Coding · Novita AI

Llama 3 Novita Base on SWE-bench Verified: score, rank and what it means

Llama 3 Novita Base scores 56.9 on SWE-bench Verified, placing it #368 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.

Score
56.9
0–100 scale
Rank
#368
of 600
Percentile
39th
within the index
Benchmark avg
57.5
all models
Leader
81.7
GPT-5.2 Pro
Gap to leader
24.8
points

Score distribution

The chart shows the top 10 models on SWE-bench Verified, with Llama 3 Novita Base 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 Novita Base sits at #368 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 24.8 points. The median model in this index scores 61.0, which puts Llama 3 Novita Base below the midpoint.

Novita AI models average 51.5 on this benchmark. Llama 3 Novita Base is 5.4 points above that provider line, so it is a stronger result than the average Novita AI model.

Among open-weight models, the average is 55.5. Llama 3 Novita Base is 1.4 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 56.9 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 $3.06 per million input tokens, Llama 3 Novita Base 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
Molmo Instruct Allen Institute for AI 32768 $2.53 68.3
Auto Turbo OpenRouter 65536 $7.96 70.9
Fusion Pro OpenRouter 65536 $8.69 72.2
Claude Haiku 4.5 Nano Anthropic 8192 $6.47 68.7
Llama 3 Novita Base Novita AI 8192 $3.06 70.3
Sonar Mini Perplexity 65536 $6.52 67.9
Yi-Large Base 01.AI 16384 $2.78 70.3

Other benchmarks for Llama 3 Novita Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 74.4 344 43th
GPQA Diamond 67.8 314 48th
HumanEval 73.3 368 39th
MATH-500 71.9 367 39th
AIME 2025 62.5 371 38th
LiveCodeBench 60.4 363 40th
IFEval 79.2 351 42th
TruthfulQA 76.5 293 51th
HellaSwag 82.3 341 43th
MMMU 67.7 350 42th
Arena Elo 1360.8 342 43th

FAQ

What score does Llama 3 Novita Base get on SWE-bench Verified?

Llama 3 Novita Base scores 56.9 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.

How does Llama 3 Novita Base rank on SWE-bench Verified?

It ranks #368 of 600 models, in the 39th percentile. The leader is GPT-5.2 Pro at 81.7.

Is Llama 3 Novita Base 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 Novita Base cost?

Llama 3 Novita Base costs $3.06 per million input tokens and $13.44 per million output tokens.

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