GPT-NeoX Instruct on LiveCodeBench: score, rank and what it means
GPT-NeoX Instruct scores 53.9 on LiveCodeBench, placing it #412 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.
Score distribution
The chart shows the top 10 models on LiveCodeBench, with GPT-NeoX Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 2 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 3 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 4 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 5 | Grok 4 Turbo | xAI | 1000000 | $19.21 | 86.7 |
| 6 | GPT-5.1 Turbo | OpenAI | 2000000 | $19.19 | 86.1 |
| 7 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 8 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 9 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 10 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
What this score means
On LiveCodeBench, GPT-NeoX Instruct sits at #412 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 31.2 points. The median model in this index scores 64.7, which puts GPT-NeoX Instruct below the midpoint.
EleutherAI models average 38.6 on this benchmark. GPT-NeoX Instruct is 15.3 points above that provider line, so it is a stronger result than the average EleutherAI model.
Among open-weight models, the average is 59.1. GPT-NeoX Instruct is 5.2 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 53.9 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 $1.52 per million input tokens, GPT-NeoX Instruct is below the median input price of $4.00 across the 600 models scored on LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| SmolLM3 Chat | Hugging Face | 16384 | $1.61 | 62.1 |
| Command A Nano | Cohere | 8192 | $5.90 | 66.3 |
| Nemotron Nano Chat | NVIDIA | 32768 | $2.21 | 66.2 |
| Embed 4 | Cohere | 32768 | $4.79 | 63.8 |
| GPT-NeoX Instruct | EleutherAI | 16384 | $1.52 | 62.6 |
| Nemotron Nano Base | NVIDIA | 8192 | $1.85 | 65.0 |
| GLM-4.5 8B | Zhipu AI | 32768 | $1.46 | 61.3 |
Other benchmarks for GPT-NeoX Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 67.9 | 420 | 30th | |
| GPQA Diamond | 58.5 | 420 | 30th | |
| SWE-bench Verified | 50.5 | 418 | 30th | |
| HumanEval | 63.7 | 452 | 25th | |
| MATH-500 | 64.9 | 419 | 30th | |
| AIME 2025 | 54.1 | 423 | 30th | |
| IFEval | 71.2 | 439 | 27th | |
| TruthfulQA | 66.0 | 444 | 26th | |
| HellaSwag | 75.4 | 435 | 28th | |
| MMMU | 62.5 | 404 | 33th | |
| Arena Elo | 1292.6 | 429 | 29th |
FAQ
What score does GPT-NeoX Instruct get on LiveCodeBench?
GPT-NeoX Instruct scores 53.9 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.
How does GPT-NeoX Instruct rank on LiveCodeBench?
It ranks #412 of 600 models, in the 31st percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.
Is GPT-NeoX Instruct good at LiveCodeBench?
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 LiveCodeBench score calculated?
LiveCodeBench 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 GPT-NeoX Instruct cost?
GPT-NeoX Instruct costs $1.52 per million input tokens and $5.60 per million output tokens.