deepseek-r1-0528-qwen3-8b: benchmark scores, price and ranking
deepseek-r1-0528-qwen3-8b from DeepSeek ranks #670 of 743 LLMs on the LLMs Tiger aggregate benchmark score (43.6), based on 3 benchmark results captured up to 2026-10-13.
- Organisation
- DeepSeek
- Type
- Open-weight / local model
- Open weights
- Yes
- Released
- 2025-05-28 (source: epoch)
- Cheapest API price
- $0.060 in / $0.090 out per million tokens (via novita, observed 2026-10-13T20:47Z)
- Aggregate rank
- #670 of 743 (score 43.6)
Relative to other models, deepseek-r1-0528-qwen3-8b is strongest on OTIS Mock AIME (#112 of 189) and weakest on GPQA Diamond (#275 of 275).
Across the 3 benchmarks where at least ten models are scored, deepseek-r1-0528-qwen3-8b is in the top 10% on 0 and in the top half on 0.
Its cheapest listed input price, $0.060 per million tokens, is lower than that of 89% of the 427 models with a listed price.
deepseek-r1-0528-qwen3-8b benchmark results
| Benchmark | Score | Rank | Source | Captured |
|---|---|---|---|---|
| OTIS Mock AIME | 43.9% | #112 of 189 | Epoch AI Benchmarking Hub | 2026-10-13 |
| Chess Puzzles | 3.0% | #89 of 122 | Epoch AI Benchmarking Hub | 2026-10-13 |
| GPQA Diamond | 9.3% | #275 of 275 | Epoch AI Benchmarking Hub | 2026-10-13 |
About deepseek-r1-0528-qwen3-8b
deepseek-r1-0528-qwen3-8b is an open-weight model that can run on your own hardware from DeepSeek. It was released on 2025-05-28, according to Epoch AI. The cheapest API listing we track charges $0.060 per million input tokens and $0.090 per million output tokens, through novita. We hold 3 benchmark results for it across 2 areas, and none is in the top tenth of its board.
Where deepseek-r1-0528-qwen3-8b stands
On our aggregate score, deepseek-r1-0528-qwen3-8b is #670 of 743 ranked models, in the lower half. Its score is 43.6; the scale is centred on 50, so it sits 6.4 points below the typical model. That beats 10% of the ranked field. Among the 33 ranked DeepSeek models it is #32. The next one up is DeepSeek-V3 (Mar 2025) at 44.1. The next one down is deepseek-llm-67b-chat at 42.8.
deepseek-r1-0528-qwen3-8b, benchmark by benchmark
deepseek-r1-0528-qwen3-8b lands below the middle on OTIS Mock AIME: 43.9%, #112 of 189, with 59% of the field scoring higher. Claude Fable 5.1 (max) leads at 100.0%; the closest result above is o3-mini-2025-01-31 (low) at 44.4%. It covers competition maths (AIME 2024-25 style). The board median is 57.8%, so deepseek-r1-0528-qwen3-8b is 13.9 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
deepseek-r1-0528-qwen3-8b lands below the middle on Chess Puzzles: 3.0%, #89 of 122, with 72% of the field scoring higher. GPT-6 Astra (max) leads at 72.0%; the closest result above is Claude Sonnet 4.6 (max) at 3.0%. It covers chess tactics puzzles. The board median is 12.5%, so deepseek-r1-0528-qwen3-8b is 9.5 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
GPQA Diamond is a weak spot. deepseek-r1-0528-qwen3-8b scores 9.3%, #275 of 275, behind 100% of the models measured. The leader, GPT-6 Astra (max), reaches 95.8%. It covers expert-level science questions. The board median is 71.7%, so deepseek-r1-0528-qwen3-8b is 62.4 points below it. Source: Epoch AI Benchmarking Hub, captured 2026-10-13.
Strengths and weaknesses
Reasoning, maths and science: weak, ahead of 20% of the field on average across 2 benchmarks. Its best board here is OTIS Mock AIME (#112 of 189) and its weakest is GPQA Diamond (#275 of 275).
Price and value
The median input price across the 427 priced models is $0.500 per million tokens, so deepseek-r1-0528-qwen3-8b costs less than the typical listing. Models that score within 1.5 points or better and cost less: Qwen2.5-Coder-7B-Instruct ($0.010, 49.5), DeepSeek V4.1 Flash (high) ($0.010, 48.9), Qwen2.5-Coder-3B-Instruct ($0.010, 46.5). Similar scores cost more on Qwen3-30B-A3B ($0.090) and DeepSeek-R1-Distill-Qwen-1.5B ($0.090).
Release history
The DeepSeek model released before it was DeepSeek-V3 (Mar 2025) on 2025-03-24, which scores 44.1. The next one was DeepSeek-R1 (May 2025) on 2025-05-28, at 48.2.
Reading these numbers
The 3 scores on this page come from 1 source: Epoch AI Benchmarking Hub (3). No official leaderboard supplied any of them, so treat them as third-party measurements. With 3 results the aggregate rank can move a lot when one new score arrives.
Models ranked nearby
- #667 Claude 2 (43.7)
- #668 Llama-3.1-405B-Instruct (43.6)
- #669 granite-3.1-2b-base (43.6)
- #671 ollama/granite3-dense:8b (43.6)
- #672 llemma_7b (43.6)
- #673 Mixtral-8x22B-Instruct-v0.1 (43.5)
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Compare it on the live leaderboard → · How the aggregate score works