diff --git a/docs_new/cookbook/autoregressive/Google/Gemma4.mdx b/docs_new/cookbook/autoregressive/Google/Gemma4.mdx
index 78e1218dd..f383329bf 100644
--- a/docs_new/cookbook/autoregressive/Google/Gemma4.mdx
+++ b/docs_new/cookbook/autoregressive/Google/Gemma4.mdx
@@ -77,21 +77,15 @@ pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python'
 pip install 'git+https://github.com/huggingface/transformers.git@1423d22f7a3b62e8c70ad67b58ec25cd9b675897'
 ```
 
-### Docker (prebuilt dev image)
+### Docker
 
-Prebuilt development images bundle SGLang together with the matching transformers commit preinstalled, so no manual install is needed. All tags are multi-arch (`amd64` + `arm64`):
-
-| Tag | CUDA | Hardware |
-| --- | --- | --- |
-| `lmsysorg/sglang:dev-gemma-4-12B` | 13.0 | Default — amd64 (H200 / B200) + arm64 (GB200 / GB300) |
-| `lmsysorg/sglang:dev-cu13-gemma-4-12B` | 13.0 | Alias of the default tag |
-| `lmsysorg/sglang:dev-cu12-gemma-4-12B` | 12.9 | CUDA 12.x hosts |
+`lmsysorg/sglang:latest` (CUDA 13.0, multi-arch `amd64` + `arm64`) runs on both Hopper (H200) and Blackwell (B200 / GB200 / GB300):
 
 ```bash Command
 docker run --gpus all --ipc=host --shm-size 32g \
   -v ~/.cache/huggingface:/root/.cache/huggingface \
   -p 30000:30000 \
-  lmsysorg/sglang:dev-gemma-4-12B \
+  lmsysorg/sglang:latest \
   sglang serve --model-path google/gemma-4-12B-it \
     --reasoning-parser gemma4 --tool-call-parser gemma4 \
     --host 0.0.0.0 --port 30000
diff --git a/docs_new/cookbook/autoregressive/InclusionAI/Ling-2.5-1T.mdx b/docs_new/cookbook/autoregressive/InclusionAI/Ling-2.5-1T.mdx
index 68ab2a43c..008c2dd0c 100644
--- a/docs_new/cookbook/autoregressive/InclusionAI/Ling-2.5-1T.mdx
+++ b/docs_new/cookbook/autoregressive/InclusionAI/Ling-2.5-1T.mdx
@@ -24,14 +24,11 @@ metatags:
 
 ## 2. SGLang Installation
 
-Ling-2.5-1T requires a specific SGLang Docker image:
+Ling-2.5-1T runs on the standard SGLang Docker image:
 
 ```bash Command
-# For H200/B200
-docker pull lmsysorg/sglang:nightly-dev-20260213-a0ebaa64
-
-# For GB200/GB300
-docker pull lmsysorg/sglang:nightly-dev-cu13-20260213-a0ebaa64
+# NVIDIA (H200 / B200 / GB200 / GB300)
+docker pull lmsysorg/sglang:latest
 ```
 
 For other installation methods, please refer to the [official SGLang installation guide](../../../docs/get-started/install).
diff --git a/docs_new/cookbook/autoregressive/InclusionAI/Ring-2.5-1T.mdx b/docs_new/cookbook/autoregressive/InclusionAI/Ring-2.5-1T.mdx
index ad711e7ee..42fb93cd6 100644
--- a/docs_new/cookbook/autoregressive/InclusionAI/Ring-2.5-1T.mdx
+++ b/docs_new/cookbook/autoregressive/InclusionAI/Ring-2.5-1T.mdx
@@ -25,14 +25,11 @@ metatags:
 
 ## 2. SGLang Installation
 
-Ring-2.5-1T requires a specific SGLang Docker image:
+Ring-2.5-1T runs on the standard SGLang Docker image:
 
 ```bash Command
-# For H200/B200
-docker pull lmsysorg/sglang:nightly-dev-20260213-a0ebaa64
-
-# For GB200/GB300
-docker pull lmsysorg/sglang:nightly-dev-cu13-20260213-a0ebaa64
+# NVIDIA (H200 / B200 / GB200 / GB300)
+docker pull lmsysorg/sglang:latest
 
 # For MI300X/325X
 docker pull lmsysorg/sglang:v0.5.9-rocm700-mi30x
diff --git a/docs_new/cookbook/autoregressive/MiniMax/MiniMax-M3.mdx b/docs_new/cookbook/autoregressive/MiniMax/MiniMax-M3.mdx
index bc081ee38..19723cad6 100644
--- a/docs_new/cookbook/autoregressive/MiniMax/MiniMax-M3.mdx
+++ b/docs_new/cookbook/autoregressive/MiniMax/MiniMax-M3.mdx
@@ -35,11 +35,10 @@ Then run the **Python** output of the command panel below in that environment. T
 <Tab title="Docker">
 
