diff --git a/verifiers/envs/environment.py b/verifiers/envs/environment.py
index f79eb1859..30cedcd0a 100644
--- a/verifiers/envs/environment.py
+++ b/verifiers/envs/environment.py
@@ -230,6 +230,7 @@ async def get_model_response(
         Convenience function for wrapping (chat, completion) API calls.
         Returns special error messages for context length issues.
         """
+        RAW_RESPONSE = True
         sampling_args = sampling_args or {}
         # resolve message type first
         if message_type is None:
@@ -248,10 +249,21 @@ async def get_model_response(
         ):
             sampling_args.pop("max_completion_tokens")
         clean_sampling_args = {k: v for k, v in sampling_args.items() if v is not None}
+
+        if RAW_RESPONSE:
+            if message_type == "chat":
+                fn = client.chat.completions.with_raw_response.create
+            else:
+                fn = client.completions.with_raw_response.create
+        else:
+            if message_type == "chat":
+                fn = client.chat.completions.create
+            else:
+                fn = client.completions.create
+
         try:
             if message_type == "chat":
                 assert isinstance(prompt, list)
-                # --- detect audio parts and force text-only modality if caller didn't set one ---
                 has_audio = False
                 try:
                     for m in prompt:
@@ -274,32 +286,26 @@ async def get_model_response(
                     }
 
                 if oai_tools:
-                    response = await client.chat.completions.create(
+                    response = await fn(
                         model=model,
                         messages=prompt,  # type: ignore
                         tools=oai_tools,
                         **clean_sampling_args,
                     )
                 else:
-                    response = await client.chat.completions.create(
+                    response = await fn(
                         model=model,
                         messages=prompt,  # type: ignore
                         **clean_sampling_args,
                     )
-                return response
             elif message_type == "completion":
                 if oai_tools:
                     raise ValueError(
                         "oai_tools are not supported for completion tasks."
                     )
                 assert isinstance(prompt, str)
-                response = await client.completions.create(
-                    model=model, prompt=prompt, **clean_sampling_args
-                )
-                return response
+                response = await fn(model=model, prompt=prompt, **clean_sampling_args)
         except Exception as e:
-            # in case of making a request with an overlong prompt, e.g from a too-long
-            # environment response, we return a dummy response to with finish_reason "length"
             if isinstance(e, BadRequestError):
                 error_text = e.response.text.lower()
                 context_length_phrases = [
@@ -315,6 +321,88 @@ async def get_model_response(
             self.logger.error(f"Error getting model response: {e} \n\nExiting...")
             raise e
 
+        if RAW_RESPONSE:
+            from typing import cast, Union, Dict, List, Optional, Any
+            from openai.types import Completion
+            from openai.types.chat import ChatCompletion
+            from openai._legacy_response import LegacyAPIResponse
+
+            response = cast(
+                Union[LegacyAPIResponse[Completion], LegacyAPIResponse[ChatCompletion]],
+                response,
+            )
+
+            def _as_float(x: Any) -> Optional[float]:
+                try:
+                    return float(x)
+                except Exception:
+                    return None
+
+            def _coerce_logprobs_inplace(raw: Dict[str, Any]) -> None:
+                """Mutate `raw` to normalize/strip logprobs without copying."""
+                choices = raw.get("choices")
+                if not isinstance(choices, list):
+                    return
+
+                for ch in choices:
+                    lp = ch.get("logprobs")
+
+                    if lp is None:
+                        continue
+
+                    if isinstance(lp, dict) and isinstance(lp.get("content"), list):
+                        for t in lp["content"]:
+                            if isinstance(t, dict) and "logprob" in t:
+                                t["logprob"] = _as_float(t["logprob"])
+                                tops = t.get("top_logprobs")
+                                if isinstance(tops, list):
+                                    for tlp in tops:
+                                        if isinstance(tlp, dict) and "logprob" in tlp:
+                                            tlp["logprob"] = _as_float(tlp["logprob"])
+                        continue
+
+                    if isinstance(lp, dict) and {"tokens", "token_logprobs"} <= set(
+                        lp.keys()
+                    ):
+                        tokens = lp.get("tokens") or []
+                        token_lps = lp.get("token_logprobs") or []
+                        top_series = lp.get("top_logprobs")
+                        if not isinstance(top_series, list):
+                            top_series = []
+
+                        content: List[Dict[str, Any]] = []
+                        for i, (tok, logp) in enumerate(zip(tokens, token_lps)):
+                            item: Dict[str, Any] = {
+                                "token": tok,
+                                "logprob": _as_float(logp),
+                            }
+                            tops_i = top_series[i] if i < len(top_series) else None
+                            if isinstance(tops_i, dict):
+                                item["top_logprobs"] = [
+                                    {"token": k, "logprob": _as_float(v)}
+                                    for k, v in tops_i.items()
+                                ]
+                            content.append(item)
+
+                        ch["logprobs"] = {"content": content}
+                        continue
+
+            def parse_fast_no_validate(response) -> Union[ChatCompletion, Completion]:
+                """
+                Convert the HTTP JSON to a ChatCompletion *without* running Pydantic validation.
+                Only coerces/normalizes logprobs; everything else passes through untouched.
+                """
+                raw = response.http_response.json()
+                _coerce_logprobs_inplace(raw)
+
+                return ChatCompletion.model_construct(**raw)
+
+            # --- usage ---
+            chat_completion = parse_fast_no_validate(response)
+            return chat_completion
+        else:
+            return response
+
     @abstractmethod
     async def rollout(
         self,