Coding · 01.AI

Yi-Lightning Instruct on SWE-bench Verified: score, rank and what it means

Yi-Lightning Instruct scores 65.3 on SWE-bench Verified, placing it #209 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
65.3
0–100 scale
Rank
#209
of 600
Percentile
65th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
16.4
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Yi-Lightning Instruct 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, Yi-Lightning Instruct sits at #209 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 16.4 points. The median model in this index scores 61.0, which puts Yi-Lightning Instruct above the midpoint.

01.AI models average 52.2 on this benchmark. Yi-Lightning Instruct is 13.1 points above that provider line, so it is a stronger result than the average 01.AI model.

Among multimodal models, the average is 66.4. Yi-Lightning Instruct is 1.1 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 65.3 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 $4.16 per million input tokens, Yi-Lightning Instruct 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 Chat Replicate 200000 $3.74 74.7
Reka Flash Reka AI 128000 $9.95 74.1
Llama 3 Replicate Replicate 200000 $3.75 75.7
Llama 4 Scout Chat Meta AI 32768 $4.66 78.0
Yi-Lightning Instruct 01.AI 128000 $4.16 76.5
Gemma 3 Chat Google DeepMind 65536 $3.74 74.5
QwQ Base Alibaba Cloud 65536 $3.68 74.1

Other benchmarks for Yi-Lightning Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 78.4 231 62th
GPQA Diamond 73.0 200 67th
HumanEval 79.1 264 56th
MATH-500 78.2 255 58th
AIME 2025 71.7 225 63th
LiveCodeBench 70.3 186 69th
IFEval 85.6 171 72th
TruthfulQA 81.5 143 76th
HellaSwag 85.9 221 63th
MMMU 72.1 241 60th
Arena Elo 1415.8 166 72th

FAQ

What score does Yi-Lightning Instruct get on SWE-bench Verified?

Yi-Lightning Instruct scores 65.3 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Yi-Lightning Instruct rank on SWE-bench Verified?

It ranks #209 of 600 models, in the 65th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Yi-Lightning Instruct 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 Yi-Lightning Instruct cost?

Yi-Lightning Instruct costs $4.16 per million input tokens and $22.74 per million output tokens.

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