DeepSeek-Coder-V2-Lite-Instruct: benchmark scores, price and ranking
DeepSeek-Coder-V2-Lite-Instruct from deepseek-ai ranks #546 of 743 LLMs on the LLMs Tiger aggregate benchmark score (46.0), based on 1 benchmark results captured up to 2026-10-13.
- Organisation
- deepseek-ai
- Type
- Open-weight / local model
- Open weights
- Yes
- Aggregate rank
- #546 of 743 (score 46.0)
Across the 1 benchmark where at least ten models are scored, DeepSeek-Coder-V2-Lite-Instruct is in the top 10% on 0 and in the top half on 0.
DeepSeek-Coder-V2-Lite-Instruct benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| MMLU-Pro | 41.6% | #95 of 136 | HuggingFace MMLU-Pro leaderboard | 2026-10-13 |
About DeepSeek-Coder-V2-Lite-Instruct
DeepSeek-Coder-V2-Lite-Instruct is an open-weight model that can run on your own hardware from deepseek-ai. No API price is listed for it yet. We hold 1 benchmark result for it across 1 area, and none is in the top tenth of its board.
Where DeepSeek-Coder-V2-Lite-Instruct stands
On our aggregate score, DeepSeek-Coder-V2-Lite-Instruct is #546 of 743 ranked models, in the lower half. Its score is 46.0; the scale is centred on 50, so it sits 4.0 points below the typical model. That beats 27% of the ranked field. Among the 7 ranked deepseek-ai models it is #5. The next one up is DeepSeek-V2 at 46.6. The next one down is deepseek-math-7b-instruct at 45.2.
DeepSeek-Coder-V2-Lite-Instruct, benchmark by benchmark
DeepSeek-Coder-V2-Lite-Instruct lands below the middle on MMLU-Pro: 41.6%, #95 of 136, with 69% of the field scoring higher. Darwin-180B-RSI leads at 88.1%; the closest result above is MiMo-7B-Base at 41.9%. It covers 57-subject knowledge + reasoning. The board median is 55.3%, so DeepSeek-Coder-V2-Lite-Instruct is 13.7 points below it. Source: HuggingFace MMLU-Pro leaderboard, captured 2026-10-13.
Reading these numbers
The 1 score on this page comes from 1 source: HuggingFace MMLU-Pro leaderboard (1). No official leaderboard supplied any of them, so treat them as third-party measurements. With 1 results the aggregate rank can move a lot when one new score arrives.
Models ranked nearby
- #543 SWE-1.6 (46.0)
- #544 claude-opus-4-1-20250805_32K (46.0)
- #545 Eurus-2-7B-PRIME (46.0)
- #547 o3-mini-2025-01-31 (low) (46.0)
- #548 nvidia.nemotron-nano-9b-v2 (46.0)
- #549 Gemini 2.0 Flash Thinking Exp (45.9)
More from deepseek-ai
- DeepSeek R1 (0528) (#33, 53.6)
- DeepSeek-V2.5 (#162, 50.9)
- DeepSeek-V2-Chat (#404, 47.8)
- DeepSeek-V2 (#505, 46.6)
- deepseek-math-7b-instruct (#604, 45.2)
- DeepSeek-Coder-V2-Lite-Base (#608, 45.0)
Compare it on the live leaderboard → · How the aggregate score works