 ```bash Command
-# Pull the M3 image the command panel selects for your platform, e.g.:
-docker pull lmsysorg/sglang:dev-cu13-minimax-m3
+docker pull lmsysorg/sglang:latest
 ```
 
-The command panel below fills in the right tag per platform: `dev-cu13-minimax-m3` (CUDA 13 — B300, GB200, GB300), `dev-cu12-minimax-m3` (CUDA 12 — Hopper H200), or `dev-minimax-m3` (default). On AMD Instinct it uses the matching ROCm image (MI300X/MI325X → `aigmkt/minimax-m3-sglang-rocm700-mi30x`, MI350X/MI355X → `aigmkt/minimax-m3-sglang-rocm720-mi35x`). For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker), substituting the inner `sglang serve ...` with what the command generator produces.
+On NVIDIA the command panel below uses `lmsysorg/sglang:latest` (CUDA 13, multi-arch — H200 + all Blackwell). On AMD Instinct it uses the matching ROCm image (MI300X/MI325X → `aigmkt/minimax-m3-sglang-rocm700-mi30x`, MI350X/MI355X → `aigmkt/minimax-m3-sglang-rocm720-mi35x`). For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker), substituting the inner `sglang serve ...` with what the command generator produces.
 
 <Note>
 These M3 dev images now **bundle MiniMax's MSA sparse-attention kernel** (`fmha_sm100`), so Blackwell users get the recommended fast path automatically — no manual install needed (see **§2.1**). On a custom image without it, the same recipe still serves on the built-in Triton sparse path.
@@ -87,7 +86,7 @@ Key characteristics as served by SGLang:
 
 ### 2.1 MSA sparse-attention fast path (recommended for Blackwell users)
 
-[MiniMax MSA](https://github.com/MiniMax-AI/MSA) (`fmha_sm100`, MIT-licensed) is the recommended Blackwell kernel for M3's main sparse-attention step — faster and more memory-efficient than the built-in Triton fallback. **It ships pre-installed in the M3 dev image** (`lmsysorg/sglang:dev-minimax-m3`, also published under the `dev-cu13-minimax-m3` tag), so the Blackwell recipe above engages it automatically with no extra setup — `import fmha_sm100` works out of the box and the kernels JIT-compile on first use. It is otherwise purely additive: on a custom image, install it (below) and the recipe engages it automatically; without it the same recipe still serves on the built-in Triton path. The swap is numerically equivalent (cosine ≥ 0.99999 vs Triton), decode stays CUDA-graph-capturable, prefill TTFT drops ~9–12% at 8K–64K context, and the MSA path survives memory configurations where the Triton path OOMs.
+[MiniMax MSA](https://github.com/MiniMax-AI/MSA) (`fmha_sm100`, MIT-licensed) is the recommended Blackwell kernel for M3's main sparse-attention step — faster and more memory-efficient than the built-in Triton fallback. **It ships pre-installed in `lmsysorg/sglang:latest`**, so the Blackwell recipe above engages it automatically with no extra setup — `import fmha_sm100` works out of the box and the kernels JIT-compile on first use. It is otherwise purely additive: on a custom image, install it (below) and the recipe engages it automatically; without it the same recipe still serves on the built-in Triton path. The swap is numerically equivalent (cosine ≥ 0.99999 vs Triton), decode stays CUDA-graph-capturable, prefill TTFT drops ~9–12% at 8K–64K context, and the MSA path survives memory configurations where the Triton path OOMs.
 
 **Requirements** (from the [MSA README](https://github.com/MiniMax-AI/MSA#requirements)):
 
diff --git a/docs_new/cookbook/autoregressive/NVIDIA/Nemotron3-Nano-Omni.mdx b/docs_new/cookbook/autoregressive/NVIDIA/Nemotron3-Nano-Omni.mdx
index fce05d5cf..29bd16733 100644
--- a/docs_new/cookbook/autoregressive/NVIDIA/Nemotron3-Nano-Omni.mdx
+++ b/docs_new/cookbook/autoregressive/NVIDIA/Nemotron3-Nano-Omni.mdx
@@ -49,7 +49,7 @@ pip install sglang
 uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python'
 
 # Or use Docker
-docker pull lmsysorg/sglang:dev-cu13-nemotronh-nano-omni-reasoning-v3
+docker pull lmsysorg/sglang:latest
 ```
 
