diff --git a/lm_eval/models/__init__.py b/lm_eval/models/__init__.py
index 218f880939b..5e0de386cd7 100644
--- a/lm_eval/models/__init__.py
+++ b/lm_eval/models/__init__.py
@@ -32,6 +32,7 @@
     "hf": "lm_eval.models.huggingface:HFLM",
     "hf-audiolm-qwen": "lm_eval.models.hf_audiolm:HFAudioLM",
     "hf-auto": "lm_eval.models.huggingface:HFLM",
+    "hf-mistral3": "lm_eval.models.mistral3:Mistral3LM",
     "hf-multimodal": "lm_eval.models.hf_vlms:HFMultimodalLM",
     "huggingface": "lm_eval.models.huggingface:HFLM",
     "ipex": "lm_eval.models.optimum_ipex:IPEXForCausalLM",
diff --git a/lm_eval/models/mistral3.py b/lm_eval/models/mistral3.py
new file mode 100644
index 00000000000..fa8164a3f91
--- /dev/null
+++ b/lm_eval/models/mistral3.py
@@ -0,0 +1,129 @@
+"""
+Mistral3 model adapter for lm-evaluation-harness.
+
+This adapter enables evaluation of Ministral-3 models (3B, 8B, 14B) which use
+Mistral3ForConditionalGeneration instead of AutoModelForCausalLM.
+
+Usage:
+    lm_eval --model hf-mistral3 \
+        --model_args pretrained=mistralai/Ministral-3-3B-Instruct-2512-BF16,dtype=bfloat16 \
+        --tasks hellaswag \
+        --device cuda:0 \
+        --batch_size 8
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Literal
+
+import torch
+
+from lm_eval.api.registry import register_model
+from lm_eval.models.huggingface import HFLM
+
+
+if TYPE_CHECKING:
+    import transformers
+
+eval_logger = logging.getLogger(__name__)
+
+
+@register_model("hf-mistral3")
+class Mistral3LM(HFLM):
+    """
+    Model adapter for Mistral3 models (Ministral-3 family).
+
+    These models use Mistral3ForConditionalGeneration which is a vision-language
+    model class, but can be used for text-only evaluation by ignoring the vision
+    encoder.
+    """
+
+    AUTO_MODEL_CLASS = None  # Set dynamically in __init__
+
+    def __init__(self, **kwargs):
+        # Import here to avoid import errors if transformers version doesn't support Mistral3
+        try:
+            from transformers import Mistral3ForConditionalGeneration
+
+            self.AUTO_MODEL_CLASS = Mistral3ForConditionalGeneration
+        except ImportError:
+            raise ImportError(
+                "Mistral3ForConditionalGeneration not found in transformers. "
+                "Please install transformers >= 5.0.0 or from main: "
+                "pip install git+https://github.com/huggingface/transformers"
+            ) from None
+
+        super().__init__(**kwargs)
+
+    def _get_backend(
+        self,
+        config: transformers.PretrainedConfig | transformers.AutoConfig,
+        backend: Literal["default", "causal", "seq2seq"] = "default",
+        trust_remote_code: bool | None = False,
+    ) -> None:
+        """
+        Override to force causal backend for Mistral3 models.
+
+        Mistral3 models are decoder-only despite using a conditional generation class.
+        """
+        # Always use causal backend for Mistral3
+        self.backend = "causal"
+        eval_logger.info("Using backend 'causal' for Mistral3 model")
+
+    def _model_call(
+        self,
+        inps: torch.Tensor,
+        attn_mask: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """
+        Override to handle Mistral3 model output format.
+
+        Mistral3ForConditionalGeneration returns logits in the same format as
+        causal LMs, so we call the model directly but bypass the base class
+        assertion that checks for AutoModelForCausalLM.
+        """
+        with (
+            torch.no_grad(),
+            torch.autocast(
+                device_type=self.device.type,
+                dtype=self.mixed_precision_dtype,
+                enabled=self.mixed_precision_dtype is not None,
+            ),
+        ):
+            # Mistral3 models work like causal LMs for text-only input
+            return self.model(inps).logits
+
+    @property
+    def max_length(self) -> int:
+        """Get the maximum sequence length for the model."""
+        if self._max_length:
+            return self._max_length
+
+        seqlen_config_attrs = (
+            "max_position_embeddings",
+            "n_positions",
+            "n_ctx",
+        )
+
+        # First check text_config if it exists (for VLM-style models like Mistral3)
+        if hasattr(self.model.config, "text_config"):
+            text_config = self.model.config.text_config
+            for attr in seqlen_config_attrs:
+                if hasattr(text_config, attr):
+                    return getattr(text_config, attr)
+
+        # Fall back to main config
+        for attr in seqlen_config_attrs:
+            if hasattr(self.model.config, attr):
+                return getattr(self.model.config, attr)
+
+        # Check tokenizer
+        if (
+            hasattr(self.tokenizer, "model_max_length")
+            and self.tokenizer.model_max_length < 1000000000
+        ):
+            return self.tokenizer.model_max_length
+
+        return self._DEFAULT_MAX_LENGTH