diff --git a/docs_new/cookbook/autoregressive/Mistral/Mistral-Medium-3.5.mdx b/docs_new/cookbook/autoregressive/Mistral/Mistral-Medium-3.5.mdx
new file mode 100644
index 000000000..a5c2a3104
--- /dev/null
+++ b/docs_new/cookbook/autoregressive/Mistral/Mistral-Medium-3.5.mdx
@@ -0,0 +1,398 @@
+---
+title: Mistral Medium 3.5
+metatags:
+    description: "Deploy Mistral Medium 3.5 with SGLang - 128B dense flagship merged model with hybrid reasoning, 256K context, vision input, and FP8 quantization."
+---
+
+import { MistralMedium35Deployment } from '/src/snippets/autoregressive/mistral-medium-3-5-deployment.jsx';
+
+## 1. Model Introduction
+
+**Mistral Medium 3.5** is Mistral AI's first flagship **merged model** — a single dense 128B checkpoint that handles instruction following, reasoning, and coding in one set of weights. It replaces Mistral Medium 3.1 and Magistral in Le Chat, and replaces Devstral 2 in the Vibe coding agent. Reasoning effort is configurable per request, so the same model can answer a quick chat reply or work through a deep agentic run. The vision encoder was trained from scratch to handle variable image sizes and aspect ratios.
+
+**Key Features:**
+
+- **Dense 128B parameters** — no MoE, no MLA, plain GQA (96 heads, 8 KV heads, head_dim=128)
+- **256K context window** — YARN RoPE scaling on top of the original 4K base
+- **Hybrid Reasoning**: Toggle between instant reply and deep reasoning per request via `reasoning_effort` (`"none"` or `"high"`)
+- **Vision**: Accepts text + image input; from-scratch encoder that handles variable image sizes/aspect ratios
+- **Function Calling**: Native tool calling and JSON output
+- **FP8 Native**: Released with FP8 e4m3 static-tensor quantization built in
+- **Multilingual**: 24 supported languages including English, French, German, Spanish, Portuguese, Italian, Japanese, Korean, Russian, Chinese, Arabic, Persian, Indonesian, Malay, Nepali, Polish, Romanian, Serbian, Swedish, Turkish, Ukrainian, Vietnamese, Hindi, and Bengali
+- **License**: Modified MIT (open for commercial and non-commercial use except for companies with large revenue)
+
+**Architecture:**
+
+- Mistral 3 backbone with YARN RoPE for 256K context
+- Dense (no MoE), 128B parameters
+- Standard GQA attention (not MLA)
+- Pixtral-style vision encoder (48 layers, patch_size=14, spatial_merge=2, image_size=1540) trained from scratch
+- Multimodal input: text + image
+
+**Models:**
+
+- **[mistralai/Mistral-Medium-3.5-128B](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B)** (FP8)
+
+The HuggingFace repo ships both the mistral native layout (`params.json` + `consolidated-*.safetensors`) and the HF layout (`config.json` + `model-*.safetensors`). SGLang auto-detects the format — the HF layout is preferred when both are present.
+
+---
+
+## 2. SGLang Installation
+
+SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.
+
+Please refer to the [official SGLang installation guide](../../../docs/get-started/install) for installation instructions.
+
+---
+
+## 3. Model Deployment
+
+### 3.1 Basic Configuration
+
+**Interactive Command Generator**: Use the configuration selector below to generate a launch command for Mistral Medium 3.5.
+
+<MistralMedium35Deployment />
+
+### 3.2 Configuration Tips
+
+- **Tensor Parallelism**: Mistral Medium 3.5 FP8 (~130 GB) requires `--tp 4` on Hopper (H100/H200) and `--tp 2` on Blackwell (B200/B300).
+- **Reasoning effort**: Reasoning depth is configurable per request via `reasoning_effort` (`"none"`, `"high"`). No restart required — toggle per call.
+- **Recommended temperature**: `0.7` when `reasoning_effort="high"`. Anywhere from `0.0` to `0.7` when `reasoning_effort="none"`, depending on the task — lower for to-the-point answers, higher for creative output.
+- **Context length vs memory**: The model has a 256K context window. If you are memory-constrained, lower `--context-length` (e.g. `32768`) and increase once things are stable.
+- **Tool calling**: Enable `--tool-call-parser mistral` to activate native function calling support.
+- **Reasoning parser**: Enable `--reasoning-parser mistral` to separate `reasoning_content` from the main response content.
+- **System prompt**: The model ships with a recommended system prompt in `chat_template.jinja` and `SYSTEM_PROMPT.txt`. If you do not pass a system message yourself, the chat template injects Mistral's default (model identity, current date, tool-use guidelines). For full fidelity with Mistral's reference setup, load `SYSTEM_PROMPT.txt` from the HF repo and substitute `{name}`, `{today}`, `{yesterday}` (see Section 4.6).
