Coding · Hyperbolic

Llama 3 Hyperbolic 1.5B on SWE-bench Verified: score, rank and what it means

Llama 3 Hyperbolic 1.5B scores 33.2 on SWE-bench Verified, placing it #569 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
33.2
0–100 scale
Rank
#569
of 600
Percentile
5th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
48.5
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3 Hyperbolic 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 Hyperbolic 1.5B sits at #569 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 48.5 points. The median model in this index scores 61.0, which puts Llama 3 Hyperbolic 1.5B below the midpoint.

Hyperbolic models average 52.7 on this benchmark. Llama 3 Hyperbolic 1.5B is 19.5 points below that provider line, so it is a weaker result than the average Hyperbolic model.

Among small open-weight models, the average is 39.0. Llama 3 Hyperbolic 1.5B is 5.8 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 33.2 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 $0.45 per million input tokens, Llama 3 Hyperbolic 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
Codestral 1.5B Mistral AI 8192 $0.52 49.9
Llama 3 Modal 1.5B Modal 4096 $0.42 49.7
Molmo 8B Allen Institute for AI 32768 $0.44 49.9
Yi-Large 1.5B 01.AI 8192 $0.42 46.8
Llama 3 Hyperbolic 1.5B Hyperbolic 32768 $0.45 48.4
Phi-3.5 7B Microsoft 4096 $0.50 50.5
Embed 4 Nano Cohere 8192 $1.48 49.7

Other benchmarks for Llama 3 Hyperbolic 1.5B

BenchmarkScoreRankPercentileRelative
MMLU-Pro 53.5 579 4th
GPQA Diamond 45.3 564 6th
HumanEval 49.2 579 4th
MATH-500 49.6 559 7th
AIME 2025 34.5 572 5th
LiveCodeBench 38.2 548 9th
IFEval 62.7 547 9th
TruthfulQA 56.0 567 6th
HellaSwag 65.2 581 3th
MMMU 45.5 582 3th
Arena Elo 1193.7 562 6th

FAQ

What score does Llama 3 Hyperbolic 1.5B get on SWE-bench Verified?

Llama 3 Hyperbolic 1.5B scores 33.2 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Llama 3 Hyperbolic 1.5B rank on SWE-bench Verified?

It ranks #569 of 600 models, in the 5th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3 Hyperbolic 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 Hyperbolic 1.5B cost?

Llama 3 Hyperbolic 1.5B costs $0.45 per million input tokens and $1.70 per million output tokens.

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