Coding · Cerebras

Llama 3.1 Cerebras 8B on SWE-bench Verified: score, rank and what it means

Llama 3.1 Cerebras 8B scores 41.6 on SWE-bench Verified, placing it #504 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
41.6
0–100 scale
Rank
#504
of 600
Percentile
16th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
40.1
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3.1 Cerebras 8B 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 Cerebras 8B sits at #504 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 40.1 points. The median model in this index scores 61.0, which puts Llama 3.1 Cerebras 8B below the midpoint.

Cerebras models average 53.4 on this benchmark. Llama 3.1 Cerebras 8B is 11.8 points below that provider line, so it is a weaker result than the average Cerebras model.

Among small open-weight models, the average is 39.0. Llama 3.1 Cerebras 8B is 2.6 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 41.6 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 $1.02 per million input tokens, Llama 3.1 Cerebras 8B 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
Llama 3 Anyscale 8B Anyscale 32768 $0.97 57.3
Fusion Nano OpenRouter 8192 $2.96 56.8
Gemma 3 8B Google DeepMind 16384 $0.96 56.5
Llama 3 Replicate 7B Replicate 32768 $0.91 56.0
Llama 3.1 Cerebras 8B Cerebras 32768 $1.02 58.2
Yi-Large 7B 01.AI 4096 $0.83 55.0
Llama 3.1 Cerebras 7B Cerebras 32768 $0.95 56.5

Other benchmarks for Llama 3.1 Cerebras 8B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 64.4 462 23th
GPQA Diamond 54.6 470 22th
HumanEval 60.5 495 18th
MATH-500 60.3 457 24th
AIME 2025 47.2 475 21th
LiveCodeBench 47.0 475 21th
IFEval 68.9 475 21th
TruthfulQA 64.1 468 22th
HellaSwag 73.4 472 21th
MMMU 57.7 461 23th
Arena Elo 1254.4 483 20th

FAQ

What score does Llama 3.1 Cerebras 8B get on SWE-bench Verified?

Llama 3.1 Cerebras 8B scores 41.6 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Llama 3.1 Cerebras 8B rank on SWE-bench Verified?

It ranks #504 of 600 models, in the 16th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3.1 Cerebras 8B 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 Llama 3.1 Cerebras 8B cost?

Llama 3.1 Cerebras 8B costs $1.02 per million input tokens and $5.02 per million output tokens.

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