DeepSeek OCR
Built by DeepSeek · China · deepseek.com
DeepSeek-OCR as an initial investigation into the feasibility of compressing long contexts via optical 2D mapping. DeepSeek-OCR consists of two components: DeepEncoder and DeepSeek3B-MoE-A570M as the decoder. Specifically, DeepEncoder serves as the core engine, designed to maintain low activations under high-resolution input while achieving high compression ratios to ensure an optimal and manageable number of vision tokens. Experiments show that when the number of text tokens is within 10 times that of vision tokens (i.e., a compression ratio < 10x), the model can achieve decoding (OCR) precision of 97%. Even at a compression ratio of 20x, the OCR accuracy still remains at about 60%. This shows considerable promise for research areas such as historical long-context compression and memory forgetting mechanisms in LLMs.
How to use
Just add the suffix. Hard questions still come back from this model; only easy ones drop to something cheaper, and nothing pricier than it gets used.
model: "deepseek/deepseek-ocr:auto"
Sets no behavior — whatever your key already stores stays in effect.
Without the suffix, the bare name always goes to this model — no routing, so nothing saved.
Context
Max output
Input $/1M
Output $/1M
Features
Providers
NovitaProviders
Price, latency, and uptime per provider serving this model. Warp tries them in order of how each host has just been behaving, moving to the next on failure. Click a row for regions and data policies.
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Performance
Measured latency (lower is better) and throughput (higher is better) per provider.
Speed
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Parameter support
What Warp actually does with each parameter when you call this model. The answer differs by serving provider and by API surface.