DeepSeek-V3.2 1.5B on Arena Elo: score, rank and what it means
DeepSeek-V3.2 1.5B scores 1260.0 on Arena Elo, placing it #475 of 600 models in the index. The benchmark average is 1349.9 and the leader is Claude Opus 4.5 Max at 1501.9.
Score distribution
The chart shows the top 10 models on Arena Elo, with DeepSeek-V3.2 1.5B highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 2 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 3 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 4 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 5 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 6 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 7 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 8 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 9 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 10 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
What this score means
On Arena Elo, DeepSeek-V3.2 1.5B sits at #475 of 600. The leader, Claude Opus 4.5 Max, scores 1501.9, so the gap to the top is 241.9 points. The median model in this index scores 1373.8, which puts DeepSeek-V3.2 1.5B below the midpoint.
DeepSeek models average 1363.3 on this benchmark. DeepSeek-V3.2 1.5B is 103.3 points below that provider line, so it is a weaker result than the average DeepSeek model.
Among small open-weight models, the average is 1224.7. DeepSeek-V3.2 1.5B is 35.3 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 1260.0 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 $1.02 per million input tokens, DeepSeek-V3.2 1.5B is below the median input price of $4.00 across the 600 models scored on Arena Elo. 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 |
|---|---|---|---|---|
| Llama 3.1 Cerebras 7B | Cerebras | 32768 | $0.95 | 56.5 |
| Yi-Lightning 7B | 01.AI | 32768 | $1.00 | 55.9 |
| DeepSeek-Coder 7B | DeepSeek | 32768 | $1.14 | 57.3 |
| DeepSeek-Coder 8B | DeepSeek | 32768 | $1.26 | 58.7 |
| DeepSeek-V3.2 1.5B | DeepSeek | 4096 | $1.02 | 56.9 |
| Pythia | EleutherAI | 32768 | $1.29 | 59.8 |
| GLM-4.5 7B | Zhipu AI | 8192 | $1.32 | 60.2 |
Other benchmarks for DeepSeek-V3.2 1.5B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 63.7 | 476 | 21th | |
| GPQA Diamond | 53.6 | 486 | 19th | |
| SWE-bench Verified | 41.4 | 507 | 16th | |
| HumanEval | 61.0 | 487 | 19th | |
| MATH-500 | 55.4 | 517 | 14th | |
| AIME 2025 | 45.4 | 494 | 18th | |
| LiveCodeBench | 45.5 | 493 | 18th | |
| IFEval | 70.1 | 460 | 23th | |
| TruthfulQA | 61.3 | 516 | 14th | |
| HellaSwag | 72.0 | 496 | 17th | |
| MMMU | 56.3 | 483 | 20th |
FAQ
What score does DeepSeek-V3.2 1.5B get on Arena Elo?
DeepSeek-V3.2 1.5B scores 1260.0 on Arena Elo in this index. The benchmark average is 1349.9 and the top score is 1501.9.
How does DeepSeek-V3.2 1.5B rank on Arena Elo?
It ranks #475 of 600 models, in the 21st percentile. The leader is Claude Opus 4.5 Max at 1501.9.
Is DeepSeek-V3.2 1.5B good at Arena Elo?
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 Arena Elo score calculated?
Arena Elo is a preference 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 DeepSeek-V3.2 1.5B cost?
DeepSeek-V3.2 1.5B costs $1.02 per million input tokens and $5.25 per million output tokens.