DeepSeek¶

An OpenAI-compatible dialect with three deltas that would otherwise cost a developer silently: reasoning arrives on its own channel, thinking is on by default, and cache accounting is automatic and split.

streaming structured output (JSON mode) tool calls reasoning cache accounting

Setup¶

import anyinfer as ai

client = ai.Client(
    [
        ai.ProviderSettings.of("deepseek", api_key="env://DEEPSEEK_API_KEY"),
    ]
)

result = client.generate(prompt, target="deepseek:deepseek-v4-pro")

Two models are served: deepseek-v4-flash and deepseek-v4-pro. The old deepseek-chat / deepseek-reasoner aliases were discontinued in July 2026.

Reasoning¶

Thinking is on by default. Chain-of-thought streams as ReasoningDelta events, separate from the answer:

with client.stream(prompt, target="deepseek:deepseek-v4-pro") as stream:
    for event in stream:
        if isinstance(event, ai.ReasoningDelta):
            print("[thinking]", event.text, end="")
        elif isinstance(event, ai.TextDelta):
            print(event.text, end="")

Requesting an effort level enables thinking explicitly and sets the level. Because DeepSeek accepts low/high/max, AnyInfer maps minimal and low to low, and medium and high to high, rather than sending a value the API would rewrite:

result = client.generate(prompt, target="deepseek:deepseek-v4-pro", reasoning="low")

To turn thinking off (a behavior change, not an effort setting):

client.generate(
    prompt,
    target="deepseek:deepseek-v4-pro",
    provider_options={"deepseek": {"thinking": {"type": "disabled"}}},
)

Sampling is ignored while thinking

DeepSeek silently discards temperature and top_p in thinking mode, which is the default. AnyInfer declares both as ignored, so setting one raises a ParameterDropped telemetry event instead of silently doing nothing.

Cache Accounting¶

Context caching is automatic: no opt-in, no cache-control parameters. DeepSeek reports the split, and cache hits bill at a much lower rate:

result = client.generate(long_prompt, target="deepseek:deepseek-v4-flash")
print(result.usage.input_tokens)  # hits + misses
print(result.usage.cache_read_tokens)  # the part that was cheap

Cost is a ceiling here

The bundled pricing table records the standard (cache-miss) rate, so usage.cost_usd overstates spend on cache-heavy workloads; supply capability_overrides for the blended rate.

The Anthropic-Compatible Endpoint¶

DeepSeek also exposes a Messages endpoint at https://api.deepseek.com/anthropic, which the Anthropic adapter serves through a base-URL override; see pointing that adapter elsewhere. Use the native deepseek: provider unless Messages-dialect behavior is specifically needed; the reasoning channel and cache accounting above are only wired there.

Wire Contract¶

For the exact request/response fields this adapter depends on, see contracts/deepseek.md.