+
+---
+
+## 4. Model Invocation
+
+### 4.1 Thinking Mode
+
+Mistral Medium 3.5 is a hybrid reasoning model. By default it does not produce a reasoning trace — pass `reasoning_effort="high"` to switch on the deep-reasoning path. Mistral recommends `temperature=0.7` for reasoning mode.
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:30000/v1",
+    api_key="EMPTY",
+)
+
+response = client.chat.completions.create(
+    model="mistralai/Mistral-Medium-3.5-128B",
+    messages=[
+        {"role": "user", "content": "Solve step by step: what is 17 × 23 + 144 / 12?"},
+    ],
+    temperature=0.7,
+    extra_body={"reasoning_effort": "high"},
+)
+
+print("Reasoning:", response.choices[0].message.reasoning_content)
+print("Answer:", response.choices[0].message.content)
+```
+
+**Output:**
+
+```text Output
+Reasoning: I need to follow the order of operations (PEMDAS/BODMAS): multiplication and
+division before addition, evaluated left to right.
+
+17 × 23: I'll break it as 17 × (20 + 3) = 340 + 51 = 391.
+144 / 12 = 12.
+Finally, 391 + 12 = 403.
+
+Answer: **17 × 23 + 144 / 12 = 403**
+
+Step by step:
+1. 17 × 23 = 391
+2. 144 / 12 = 12
+3. 391 + 12 = 403
+```
+
+### 4.2 Instruct Mode (Reasoning Off)
+
+To skip the reasoning trace and get a fast direct response, set `reasoning_effort="none"`. For instruct mode, Mistral recommends temperature in the `0.0`–`0.7` range depending on how creative the task is:
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:30000/v1",
+    api_key="EMPTY",
+)
+
+response = client.chat.completions.create(
+    model="mistralai/Mistral-Medium-3.5-128B",
+    messages=[
+        {"role": "user", "content": "What is the capital of France?"},
+    ],
+    temperature=0.1,
+    extra_body={"reasoning_effort": "none"},
+)
+
+print(response.choices[0].message.content)
+```
+
+**Output:**
+
+```text Output
+The capital of France is **Paris**. It is one of the most famous and visited cities in
+the world, known for its rich history, art, culture, and landmarks like the Eiffel Tower,
+Louvre Museum, and Notre-Dame Cathedral.
+```
+
+### 4.3 Streaming with Reasoning
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:30000/v1",
+    api_key="EMPTY",
+)
+
+stream = client.chat.completions.create(
+    model="mistralai/Mistral-Medium-3.5-128B",
+    messages=[
+        {"role": "user", "content": "Explain the difference between async and threading in Python."},
+    ],
+    temperature=0.7,
+    extra_body={"reasoning_effort": "high"},
+    stream=True,
+)
+
+print("=== Reasoning ===")
+for chunk in stream:
+    delta = chunk.choices[0].delta
+    if hasattr(delta, "reasoning_content") and delta.reasoning_content:
+        print(delta.reasoning_content, end="", flush=True)
+    elif delta.content:
+        print("\n=== Response ===")
+        print(delta.content, end="", flush=True)
+print()
+```
+
+### 4.4 Tool Calling
+
+Mistral Medium 3.5 supports native function calling. Enable with `--tool-call-parser mistral`:
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:30000/v1",
+    api_key="EMPTY",
+)
+
+tools = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get the current weather for a city",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "location": {"type": "string", "description": "City name"},
+                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+                },
+                "required": ["location"],
+            },
+        },
+    }
+]
+
+response = client.chat.completions.create(
+    model="mistralai/Mistral-Medium-3.5-128B",
+    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
+    tools=tools,
+    tool_choice="auto",
+)
+
+tool_calls = response.choices[0].message.tool_calls
+for tc in tool_calls:
+    print(f"Tool: {tc.function.name}")
+    print(f"Args: {tc.function.arguments}")
+```
+
+**Output:**
+
+```text Output
+Tool: get_weather
+Args: {"location": "Paris"}
+```
+
+### 4.5 Vision (Image Input)
+
+Mistral Medium 3.5 accepts image inputs alongside text. The vision encoder was retrained from scratch to handle variable image sizes and aspect ratios:
+
+```python Example
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:30000/v1",
+    api_key="EMPTY",
+)
+
+response = client.chat.completions.create(
+    model="mistralai/Mistral-Medium-3.5-128B",
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "Describe what you see in this image."},
+                {
