Coding · Google DeepMind

Gemini 3 Flash Turbo on SWE-bench Verified: score, rank and what it means

Gemini 3 Flash Turbo scores 72.5 on SWE-bench Verified, placing it #68 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
72.5
0–100 scale
Rank
#68
of 600
Percentile
89th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
9.2
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Gemini 3 Flash Turbo highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 81.7
Claude Opus 4.5 Pro 81.1
GPT-5.2 Max 80.5
Claude Sonnet 4.5 Max 80.2
GPT-5.2 80.2
GPT-5.1 Pro 80.1
o4 Max 79.8
Claude Sonnet 4.5 Reasoning 79.6
Claude Opus 4.5 Max 79.5
Claude Opus 4.5 Reasoning 79.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
3 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
4 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2
5 GPT-5.2 OpenAI 2000000 $19.71 88.0
6 GPT-5.1 Pro OpenAI 200000 $19.77 87.8
7 o4 Max OpenAI 2000000 $19.51 86.2
8 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
9 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
10 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2

What this score means

On SWE-bench Verified, Gemini 3 Flash Turbo sits at #68 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 9.2 points. The median model in this index scores 61.0, which puts Gemini 3 Flash Turbo above the midpoint.

Google DeepMind models average 66.4 on this benchmark. Gemini 3 Flash Turbo is 6.1 points above that provider line, so it is a stronger result than the average Google DeepMind model.

Among frontier models, the average is 75.6. Gemini 3 Flash Turbo is 3.1 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 72.5 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 $17.07 per million input tokens, Gemini 3 Flash Turbo is above the median input price of $4.00 across the 600 models scored on SWE-bench Verified. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
o3 OpenAI 400000 $16.80 84.5
Grok 3 xAI 128000 $15.84 82.8
Gemini 3 Flash Google DeepMind 128000 $15.67 83.6
Gemini 3 Flash Reasoning Google DeepMind 128000 $17.05 83.7
Gemini 3 Flash Turbo Google DeepMind 200000 $17.07 84.1
DeepSeek-R1 Instruct DeepSeek 1000000 $5.90 83.1
GPT-4.1 Vision OpenAI 1000000 $14.88 82.0

Other benchmarks for Gemini 3 Flash Turbo

BenchmarkScoreRankPercentileRelative
MMLU-Pro 87.9 18 97th
GPQA Diamond 77.5 96 84th
HumanEval 90.6 37 94th
MATH-500 87.6 70 88th
AIME 2025 81.3 69 89th
LiveCodeBench 78.5 60 90th
IFEval 88.9 85 86th
TruthfulQA 87.4 29 95th
HellaSwag 89.5 99 84th
MMMU 83.0 23 96th
Arena Elo 1473.4 27 96th

FAQ

What score does Gemini 3 Flash Turbo get on SWE-bench Verified?

Gemini 3 Flash Turbo scores 72.5 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Gemini 3 Flash Turbo rank on SWE-bench Verified?

It ranks #68 of 600 models, in the 89th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Gemini 3 Flash Turbo good at SWE-bench Verified?

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 SWE-bench Verified score calculated?

SWE-bench Verified is a coding 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 Turbo cost?

Gemini 3 Flash Turbo costs $17.07 per million input tokens and $69.05 per million output tokens.

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