Coding · Hyperbolic

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

Llama 3 Hyperbolic Chat scores 61.8 on SWE-bench Verified, placing it #284 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
61.8
0–100 scale
Rank
#284
of 600
Percentile
53th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
19.9
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3 Hyperbolic 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 Hyperbolic Chat sits at #284 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 19.9 points. The median model in this index scores 61.0, which puts Llama 3 Hyperbolic Chat above the midpoint.

Hyperbolic models average 52.7 on this benchmark. Llama 3 Hyperbolic Chat is 9.1 points above that provider line, so it is a stronger result than the average Hyperbolic model.

Among multimodal models, the average is 66.4. Llama 3 Hyperbolic Chat is 4.6 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 61.8 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 $3.64 per million input tokens, Llama 3 Hyperbolic 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
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
Llama 3 Anyscale Chat Anyscale 65536 $3.67 74.6
Llama 3 Hyperbolic Chat Hyperbolic 200000 $3.64 75.1
Llama 3 Novita Novita AI 128000 $3.70 74.4
Nova Lite Pro Amazon 200000 $9.69 74.1

Other benchmarks for Llama 3 Hyperbolic Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 76.4 293 51th
GPQA Diamond 72.7 204 66th
HumanEval 79.8 239 60th
MATH-500 79.2 228 62th
AIME 2025 73.4 199 67th
LiveCodeBench 69.0 207 66th
IFEval 81.2 296 51th
TruthfulQA 77.7 263 56th
HellaSwag 84.8 256 57th
MMMU 70.5 281 53th
Arena Elo 1375.5 291 52th

FAQ

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

Llama 3 Hyperbolic Chat scores 61.8 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 Chat rank on SWE-bench Verified?

It ranks #284 of 600 models, in the 53rd percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3 Hyperbolic 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 Hyperbolic Chat cost?

Llama 3 Hyperbolic Chat costs $3.64 per million input tokens and $16.55 per million output tokens.

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