+                    "type": "image_url",
+                    "image_url": {"url": "https://raw.githubusercontent.com/sgl-project/sglang/main/assets/logo.png"},
+                },
+            ],
+        }
+    ],
+    temperature=0.7,
+    extra_body={"reasoning_effort": "none"},
+)
+
+print(response.choices[0].message.content)
+```
+
+**Output:**
+
+```text Output
+The image features a stylized representation of the acronym "SGL." The letters
+are large, bold, and orange with a brown outline, giving them a three-dimensional
+effect. To the left of the letters, there is a graphic that resembles a neuron
+or a node with connections, also in a similar orange and brown color scheme. The
+node has a code symbol (</>) inside a square, suggesting a connection to
+programming or technology.
+```
+
+### 4.6 Loading the Reference System Prompt
+
+Mistral ships a `SYSTEM_PROMPT.txt` alongside the weights. The reference setup loads it from the HF repo and substitutes `{name}`, `{today}`, and `{yesterday}` at runtime so the model knows its identity and the current date. SGLang's chat template will inject a default system prompt if you omit one, but for full parity with Mistral's reference, load it explicitly:
+
+```python Example
+from datetime import datetime, timedelta
+from huggingface_hub import hf_hub_download
+from openai import OpenAI
+
+MODEL = "mistralai/Mistral-Medium-3.5-128B"
+
+def load_system_prompt(repo_id: str, filename: str = "SYSTEM_PROMPT.txt") -> str:
+    path = hf_hub_download(repo_id=repo_id, filename=filename)
+    today = datetime.today().strftime("%Y-%m-%d")
+    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
+    name = repo_id.split("/")[-1]
+    with open(path) as f:
+        return f.read().format(name=name, today=today, yesterday=yesterday)
+
+client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
+
+response = client.chat.completions.create(
+    model=MODEL,
+    messages=[
+        {"role": "system", "content": load_system_prompt(MODEL)},
+        {"role": "user", "content": "Write me a sentence where every word starts with the next letter in the alphabet — start with 'a' and end with 'z'."},
+    ],
+    temperature=0.1,
+    extra_body={"reasoning_effort": "none"},
+)
+
+print(response.choices[0].message.content)
+```
+
+---
+
+## 5. Benchmarks
+
+Validation runs on 4× H200 with `--tp 4`, served via the `/v1/chat/completions` endpoint.
+
+### 5.1 Accuracy Benchmarks
+
+#### GSM8K
+
+```bash Command
+python3 benchmark/gsm8k/bench_sglang.py --port 30000
+```
+
+**Results:**
+
+```text Output
+Accuracy: 0.945
+Invalid: 0.000
+Latency: 13.594 s
+Output throughput: 1560.660 token/s
+```
+
+#### MMMU
+
+```bash Command
+python3 benchmark/mmmu/bench_sglang.py --port 30000
+```
+
+**Results:**
+
+```text Output
+Overall accuracy: 0.586
+```
+
+### 5.2 Speed Benchmarks
+
+#### Latency (Low Concurrency)
+
+```bash Command
+python3 -m sglang.bench_serving \
+  --backend sglang \
+  --dataset-name random \
+  --num-prompts 10 \
+  --max-concurrency 1 \
+  --random-input-len 1024 \
+  --random-output-len 512 \
+  --port 30000
+```
+
+**Results:**
+
+```text Output
+============ Serving Benchmark Result ============
+Backend:                                 sglang
+Successful requests:                     10
+Benchmark duration (s):                  38.86
+Total input tokens:                      6101
+Total generated tokens:                  2684
+Output token throughput (tok/s):         69.07
+Mean E2E Latency (ms):                   3883.80
+Median TTFT (ms):                        95.90
+Median TPOT (ms):                        14.19
+==================================================
+```
+
+#### Throughput (High Concurrency)
+
+```bash Command
+python3 -m sglang.bench_serving \
+  --backend sglang \
+  --dataset-name random \
+  --num-prompts 1000 \
+  --max-concurrency 100 \
+  --random-input-len 1024 \
+  --random-output-len 512 \
+  --port 30000
+```
+
+**Results:**
+
+```text Output
+============ Serving Benchmark Result ============
+Backend:                                 sglang
+Successful requests:                     1000
+Benchmark duration (s):                  117.28
+Total input tokens:                      512842
+Total generated tokens:                  262023
+Output token throughput (tok/s):         2234.18
+Total token throughput (tok/s):          6607.01
+Mean E2E Latency (ms):                   11303.79
+Median TTFT (ms):                        152.95
+Median TPOT (ms):                        42.53
