diff --git a/evalharness/model/runner.py b/evalharness/model/runner.py
index 569715a..682cc0d 100644
--- a/evalharness/model/runner.py
+++ b/evalharness/model/runner.py
@@ -35,6 +35,8 @@ async def generate_predictions(
     system: str = '',
     max_turns: int = 8,
     max_input_chars: int = 0,
+    max_input_tokens: int = 0,
+    tokenizer_path: str = '',
     attach_context_keys: tuple = ('passage', 'context'),
     limit_per_task: Optional[int] = None,
     checkpoint: Union[bool, str] = False,
@@ -113,6 +115,14 @@ async def generate_predictions(
                             'in the form "Answer: <answer>".')
         parts.append(question)
         text = '\n\n'.join(parts)
+        if max_input_tokens:
+            try:
+                from .truncation import truncate_middle_tokens, default_tokenizer_path
+
+                text = truncate_middle_tokens(text, max_input_tokens,
+                                              tokenizer_path or default_tokenizer_path())
+            except Exception:
+                pass  # no tokenizer: fall through to chars truncation
         if max_input_chars and len(text) > max_input_chars:
             keep = max_input_chars // 2
             head = text[:keep]
@@ -268,6 +278,7 @@ async def run_eval(
     system: str = '',
     max_turns: int = 8,
     max_input_chars: int = 0,
+    max_input_tokens: int = 0,
     limit_per_task: Optional[int] = None,
     checkpoint: Union[bool, str] = False,
     dataset_name: str = 'adhoc',
@@ -351,6 +362,7 @@ async def run_eval(
             adapter, samples, concurrency, progress=progress,
             gen_kwargs=gen_kwargs, env_factory=env_factory,
             system=system, max_turns=max_turns, max_input_chars=max_input_chars,
+            max_input_tokens=max_input_tokens,
             limit_per_task=limit_per_task,
             checkpoint=checkpoint,
             dataset_name=name,
diff --git a/evalharness/model/truncation.py b/evalharness/model/truncation.py
new file mode 100644
index 0000000..ac7fac2
--- /dev/null
+++ b/evalharness/model/truncation.py
@@ -0,0 +1,69 @@
+"""Token-level middle truncation (ported from your /data1/sora/evalscope/bash/run.py).
+
+Keeps head+tail halves of the token stream -- the industry-standard
+middle-truncation for long-context benchmarks (longbench_v2 / mrcr).
+The evalside run.py uses the same algorithm, guaranteeing comparable inputs.
+
+Usage in run_eval:  max_input_tokens=131072  (0/off = no truncation)
+Requires a tokenizer (transformers) at tokenizer_path or auto from the model.
+"""
+
+import os
+from functools import lru_cache
+from typing import Optional
+
+DEFAULT_TRUNCATION_TOKENS = 32768 * 4  # 131072, mirrors evalside run.py
+
+
+@lru_cache(maxsize=4)
+def _get_tokenizer(tokenizer_path: str):
+    if not tokenizer_path or not os.path.exists(tokenizer_path):
+        raise FileNotFoundError(
+            f'tokenizer not found at {tokenizer_path!r} -- token-level truncation '
+            'needs a local tokenizer dir (e.g. /data1/models/DeepSeek-V4-Flash-INT8)')
+    from transformers import AutoTokenizer
+
+    return AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True)
+
+
+def truncate_middle_tokens(text: str, max_tokens: int, tokenizer_path: str) -> str:
+    """Keep head+tail halves of the token stream; decode back to text."""
+    if max_tokens <= 0 or not text:
+        return text
+    tok = _get_tokenizer(tokenizer_path)
+    ids = tok.encode(text, add_special_tokens=False)
+    if len(ids) <= max_tokens:
+        return text
+    keep_head = max_tokens // 2
+    keep_tail = max_tokens - keep_head
+    return tok.decode(ids[:keep_head] + ids[-keep_tail:], skip_special_tokens=True)
+
+
+def truncate_messages_middle(messages: list, max_tokens: int, tokenizer_path: str,
+                             desired_index: int = 0, window: int = 2) -> list:
+    """MRCR-style: when a message list exceeds the budget, keep first/last
+    messages plus a window around the desired (needle) message, dropping
+    middle spans; middle of each KEPT long message is token-truncated."""
+    if max_tokens <= 0 or not messages:
+        return messages
+    tok = _get_tokenizer(tokenizer_path)
+    total = sum(len(tok.encode(m.get('content', '') if isinstance(m, dict) else str(m),
+                                add_special_tokens=False)) for m in messages)
+    if total <= max_tokens:
+        return messages
+    n = len(messages)
+    keep = set(range(min(2, n))) | set(range(max(0, n - 2), n))
+    di = desired_index if isinstance(desired_index, int) and 0 <= desired_index < n else 0
+    keep |= set(range(max(0, di - window), min(n, di + window + 1)))
+    return [messages[i] for i in sorted(keep)]
+
+
+def default_tokenizer_path() -> Optional[str]:
+    """Candidate local tokenizer for truncation (mirrors evalside default)."""
+    env = os.environ.get('EVALHARNESS_TOKENIZER')
+    if env and os.path.exists(env):
+        return env
+    for cand in ('/data1/models/DeepSeek-V4-Flash-INT8',):
+        if os.path.exists(cand):
+            return cand
+    return None