Coding · Meta AI

Llama 3.1 Chat on SWE-bench Verified: score, rank and what it means

Llama 3.1 Chat scores 62.0 on SWE-bench Verified, placing it #280 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
62.0
0–100 scale
Rank
#280
of 600
Percentile
53th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
19.7
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3.1 Chat 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 3.1 Chat sits at #280 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 19.7 points. The median model in this index scores 61.0, which puts Llama 3.1 Chat above the midpoint.

Meta AI models average 56.9 on this benchmark. Llama 3.1 Chat is 5.1 points above that provider line, so it is a stronger result than the average Meta AI model.

Among multimodal models, the average is 66.4. Llama 3.1 Chat is 4.4 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 62.0 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 $3.82 per million input tokens, Llama 3.1 Chat 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
MiniMax-Text Base MiniMax 128000 $3.66 74.9
Mistral Small 3 Instruct Mistral AI 32768 $3.97 75.1
Phi-4 Chat Microsoft 65536 $3.66 74.7
Firefunction Turbo Fireworks AI 32768 $8.44 70.7
Llama 3.1 Chat Meta AI 32768 $3.82 75.3
Mixtral Groq Groq 16384 $3.37 73.1
Yi-Lightning Base 01.AI 16384 $3.31 72.7

Other benchmarks for Llama 3.1 Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 79.9 186 69th
GPQA Diamond 72.4 211 65th
HumanEval 79.3 256 57th
MATH-500 78.9 239 60th
AIME 2025 67.7 300 50th
LiveCodeBench 68.5 216 64th
IFEval 81.9 280 53th
TruthfulQA 79.6 198 67th
HellaSwag 83.7 293 51th
MMMU 74.3 177 71th
Arena Elo 1392.5 239 60th

FAQ

What score does Llama 3.1 Chat get on SWE-bench Verified?

Llama 3.1 Chat scores 62.0 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Llama 3.1 Chat rank on SWE-bench Verified?

It ranks #280 of 600 models, in the 53rd percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3.1 Chat 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 3.1 Chat cost?

Llama 3.1 Chat costs $3.82 per million input tokens and $18.65 per million output tokens.

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