+==================================================
+```
diff --git a/docs_new/docs.json b/docs_new/docs.json
index 2b3b58fcb..cd71ed913 100644
--- a/docs_new/docs.json
+++ b/docs_new/docs.json
@@ -1057,6 +1057,7 @@
                     "pages": [
                       "cookbook/autoregressive/Mistral/Ministral-3",
                       "cookbook/autoregressive/Mistral/Mistral-Small-4",
+                      "cookbook/autoregressive/Mistral/Mistral-Medium-3.5",
                       "cookbook/autoregressive/Mistral/Devstral-2"
                     ]
                   },
diff --git a/docs_new/src/snippets/autoregressive/mistral-medium-3-5-deployment.jsx b/docs_new/src/snippets/autoregressive/mistral-medium-3-5-deployment.jsx
new file mode 100644
index 000000000..8dbe903eb
--- /dev/null
+++ b/docs_new/src/snippets/autoregressive/mistral-medium-3-5-deployment.jsx
@@ -0,0 +1,340 @@
+export const MistralMedium35Deployment = () => {
+  const modelId = 'mistralai/Mistral-Medium-3.5-128B';
+
+  const options = {
+    hardware: {
+      name: 'hardware',
+      title: 'Hardware Platform',
+      items: [
+        { id: 'h100', label: 'H100', default: false },
+        { id: 'h200', label: 'H200', default: true },
+        { id: 'b200', label: 'B200', default: false },
+        { id: 'b300', label: 'B300', default: false },
+      ],
+    },
+    reasoning: {
+      name: 'reasoning',
+      title: 'Reasoning Parser',
+      items: [
+        { id: 'disabled', label: 'Disabled', default: false },
+        { id: 'enabled', label: 'Enabled', default: true }
+      ],
+      commandRule: (value) => value === 'enabled' ? '--reasoning-parser mistral' : null
+    },
+    toolcall: {
+      name: 'toolcall',
+      title: 'Tool Call Parser',
+      items: [
+        { id: 'disabled', label: 'Disabled', default: false },
+        { id: 'enabled', label: 'Enabled', default: true }
+      ],
+      commandRule: (value) => value === 'enabled' ? '--tool-call-parser mistral' : null
+    },
+  };
+
+  // 128B dense FP8 ≈ 130GB, plus KV cache headroom
+  const modelConfigs = {
+    h100: { tp: 4 },
+    h200: { tp: 4 },
+    b200: { tp: 2 },
+    b300: { tp: 2 },
+  };
+
+  const generateCommand = (values) => {
+    const { hardware } = values;
+    const hwConfig = modelConfigs[hardware];
+    if (!hwConfig) return `# Error: Unknown hardware combination`;
+    const { tp } = hwConfig;
+
+    let cmd = `sglang serve --model-path ${modelId}`;
+    cmd += ` \\\n  --tp ${tp}`;
+
+    Object.entries(options).forEach(([key, option]) => {
+      if (key === 'hardware') return;
+      if (option.commandRule) {
+        const rule = option.commandRule(values[key]);
+        if (rule) cmd += ` \\\n  ${rule}`;
+      }
+    });
+
+    return cmd;
+  };
+
+  const getInitialState = () => {
+    const initialState = {};
+    Object.entries(options).forEach(([key, option]) => {
+      if (option.type === 'checkbox') {
+        initialState[key] = (option.items || [])
+          .filter((item) => item.default)
+          .map((item) => item.id);
+        return;
+      }
+      if (option.type === 'text') {
+        initialState[key] = option.default || '';
+        return;
+      }
+      let items = option.items || [];
+      if (option.getDynamicItems) {
+        const defaultValues = {};
+        Object.entries(options).forEach(([innerKey, innerOption]) => {
+          if (innerOption.type === 'checkbox') {
+            defaultValues[innerKey] = (innerOption.items || [])
+              .filter((item) => item.default)
+              .map((item) => item.id);
+          } else if (innerOption.type === 'text') {
+            defaultValues[innerKey] = innerOption.default || '';
+          } else if (innerOption.items && innerOption.items.length > 0) {
+            const defaultItem = innerOption.items.find((item) => item.default);
+            defaultValues[innerKey] = defaultItem ? defaultItem.id : innerOption.items[0].id;
+          }
+        });
+        items = option.getDynamicItems(defaultValues);
+      }
+      const defaultItem = items && items.find((item) => item.default);
+      initialState[key] = defaultItem ? defaultItem.id : items && items[0] ? items[0].id : '';
+    });
+    return initialState;
+  };
+
+  const [values, setValues] = useState(getInitialState);
+  const [isDark, setIsDark] = useState(false);
+
+  useEffect(() => {
+    const checkDarkMode = () => {
+      const html = document.documentElement;
+      const isDarkMode =
+        html.classList.contains('dark') ||
