DeepSeek R1 Distill Qwen 14B
DeepSeek R1 Distill Qwen 14B is an API model from DeepSeek. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
Intelligence
Below avg
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Pay per token
Input and output billed separately.
Pay-per-token Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- First-generation Qwen — historical; superseded by 2.5+.
- First open-source model to top OpenCompass leaderboard — beat Llama 3.1 405B with 1/5 the parameters
- 72B-Instruct: 74.2 on coding, 77 on math — outscored Claude 3.5 Sonnet (72.1) and GPT-4o (70.6) at release
- MMLU 85+, HumanEval 85+, MATH 80+ — frontier-tier across the board for an open-weights model
Best use cases
- Self-hosted production where Llama 3.1 405B is too big to serve
- Multilingual deployments (29 languages with strong coverage)
- Fine-tuning on private code or technical corpora
Tools to try
Not ideal for
- Frontier reasoning by mid-2025 — Qwen 3 / DeepSeek R1 / Claude 4 series have moved past it
- Vision tasks (use Qwen 2.5-VL) or audio (use Qwen 2.5-Audio)
Model Evolution
deepseek-r is DeepSeek's language model family.