DeepSeek-V3.2 7B on LiveCodeBench: score, rank and what it means
DeepSeek-V3.2 7B scores 50.2 on LiveCodeBench, placing it #446 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 DeepSeek-V3.2 7B 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, DeepSeek-V3.2 7B sits at #446 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 34.9 points. The median model in this index scores 64.7, which puts DeepSeek-V3.2 7B below the midpoint.
DeepSeek models average 63.4 on this benchmark. DeepSeek-V3.2 7B is 13.2 points below that provider line, so it is a weaker result than the average DeepSeek model.
Among small open-weight models, the average is 41.2. DeepSeek-V3.2 7B is 9.0 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 50.2 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 $1.75 per million input tokens, DeepSeek-V3.2 7B 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 |
|---|---|---|---|---|
| Llama 3.1 Turbo 8B | Together AI | 32768 | $1.16 | 58.8 |
| Llama 3.3 8B | Meta AI | 8192 | $1.26 | 60.1 |
| Kimi K2 7B | Moonshot AI | 16384 | $1.72 | 62.7 |
| Qwen3 7B | Alibaba Cloud | 32768 | $1.37 | 59.8 |
| DeepSeek-V3.2 7B | DeepSeek | 4096 | $1.75 | 63.2 |
| MiniMax-M2 7B | MiniMax | 8192 | $1.44 | 60.8 |
| MiniMax-M2 8B | MiniMax | 32768 | $1.65 | 61.7 |
Other benchmarks for DeepSeek-V3.2 7B
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 68.2 | 414 | 31th | |
| GPQA Diamond | 60.2 | 403 | 33th | |
| SWE-bench Verified | 49.7 | 424 | 29th | |
| HumanEval | 67.1 | 420 | 30th | |
| MATH-500 | 65.8 | 415 | 31th | |
| AIME 2025 | 54.3 | 420 | 30th | |
| IFEval | 75.1 | 397 | 34th | |
| TruthfulQA | 67.6 | 420 | 30th | |
| HellaSwag | 75.8 | 428 | 29th | |
| MMMU | 61.3 | 417 | 31th | |
| Arena Elo | 1311.8 | 405 | 33th |
FAQ
What score does DeepSeek-V3.2 7B get on LiveCodeBench?
DeepSeek-V3.2 7B scores 50.2 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.
How does DeepSeek-V3.2 7B rank on LiveCodeBench?
It ranks #446 of 600 models, in the 26th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.
Is DeepSeek-V3.2 7B 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 DeepSeek-V3.2 7B cost?
DeepSeek-V3.2 7B costs $1.75 per million input tokens and $6.84 per million output tokens.