Coding · Cerebras

Llama 3.1 Cerebras 1.5B on SWE-bench Verified: score, rank and what it means

Llama 3.1 Cerebras 1.5B scores 36.6 on SWE-bench Verified, placing it #544 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
36.6
0–100 scale
Rank
#544
of 600
Percentile
9th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
45.1
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3.1 Cerebras 1.5B 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 1.5B sits at #544 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 45.1 points. The median model in this index scores 61.0, which puts Llama 3.1 Cerebras 1.5B below the midpoint.

Cerebras models average 53.4 on this benchmark. Llama 3.1 Cerebras 1.5B is 16.8 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 1.5B is 2.4 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 36.6 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 $0.48 per million input tokens, Llama 3.1 Cerebras 1.5B 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
DBRX 8B Databricks 4096 $0.82 54.1
Qwen3-Coder 1.5B Alibaba Cloud 32768 $0.76 52.8
Qwen3 1.5B Alibaba Cloud 4096 $0.84 53.5
Arctic 8B Snowflake 8192 $0.65 52.4
Llama 3.1 Cerebras 1.5B Cerebras 4096 $0.48 50.2
Mixtral Groq 7B Groq 4096 $0.70 53.4
MiniMax-Text 1.5B MiniMax 32768 $0.58 51.5

Other benchmarks for Llama 3.1 Cerebras 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 57.5 554 8th
GPQA Diamond 46.0 558 7th
HumanEval 52.4 557 7th
MATH-500 48.7 568 5th
AIME 2025 38.1 549 9th
LiveCodeBench 39.6 540 10th
IFEval 61.5 560 7th
TruthfulQA 55.6 571 5th
HellaSwag 68.6 552 8th
MMMU 48.0 564 6th
Arena Elo 1198.1 557 7th

FAQ

What score does Llama 3.1 Cerebras 1.5B get on SWE-bench Verified?

Llama 3.1 Cerebras 1.5B scores 36.6 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 1.5B rank on SWE-bench Verified?

It ranks #544 of 600 models, in the 9th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3.1 Cerebras 1.5B 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 1.5B cost?

Llama 3.1 Cerebras 1.5B costs $0.48 per million input tokens and $2.46 per million output tokens.

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