 63rd percentile. The leader is Claude Opus 4.5 Pro at 96.1.

Is Gemini 3 Flash Mini 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 Gemini 3 Flash Mini cost?

Gemini 3 Flash Mini costs $10.71 per million input tokens and $48.11 per million output tokens.

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