+        html.getAttribute('data-theme') === 'dark' ||
+        html.style.colorScheme === 'dark';
+      setIsDark(isDarkMode);
+    };
+    checkDarkMode();
+    const observer = new MutationObserver(checkDarkMode);
+    observer.observe(document.documentElement, {
+      attributes: true,
+      attributeFilter: ['class', 'data-theme', 'style'],
+    });
+    return () => observer.disconnect();
+  }, []);
+
+  const handleRadioChange = (optionName, value) => {
+    setValues((prev) => ({ ...prev, [optionName]: value }));
+  };
+
+  const handleCheckboxChange = (optionName, itemId, isChecked) => {
+    setValues((prev) => {
+      const currentValues = prev[optionName] || [];
+      if (isChecked) {
+        return { ...prev, [optionName]: [...currentValues, itemId] };
+      }
+      return {
+        ...prev,
+        [optionName]: currentValues.filter((id) => id !== itemId),
+      };
+    });
+  };
+
+  const handleTextChange = (optionName, value) => {
+    setValues((prev) => ({ ...prev, [optionName]: value }));
+  };
+
+  const command = generateCommand(values);
+
+  const containerStyle = {
+    maxWidth: '900px',
+    margin: '0 auto',
+    display: 'flex',
+    flexDirection: 'column',
+    gap: '4px',
+  };
+  const cardStyle = {
+    padding: '8px 12px',
+    border: `1px solid ${isDark ? '#374151' : '#e5e7eb'}`,
+    borderLeft: `3px solid ${isDark ? '#E85D4D' : '#D45D44'}`,
+    borderRadius: '4px',
+    display: 'flex',
+    alignItems: 'center',
+    gap: '12px',
+    background: isDark ? '#1f2937' : '#fff',
+  };
+  const titleStyle = {
+    fontSize: '13px',
+    fontWeight: '600',
+    minWidth: '140px',
+    flexShrink: 0,
+    color: isDark ? '#e5e7eb' : 'inherit',
+  };
+  const itemsStyle = {
+    display: 'flex',
+    rowGap: '2px',
+    columnGap: '6px',
+    flexWrap: 'wrap',
+    alignItems: 'center',
+    flex: 1,
+  };
+  const labelBaseStyle = {
+    padding: '4px 10px',
+    border: `1px solid ${isDark ? '#9ca3af' : '#d1d5db'}`,
+    borderRadius: '3px',
+    cursor: 'pointer',
+    display: 'inline-flex',
+    flexDirection: 'column',
+    alignItems: 'center',
+    justifyContent: 'center',
+    fontWeight: '500',
+    fontSize: '13px',
+    transition: 'all 0.2s',
+    userSelect: 'none',
+    minWidth: '45px',
+    textAlign: 'center',
+    flex: 1,
+    background: isDark ? '#374151' : '#fff',
+    color: isDark ? '#e5e7eb' : 'inherit',
+  };
+  const checkedStyle = {
+    background: '#D45D44',
+    color: 'white',
+    borderColor: '#D45D44',
+  };
+  const disabledStyle = {
+    cursor: 'not-allowed',
+    opacity: 0.5,
+  };
+  const subtitleStyle = {
+    display: 'block',
+    fontSize: '9px',
+    marginTop: '1px',
+    lineHeight: '1.1',
+    opacity: 0.7,
+  };
+  const textInputStyle = {
+    flex: 1,
+    padding: '8px 10px',
+    borderRadius: '4px',
+    border: `1px solid ${isDark ? '#4b5563' : '#d1d5db'}`,
+    background: isDark ? '#111827' : '#fff',
+    color: isDark ? '#e5e7eb' : '#111827',
+    fontSize: '13px',
+  };
+  const commandDisplayStyle = {
+    flex: 1,
+    padding: '12px 16px',
+    background: isDark ? '#111827' : '#f5f5f5',
+    borderRadius: '6px',
+    fontFamily: "'Menlo', 'Monaco', 'Courier New', monospace",
+    fontSize: '12px',
+    lineHeight: '1.5',
+    color: isDark ? '#e5e7eb' : '#374151',
+    whiteSpace: 'pre-wrap',
+    overflowX: 'auto',
+    margin: 0,
+    border: `1px solid ${isDark ? '#374151' : '#e5e7eb'}`,
+  };
+
+  return (
+    <div style={containerStyle} className="not-prose">
+      {Object.entries(options).map(([key, option]) => {
+        if (option.condition && !option.condition(values)) {
+          return null;
+        }
+        const items = option.getDynamicItems ? option.getDynamicItems(values) : option.items || [];
+        return (
+          <div key={key} style={cardStyle}>
+            <div style={titleStyle}>{option.title}</div>
+            <div style={itemsStyle}>
+              {option.type === 'text' ? (
+                <input
+                  type="text"
+                  value={values[option.name] || ''}
+                  placeholder={option.placeholder || ''}
+                  onChange={(event) => handleTextChange(option.name, event.target.value)}
+                  style={textInputStyle}
+                />
+              ) : option.type === 'checkbox' ? (
+                (option.items || []).map((item) => {
+                  const isChecked = (values[option.name] || []).includes(item.id);
