Coding · Microsoft

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

Phi-3.5 scores 60.9 on SWE-bench Verified, placing it #308 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
60.9
0–100 scale
Rank
#308
of 600
Percentile
49th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
20.8
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Phi-3.5 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 sits at #308 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 20.8 points. The median model in this index scores 61.0, which puts Phi-3.5 below the midpoint.

Microsoft models average 49.9 on this benchmark. Phi-3.5 is 11.0 points above that provider line, so it is a stronger result than the average Microsoft model.

Among open-weight models, the average is 55.5. Phi-3.5 is 5.4 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 60.9 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 $2.81 per million input tokens, Phi-3.5 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
GPT-4.1 Mini OpenAI 128000 $10.43 74.3
Grok 4 Nano xAI 16384 $8.97 72.7
Nova Lite Amazon 65536 $9.20 73.4
OLMo 2 Chat Allen Institute for AI 16384 $2.94 70.6
Phi-3.5 Microsoft 16384 $2.81 70.9
Mixtral Turbo Instruct Together AI 32768 $3.16 71.9
Command R+ Base Cohere 65536 $3.24 71.6

Other benchmarks for Phi-3.5

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.1 304 49th
GPQA Diamond 67.0 331 45th
HumanEval 76.2 327 46th
MATH-500 75.4 316 47th
AIME 2025 60.6 383 36th
LiveCodeBench 59.8 371 38th
IFEval 77.1 377 37th
TruthfulQA 74.8 336 44th
HellaSwag 82.9 323 46th
MMMU 68.8 328 45th
Arena Elo 1356.7 350 42th

FAQ

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

Phi-3.5 scores 60.9 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 rank on SWE-bench Verified?

It ranks #308 of 600 models, in the 49th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Phi-3.5 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 cost?

Phi-3.5 costs $2.81 per million input tokens and $14.99 per million output tokens.

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