deepseek-math-7b-instruct: benchmark scores, price and ranking
deepseek-math-7b-instruct from deepseek-ai ranks #604 of 743 LLMs on the LLMs Tiger aggregate benchmark score (45.2), based on 1 benchmark results captured up to 2026-10-13.
- Organisation
- deepseek-ai
- Type
- Open-weight / local model
- Open weights
- Yes
- Aggregate rank
- #604 of 743 (score 45.2)
Across the 1 benchmark where at least ten models are scored, deepseek-math-7b-instruct is in the top 10% on 0 and in the top half on 0.
deepseek-math-7b-instruct benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| MMLU-Pro | 35.3% | #111 of 136 | HuggingFace MMLU-Pro leaderboard | 2026-10-13 |
About deepseek-math-7b-instruct
deepseek-math-7b-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-math-7b-instruct stands
On our aggregate score, deepseek-math-7b-instruct is #604 of 743 ranked models, in the lower half. Its score is 45.2; the scale is centred on 50, so it sits 4.8 points below the typical model. That beats 19% of the ranked field. Among the 7 ranked deepseek-ai models it is #6. The next one up is DeepSeek-Coder-V2-Lite-Instruct at 46.0. The next one down is DeepSeek-Coder-V2-Lite-Base at 45.0.
deepseek-math-7b-instruct, benchmark by benchmark
MMLU-Pro is a weak spot. deepseek-math-7b-instruct scores 35.3%, #111 of 136, behind 81% of the models measured. The leader, Darwin-180B-RSI, reaches 88.1%. It covers 57-subject knowledge + reasoning. The board median is 55.3%, so deepseek-math-7b-instruct is 20.0 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
- #601 Mistral-Nemo-Instruct-2407 (45.2)
- #602 claude-haiku-4-5-20251001 (45.2)
- #603 Meta-Llama-3-8B (45.2)
- #605 ollama/hermes3:8b-llama3.1-fp16 (45.1)
- #606 ollama/wojtek/opencodeinterpreter:6.7b (45.1)
- #607 DeepSeek-V3 (45.1)
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-Coder-V2-Lite-Instruct (#546, 46.0)
- DeepSeek-Coder-V2-Lite-Base (#608, 45.0)
Compare it on the live leaderboard → · How the aggregate score works