+                  const isDisabled =
+                    item.required ||
+                    (typeof item.disabledWhen === 'function' && item.disabledWhen(values));
+                  return (
+                    <label
+                      key={item.id}
+                      title={item.disabledReason || ''}
+                      style={{
+                        ...labelBaseStyle,
+                        ...(isChecked ? checkedStyle : {}),
+                        ...(isDisabled ? disabledStyle : {}),
+                      }}
+                    >
+                      <input
+                        type="checkbox"
+                        checked={isChecked}
+                        disabled={isDisabled}
+                        onChange={(event) =>
+                          handleCheckboxChange(option.name, item.id, event.target.checked)
+                        }
+                        style={{ display: 'none' }}
+                      />
+                      {item.label}
+                      {item.subtitle && (
+                        <small
+                          style={{
+                            ...subtitleStyle,
+                            color: isChecked ? 'rgba(255,255,255,0.85)' : 'inherit',
+                          }}
+                        >
+                          {item.subtitle}
+                        </small>
+                      )}
+                    </label>
+                  );
+                })
+              ) : (
+                items.map((item) => {
+                  const isChecked = values[option.name] === item.id;
+                  const isDisabled = Boolean(item.disabled);
+                  return (
+                    <label
+                      key={item.id}
+                      title={item.disabledReason || ''}
+                      style={{
+                        ...labelBaseStyle,
+                        ...(isChecked ? checkedStyle : {}),
+                        ...(isDisabled ? disabledStyle : {}),
+                      }}
+                    >
+                      <input
+                        type="radio"
+                        name={option.name}
+                        value={item.id}
+                        checked={isChecked}
+                        disabled={isDisabled}
+                        onChange={() => !isDisabled && handleRadioChange(option.name, item.id)}
+                        style={{ display: 'none' }}
+                      />
+                      {item.label}
+                      {item.subtitle && (
+                        <small
+                          style={{
+                            ...subtitleStyle,
+                            color: isChecked ? 'rgba(255,255,255,0.85)' : 'inherit',
+                          }}
+                        >
+                          {item.subtitle}
+                        </small>
+                      )}
+                    </label>
+                  );
+                })
+              )}
+            </div>
+          </div>
+        );
+      })}
+      <div style={cardStyle}>
+        <div style={titleStyle}>Run this Command:</div>
+        <pre style={commandDisplayStyle}>{command}</pre>
+      </div>
+    </div>
+  );
+};
diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py
index 4c850b44a..a4492bd17 100644
--- a/python/sglang/srt/configs/model_config.py
+++ b/python/sglang/srt/configs/model_config.py
@@ -499,7 +499,10 @@ class ModelConfig:
             or "LongcatFlashForCausalLMNextN" in self.hf_config.architectures
             or "DotsVLMForCausalLM" in self.hf_config.architectures
             or "MistralLarge3ForCausalLM" in self.hf_config.architectures
-            or "PixtralForConditionalGeneration" in self.hf_config.architectures
+            or (
+                "PixtralForConditionalGeneration" in self.hf_config.architectures
+                and getattr(self.hf_text_config, "kv_lora_rank", None) is not None
+            )
             or "MistralLarge3ForCausalLMEagle" in self.hf_config.architectures
             or "KimiK25ForConditionalGeneration" in self.hf_config.architectures
         ):
diff --git a/python/sglang/srt/models/mistral.py b/python/sglang/srt/models/mistral.py
index 632e857c2..f97ba66bc 100644
--- a/python/sglang/srt/models/mistral.py
+++ b/python/sglang/srt/models/mistral.py
@@ -13,19 +13,81 @@
 # ==============================================================================
 """Inference-only Mistral model."""
 
