Coding · Microsoft

Phi-3.5 7B on SWE-bench Verified: score, rank and what it means

Phi-3.5 7B scores 33.2 on SWE-bench Verified, placing it #570 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
33.2
0–100 scale
Rank
#570
of 600
Percentile
5th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
48.5
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Phi-3.5 7B 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, Phi-3.5 7B sits at #570 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 48.5 points. The median model in this index scores 61.0, which puts Phi-3.5 7B below the midpoint.

Microsoft models average 49.9 on this benchmark. Phi-3.5 7B is 16.7 points below that provider line, so it is a weaker result than the average Microsoft model.

Among small open-weight models, the average is 39.0. Phi-3.5 7B is 5.8 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 33.2 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 $0.50 per million input tokens, Phi-3.5 7B is below 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
Llama 3 Modal 1.5B Modal 4096 $0.42 49.7
Molmo 8B Allen Institute for AI 32768 $0.44 49.9
Yi-Large 1.5B 01.AI 8192 $0.42 46.8
Llama 3 Hyperbolic 1.5B Hyperbolic 32768 $0.45 48.4
Phi-3.5 7B Microsoft 4096 $0.50 50.5
Embed 4 Nano Cohere 8192 $1.48 49.7
DBRX 1.5B Databricks 16384 $0.32 47.0

Other benchmarks for Phi-3.5 7B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 59.3 534 11th
GPQA Diamond 47.6 542 10th
HumanEval 54.5 547 9th
MATH-500 51.8 544 9th
AIME 2025 36.8 557 7th
LiveCodeBench 35.2 569 5th
IFEval 61.5 561 7th
TruthfulQA 55.0 574 4th
HellaSwag 69.1 546 9th
MMMU 51.1 543 10th
Arena Elo 1190.4 565 6th

FAQ

What score does Phi-3.5 7B get on SWE-bench Verified?

Phi-3.5 7B scores 33.2 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Phi-3.5 7B rank on SWE-bench Verified?

It ranks #570 of 600 models, in the 5th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Phi-3.5 7B good at SWE-bench Verified?

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 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 Phi-3.5 7B cost?

Phi-3.5 7B costs $0.50 per million input tokens and $2.68 per million output tokens.

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