DeepSeek-Coder Base on SWE-bench Verified: score, rank and what it means
DeepSeek-Coder Base scores 59.3 on SWE-bench Verified, placing it #337 of 600 models in the index. The benchmark average is 57.5 and the leader is GPT-5.2 Pro at 81.7.
Score distribution
The chart shows the top 10 models on SWE-bench Verified, with DeepSeek-Coder Base highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg 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, DeepSeek-Coder Base sits at #337 of 600. The leader, GPT-5.2 Pro, scores 81.7, so the gap to the top is 22.4 points. The median model in this index scores 61.0, which puts DeepSeek-Coder Base below the midpoint.
DeepSeek models average 60.0 on this benchmark. DeepSeek-Coder Base is 0.7 points below that provider line, so it is a weaker result than the average DeepSeek model.
Among coding models, the average is 56.2. DeepSeek-Coder Base is 3.1 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 59.3 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 $3.53 per million input tokens, DeepSeek-Coder 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.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Doubao 1.5 Pro Mini | ByteDance | 32768 | $8.30 | 72.3 |
| Mixtral Chat | Mistral AI | 65536 | $3.30 | 72.6 |
| Reka Core Mini | Reka AI | 8192 | $7.48 | 70.1 |
| Mistral Small 3 Base | Mistral AI | 16384 | $3.19 | 72.7 |
| DeepSeek-Coder Base | DeepSeek | 32768 | $3.53 | 74.1 |
| Yi-Large Chat | 01.AI | 65536 | $3.49 | 73.5 |
| Qwen3-Max Nano | Alibaba Cloud | 65536 | $8.31 | 71.7 |
Other benchmarks for DeepSeek-Coder Base
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 78.9 | 211 | 65th | |
| GPQA Diamond | 70.8 | 240 | 60th | |
| HumanEval | 78.4 | 283 | 53th | |
| MATH-500 | 74.2 | 341 | 43th | |
| AIME 2025 | 71.7 | 223 | 63th | |
| LiveCodeBench | 67.3 | 242 | 60th | |
| IFEval | 81.6 | 282 | 53th | |
| TruthfulQA | 79.0 | 215 | 64th | |
| HellaSwag | 84.8 | 255 | 58th | |
| MMMU | 69.0 | 325 | 46th | |
| Arena Elo | 1388.5 | 255 | 58th |
FAQ
What score does DeepSeek-Coder Base get on SWE-bench Verified?
DeepSeek-Coder Base scores 59.3 on SWE-bench Verified in this index. The benchmark average is 57.5 and the top score is 81.7.
How does DeepSeek-Coder Base rank on SWE-bench Verified?
It ranks #337 of 600 models, in the 44th percentile. The leader is GPT-5.2 Pro at 81.7.
Is DeepSeek-Coder 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 DeepSeek-Coder Base cost?
DeepSeek-Coder Base costs $3.53 per million input tokens and $12.53 per million output tokens.