Coding · Modal

Llama 3 Modal Instruct on SWE-bench Verified: score, rank and what it means

Llama 3 Modal Instruct scores 63.6 on SWE-bench Verified, placing it #244 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
63.6
0–100 scale
Rank
#244
of 600
Percentile
59th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
18.1
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3 Modal 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, Llama 3 Modal Instruct sits at #244 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 18.1 points. The median model in this index scores 61.0, which puts Llama 3 Modal Instruct above the midpoint.

Modal models average 52.4 on this benchmark. Llama 3 Modal Instruct is 11.2 points above that provider line, so it is a stronger result than the average Modal model.

Among multimodal models, the average is 66.4. Llama 3 Modal Instruct is 2.8 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 63.6 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 $3.84 per million input tokens, Llama 3 Modal Instruct 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
Nemotron 4 NVIDIA 65536 $4.18 76.6
Yi-Large 01.AI 32768 $3.40 73.5
Gemini 2.5 Pro Mini Google DeepMind 128000 $10.07 74.2
Llama 3 Modal Modal 32768 $3.82 74.9
Llama 3 Modal Instruct Modal 128000 $3.84 75.2
Llama 3.1 Turbo Together AI 200000 $4.17 76.8
Hunyuan-Turbo Tencent 128000 $10.55 75.8

Other benchmarks for Llama 3 Modal Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.6 166 72th
GPQA Diamond 71.9 221 63th
HumanEval 79.5 248 59th
MATH-500 81.3 183 70th
AIME 2025 68.7 285 53th
LiveCodeBench 67.0 251 58th
IFEval 83.7 219 64th
TruthfulQA 78.6 233 61th
HellaSwag 83.5 305 49th
MMMU 69.2 323 46th
Arena Elo 1407.2 189 69th

FAQ

What score does Llama 3 Modal Instruct get on SWE-bench Verified?

Llama 3 Modal Instruct scores 63.6 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Llama 3 Modal Instruct rank on SWE-bench Verified?

It ranks #244 of 600 models, in the 59th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3 Modal 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 Llama 3 Modal Instruct cost?

Llama 3 Modal Instruct costs $3.84 per million input tokens and $18.84 per million output tokens.

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