Coding · Anthropic

Claude Opus 4.5 Nano on SWE-bench Verified: score, rank and what it means

Claude Opus 4.5 Nano scores 62.2 on SWE-bench Verified, placing it #273 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
62.2
0–100 scale
Rank
#273
of 600
Percentile
55th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
19.5
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Claude Opus 4.5 Nano 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, Claude Opus 4.5 Nano sits at #273 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 19.5 points. The median model in this index scores 61.0, which puts Claude Opus 4.5 Nano above the midpoint.

Anthropic models average 71.0 on this benchmark. Claude Opus 4.5 Nano is 8.8 points below that provider line, so it is a weaker result than the average Anthropic model.

Among multimodal models, the average is 66.4. Claude Opus 4.5 Nano is 4.2 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 62.2 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 $9.38 per million input tokens, Claude Opus 4.5 Nano 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
Llama 3 Replicate Instruct Replicate 128000 $3.71 75.1
Nova Lite Reasoning Amazon 32768 $9.92 74.3
Nemotron 4 Chat NVIDIA 65536 $3.99 75.0
o4 Nano OpenAI 65536 $8.61 72.0
Claude Opus 4.5 Nano Anthropic 32768 $9.38 73.3
Llama 3 Replicate Base Replicate 65536 $3.15 72.1
Llama 3.1 Turbo Base Together AI 16384 $3.30 73.0

Other benchmarks for Claude Opus 4.5 Nano

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.1 300 50th
GPQA Diamond 69.4 281 53th
HumanEval 75.5 334 44th
MATH-500 77.2 272 55th
AIME 2025 71.4 229 62th
LiveCodeBench 65.0 293 51th
IFEval 79.7 337 44th
TruthfulQA 78.1 252 58th
HellaSwag 82.1 344 43th
MMMU 69.2 322 46th
Arena Elo 1373.3 305 49th

FAQ

What score does Claude Opus 4.5 Nano get on SWE-bench Verified?

Claude Opus 4.5 Nano scores 62.2 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Claude Opus 4.5 Nano rank on SWE-bench Verified?

It ranks #273 of 600 models, in the 55th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Claude Opus 4.5 Nano 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 Claude Opus 4.5 Nano cost?

Claude Opus 4.5 Nano costs $9.38 per million input tokens and $36.06 per million output tokens.

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