+import logging
+from collections.abc import Iterable
 from typing import List
 
+import regex as re
 import torch
 from transformers.models.mistral3.modeling_mistral3 import Mistral3MultiModalProjector
 
 from sglang.srt.managers.schedule_batch import MultimodalDataItem
 from sglang.srt.models.llama import LlamaForCausalLM
 
+logger = logging.getLogger(__name__)
+
 
 class MistralForCausalLM(LlamaForCausalLM):
     pass
 
 
+class MistralForCausalLMMistralFormat(MistralForCausalLM):
+    """Mistral GQA model loaded from mistral native format (params.json).
+
+    Handles weight name remapping from mistral native format to HF/Llama
+    format. This is the GQA counterpart to MistralLarge3ForCausalLM which
+    handles MLA models in mistral native format.
+    """
+
+    # fmt: off
+    remapping = {
+        r"layers\.(\d+)\.attention_norm\.weight": r"model.layers.\1.input_layernorm.weight",
+        r"layers\.(\d+)\.attention\.wq\.(\w+)": r"model.layers.\1.self_attn.q_proj.\2",
+        r"layers\.(\d+)\.attention\.wk\.(\w+)": r"model.layers.\1.self_attn.k_proj.\2",
+        r"layers\.(\d+)\.attention\.wv\.(\w+)": r"model.layers.\1.self_attn.v_proj.\2",
+        r"layers\.(\d+)\.attention\.wo\.(\w+)": r"model.layers.\1.self_attn.o_proj.\2",
+        r"layers\.(\d+)\.ffn_norm\.weight": r"model.layers.\1.post_attention_layernorm.weight",
+        r"layers\.(\d+)\.feed_forward\.w1\.(\w+)": r"model.layers.\1.mlp.gate_proj.\2",
+        r"layers\.(\d+)\.feed_forward\.w2\.(\w+)": r"model.layers.\1.mlp.down_proj.\2",
+        r"layers\.(\d+)\.feed_forward\.w3\.(\w+)": r"model.layers.\1.mlp.up_proj.\2",
+        r"norm\.weight": "model.norm.weight",
+        r"tok_embeddings\.weight": "model.embed_tokens.weight",
+        r"output\.weight": "lm_head.weight",
+    }
+    # fmt: on
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        return super().load_weights(self._remap_mistral_to_llama(weights))
+
+    def _remap_mistral_to_llama(
+        self, weights: Iterable[tuple[str, torch.Tensor]]
+    ) -> Iterable[tuple[str, torch.Tensor]]:
+        """Remap Mistral native format weight names to HF/Llama format."""
+        for name, loaded_weight in weights:
+            # Pass through weights already in HF/Llama layout so this loader
+            # tolerates mixed-format checkpoints (e.g. native body + HF-style
+            # multi_modal_projector weights spliced in by a parent class).
+            if name.startswith("model.") or name.startswith("lm_head."):
+                yield name, loaded_weight
+                continue
+
+            for k, v in self.remapping.items():
+                match = re.fullmatch(k, name)
+                if match:
+                    name = match.expand(v)
+                    break
+            else:
+                logger.warning(f"Unrecognized weight: {name}. Skipping.")
+                continue
+
+            if name.endswith(".qscale_act"):
+                name = re.sub(r"\.qscale_act$", ".input_scale", name)
+            elif name.endswith(".qscale_weight"):
+                name = re.sub(r"\.qscale_weight$", ".weight_scale", name)
+
+            yield name, loaded_weight
+
+
 class Mistral3ForConditionalGeneration:
     MULTIMODAL_PROJECTOR_TYPE = Mistral3MultiModalProjector
 
@@ -89,5 +151,45 @@ class Mistral3ForConditionalGeneration:
     def __call__(self, *args, **kwargs):
         return self.inner(*args, **kwargs)
 
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        """Normalize transformers v5 Mistral3 weight names for
+        LlavaForConditionalGeneration.load_weights.
+
+        v5 checkpoints lay out Mistral3 weights as:
+          model.language_model.{embed_tokens,layers.*,norm}.*
+          model.vision_tower.*
+          model.multi_modal_projector.*
+          lm_head.*
+
+        The Llava loader routes by top-level `language_model.` /
+        `vision_tower.` prefixes, stripping one segment before forwarding to
+        the sub-module.  The sub-module's own `load_weights` expects the
+        standard HF layout: `model.layers.*`, `model.embed_tokens.weight`,
+        `lm_head.weight` for Llama, and `vision_tower` internals at their
+        top level.  So we rewrite:
+          model.language_model.X   -> language_model.model.X
+          model.vision_tower.X     -> vision_tower.X
+          model.multi_modal_projector.X -> multi_modal_projector.X
+          lm_head.X                -> language_model.lm_head.X
+        """
+
+        def normalize(ws):
+            for name, w in ws:
+                if name.startswith("model.language_model."):
+                    rest = name[len("model.language_model.") :]
+                    name = "language_model.model." + rest
+                elif name.startswith("model.vision_tower."):
+                    name = "vision_tower." + name[len("model.vision_tower.") :]
+                elif name.startswith("model.multi_modal_projector."):
+                    name = (
+                        "multi_modal_projector."
+                        + name[len("model.multi_modal_projector.") :]
+                    )
+                elif name.startswith("lm_head."):
+                    name = "language_model." + name
+                yield name, w
+
+        return self.inner.load_weights(normalize(weights))
+
 
