DeepSeek OCR 2
Built by DeepSeek · China · deepseek.com
DeepSeek-OCR 2 is a multimodal document recognition model released by DeepSeek AI, serving as an upgrade to DeepSeek-OCR. By introducing the DeepEncoder V2 architecture, it achieves a paradigm shift in visual encoding from "fixed scanning" to "semantic reasoning." The model replaces the original CLIP encoder with a lightweight language model (Qwen2-0.5B) and incorporates a causal flow query mechanism, while retaining the DeepSeek-3B-MoE decoder. The model requires only 256 to 1120 visual tokens to cover complex document pages. On the OmniDocBench v1.5 benchmark, it achieves an overall score of 91.09%, a 3.73% improvement over its predecessor, with reading order recognition edit distance reduced from 0.085 to 0.057.
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-2: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.