DeepSeek cache-hit token extraction and pricing fix

Fixes on record

Merged to main. A stamp counts the issues a fix is linked to.

• #65678 fix(usage): read DeepSeek's native prompt_cache_hit_tokens cache field

  merged to main

  files: agent/transports/chat_completions.py, agent/usage_pricing.py, tests/agent/test_usage_pricing.py, tests/agent/transports/test_chat_completions.py

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  • agent/transports/chat_completions.py

    @@ -776,15 +776,18 @@ def validate_response(self, response: Any) -> bool:
             return True
     
         def extract_cache_stats(self, response: Any) -> dict[str, int] | None:
    -        """Extract OpenRouter/OpenAI cache stats from prompt_tokens_details."""
    +        """Extract cache stats from prompt_tokens_details (OpenRouter/OpenAI)
    +        or DeepSeek's native top-level prompt_cache_hit_tokens field."""
             usage = getattr(response, "usage", None)
             if usage is None:
                 return None
             details = getattr(usage, "prompt_tokens_details", None)
    -        if details is None:
    -            return None
    -        cached = getattr(details, "cached_tokens", 0) or 0
    -        written = getattr(details, "cache_write_tokens", 0) or 0
    +        cached = getattr(details, "cached_tokens", 0) or 0 if details else 0
    +        written = getattr(details, "cache_write_tokens", 0) or 0 if details else 0
    +        if not cached:
    +            # DeepSeek native API shape (api.deepseek.com): top-level
    +            # prompt_cache_hit_tokens / prompt_cache_miss_tokens (#61871).
    +            cached = getattr(usage, "prompt_cache_hit_tokens", 0) or 0
             if cached or written:
                 return {"cached_tokens": cached, "creation_tokens": written}
             return None

  • agent/usage_pricing.py

    @@ -1074,6 +1074,15 @@ def normalize_usage(
             cache_read_tokens = [redacted:secret-assignment] "cached_tokens", 0) if details else 0)
             if not cache_read_tokens:
                 [redacted:secret-assignment] = _to_int(getattr(response_usage, "cache_read_input_tokens", 0))
    +        if not cache_read_tokens:
    +            # DeepSeek's native API (api.deepseek.com) reports context-cache
    +            # hits as top-level prompt_cache_hit_tokens (+ the complementary
    +            # prompt_cache_miss_tokens; prompt_tokens = hit + miss), not the
    +            # OpenAI nested shape. Without this, direct DeepSeek sessions
    +            # always showed 0 cache-hit tokens (#61871).
    +            cache_read_tokens = [redacted:secret-assignment]
    +                getattr(response_usage, "prompt_cache_hit_tokens", 0)
    +            )
             cache_write_tokens = [redacted:secret-assignment]
                 getattr(details, "cache_write_tokens", 0) if details else 0
             )

  • tests/agent/test_usage_pricing.py

    @@ -39,6 +39,44 @@ def test_normalize_usage_openai_subtracts_cached_prompt_tokens():
         assert normalized.output_tokens == 700
     
     
    +def test_normalize_usage_reads_deepseek_native_cache_hit_tokens():
    +    """DeepSeek's native API (api.deepseek.com) reports context-cache hits as
    +    top-level prompt_cache_hit_tokens / prompt_cache_miss_tokens (with
    +    prompt_tokens = hit + miss), not OpenAI's nested
    +    prompt_tokens_details.cached_tokens. Before this fix, direct DeepSeek
    +    sessions always normalized to cache_read_tokens=0 — cache hits were
    +    invisible in accounting and billed at the full input rate (#61871)."""
    +    usage = SimpleNamespace(
    +        prompt_tokens=2000,
    +        completion_tokens=400,
    +        prompt_cache_hit_tokens=1500,
    +        prompt_cache_miss_tokens=500,
    +    )
    +
    +    normalized = normalize_usage(usage, provider="deepseek", api_mode="chat_completions")
    +
    +    assert normalized.cache_read_tokens == 1500
    +    # prompt_tokens includes cached; input = 2000 - 1500 = the miss bucket
    +    assert normalized.input_tokens == 500
    +    assert normalized.output_tokens == 400
    +
    +
    +def test_normalize_usage_nested_details_win_over_deepseek_top_level():
    +    """When a proxy forwards both shapes, the OpenAI nested value wins and
    +    the DeepSeek top-level field is not double-read."""
    +    usage = SimpleNamespace(
    +        prompt_tokens=2000,
    +        completion_tokens=100,
    +        prompt_tokens_details=[redacted:secret-assignment]
    +        prompt_cache_hit_tokens=1500,
    +    )
    +
    +    normalized = normalize_usage(usage, provider="deepseek", api_mode="chat_completions")
    +
    +    assert normalized.cache_read_tokens == 900
    +    assert normalized.input_tokens == 1100
    +
    +
     def test_normalize_usage_openai_reads_top_level_anthropic_cache_fields():
         """Some OpenAI-compatible proxies (OpenRouter, Cline) expose
         Anthropic-style cache token counts at the top level of the usage object when

  • tests/agent/transports/test_chat_completions.py

    @@ -1115,6 +1115,33 @@ def test_with_cache(self, transport):
             result = transport.extract_cache_stats(r)
             assert result == {"cached_tokens": 500, "creation_tokens": 100}
     
    +    def test_deepseek_native_top_level_cache_hit_tokens(self, transport):
    +        """DeepSeek's native API (api.deepseek.com) reports cache hits as
    +        top-level prompt_cache_hit_tokens, not the OpenAI nested shape —
    +        the extractor must read it or direct DeepSeek sessions show 0%
    +        cache hit rate (#61871)."""
    +        r = SimpleNamespace(
    +            usage=SimpleNamespace(
    +                prompt_tokens_details=None,
    +                prompt_cache_hit_tokens=1500,
    +                prompt_cache_miss_tokens=500,
    +            )
    +        )
    +        result = transport.extract_cache_stats(r)
    +        assert result == {"cached_tokens": 1500, "creation_tokens": 0}
    +
    +    def test_nested_details_win_over_deepseek_top_level(self, transport):
    +        """When both shapes are present, the OpenAI nested value wins."""
    +        details = SimpleNamespace(cached_tokens=800, cache_write_tokens=0)
    +        r = SimpleNamespace(
    +            usage=SimpleNamespace(
    +                prompt_tokens_details=[redacted:secret-assignment]
    +                prompt_cache_hit_tokens=1500,
    +            )
    +        )
    +        result = transport.extract_cache_stats(r)
    +        assert result == {"cached_tokens": 800, "creation_tokens": 0}
    +
     
     class TestChatCompletionsGeminiNativeExtraBodyStrip:
         """Profile extra_body (e.g. Nous portal tags) must not reach a native