 EntryClass = [MistralForCausalLM, Mistral3ForConditionalGeneration]
diff --git a/python/sglang/srt/models/pixtral.py b/python/sglang/srt/models/pixtral.py
index ac0f351a9..c55fccd6d 100644
--- a/python/sglang/srt/models/pixtral.py
+++ b/python/sglang/srt/models/pixtral.py
@@ -45,6 +45,7 @@ from sglang.srt.managers.mm_utils import (
 )
 from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
 from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.mistral import MistralForCausalLMMistralFormat
 from sglang.srt.models.mistral_large_3 import MistralLarge3ForCausalLM
 
 USE_XFORMERS_OPS = False
@@ -94,10 +95,21 @@ class PixtralForConditionalGeneration(nn.Module):
 
         self.vision_args = VisionEncoderArgs(**vision_args)
 
-        self.language_model = MistralLarge3ForCausalLM(
-            config=self.config.text_config,
-            quant_config=kwargs.get("quant_config"),
-        )
+        # Choose language model based on text architecture:
+        # MLA text configs use DeepSeek V3 backbone (model_type="deepseek_v3"),
+        # GQA text configs use the standard Llama-style Mistral backbone.
+        text_config = self.config.text_config
+        is_mla = getattr(text_config, "model_type", "") == "deepseek_v3"
+        if is_mla:
+            self.language_model = MistralLarge3ForCausalLM(
+                config=text_config,
+                quant_config=kwargs.get("quant_config"),
+            )
+        else:
+            self.language_model = MistralForCausalLMMistralFormat(
+                config=text_config,
+                quant_config=kwargs.get("quant_config"),
+            )
 
         self.vision_encoder = VisionTransformer(self.vision_args)
 
diff --git a/python/sglang/srt/utils/hf_transformers/common.py b/python/sglang/srt/utils/hf_transformers/common.py
index 053550ded..914c8856e 100644
--- a/python/sglang/srt/utils/hf_transformers/common.py
+++ b/python/sglang/srt/utils/hf_transformers/common.py
@@ -227,6 +227,14 @@ def get_hf_text_config(config: PretrainedConfig):
             if getattr(_converted, "dtype", None) is None and parent_dtype is not None:
                 _converted.dtype = parent_dtype
             setattr(config, _attr, _converted)
+        elif _sub is not None and parent_dtype is not None:
+            # transformers v5 multimodal configs (e.g. Mistral3Config) carry
+            # `dtype` only on the top-level config, leaving the sub-configs at
+            # None. Without this, _get_and_verify_dtype falls back to float32
+            # and then "auto" downcasts to float16, which overflows the Pixtral
+            # vision tower on real images and produces NaN features.
+            if getattr(_sub, "dtype", None) is None:
+                _sub.dtype = parent_dtype
 
     # Priority: thinker_config > llm_config > language_config > text_config
     if hasattr(config, "thinker_config"):
diff --git a/python/sglang/srt/utils/hf_transformers/mistral_utils.py b/python/sglang/srt/utils/hf_transformers/mistral_utils.py
index cdaa72298..fa8f1c623 100644
--- a/python/sglang/srt/utils/hf_transformers/mistral_utils.py
+++ b/python/sglang/srt/utils/hf_transformers/mistral_utils.py
@@ -73,6 +73,22 @@ def adapt_config_dict(
             config_dict["architectures"] = ["MixtralForCausalLM"]
     else:
         config_dict["architectures"] = ["MistralForCausalLM"]
+        config_dict["model_type"] = "mistral"
+        # Mistral models use non-interleaved RoPE (is_neox_style=False),
+        # unlike Llama which defaults to True.
+        config_dict["rope_is_neox_style"] = False
+        # Remove None-valued MLA fields that would shadow defaults in
+        # model_config._derive_model_shapes (getattr returns None instead
+        # of the fallback when the attribute exists but is None).
+        for mla_key in (
+            "q_lora_rank",
+            "qk_rope_head_dim",
+            "qk_nope_head_dim",
+            "kv_lora_rank",
+            "v_head_dim",
+        ):
+            if config_dict.get(mla_key) is None:
+                config_dict.pop(mla_key, None)
 
     if bool(config_dict.get("yarn")):
         config_dict = _remap_mistral_yarn_args(config_dict)