 For the full Docker setup and other installation methods, refer to the [official SGLang installation guide](../../../docs/get-started/install).
diff --git a/docs_new/cookbook/autoregressive/Poolside/Laguna-M.1.mdx b/docs_new/cookbook/autoregressive/Poolside/Laguna-M.1.mdx
index 2e65f387b..ea643df4a 100644
--- a/docs_new/cookbook/autoregressive/Poolside/Laguna-M.1.mdx
+++ b/docs_new/cookbook/autoregressive/Poolside/Laguna-M.1.mdx
@@ -27,15 +27,15 @@ cd sglang
 uv pip install -e python
 ```
 
-Then run the **Python** output of the command panel below in that environment. The **Docker** tab is simpler — its image (`dev-cu13-618-nightly`) bundles the CUDA-13 runtime and the M.1 code. Once M.1 support lands in a tagged release, `uv pip install sglang` will pull it directly.
+Then run the **Python** output of the command panel below in that environment. The **Docker** tab is simpler — `lmsysorg/sglang:latest` bundles the CUDA-13 runtime and the M.1 code.
 
 </Tab>
 
 <Tab title="Docker">
 
 ```bash Command
-# Pinned nightly with the Laguna-M.1 build (PR #28400 + #28604; CUDA 13 — covers H200 + all Blackwell):
-docker pull lmsysorg/sglang:dev-cu13-618-nightly
+# CUDA 13 — covers H200 + all Blackwell:
+docker pull lmsysorg/sglang:latest
 ```
 
 For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.
diff --git a/docs_new/cookbook/autoregressive/Poolside/Laguna-XS-2.1.mdx b/docs_new/cookbook/autoregressive/Poolside/Laguna-XS-2.1.mdx
index 39e6cf1b1..5423eeda2 100644
--- a/docs_new/cookbook/autoregressive/Poolside/Laguna-XS-2.1.mdx
+++ b/docs_new/cookbook/autoregressive/Poolside/Laguna-XS-2.1.mdx
@@ -34,7 +34,7 @@ Then run the **Python** output of the command panel below in that environment.
 <Tab title="Docker">
 
 ```bash Command
-docker pull lmsysorg/sglang:dev-cu13-laguna-xs-2-1
+docker pull lmsysorg/sglang:latest
 ```
 
 For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.
diff --git a/docs_new/cookbook/autoregressive/Poolside/Laguna-XS.2.mdx b/docs_new/cookbook/autoregressive/Poolside/Laguna-XS.2.mdx
index b8aef70b1..eeddd0d7c 100644
--- a/docs_new/cookbook/autoregressive/Poolside/Laguna-XS.2.mdx
+++ b/docs_new/cookbook/autoregressive/Poolside/Laguna-XS.2.mdx
@@ -62,9 +62,8 @@ python3 -m pip install --extra-index-url https://docs.sglang.ai/whl/cu130 \
 python3 -m pip install --extra-index-url https://docs.sglang.ai/whl/cu129 \
   "sglang[all]==0.5.12.dev20260509+g096ad02b0"
 
-# Or use Docker (multi-arch amd64/arm64)
-docker pull lmsysorg/sglang:dev-cu13-laguna-xs2 # CUDA 13 (H200 / B200)
-docker pull lmsysorg/sglang:dev-cu12-laguna-xs2 # CUDA 12 (H200)
+# Or use Docker (multi-arch amd64/arm64; CUDA 13, H200 / B200)
+docker pull lmsysorg/sglang:latest
 ```
 
 For the full Docker setup and other installation methods, please refer to the [official SGLang installation guide](../../../docs/get-started/install).
diff --git a/docs_new/cookbook/autoregressive/Qwen/Qwen3.6.mdx b/docs_new/cookbook/autoregressive/Qwen/Qwen3.6.mdx
index 58ffa02be..92063dd59 100644
--- a/docs_new/cookbook/autoregressive/Qwen/Qwen3.6.mdx
+++ b/docs_new/cookbook/autoregressive/Qwen/Qwen3.6.mdx
@@ -78,11 +78,8 @@ uv pip install sglang
 # Or install from source
 uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python'
 
-# Or use Docker (NVIDIA GPUs)
+# Or use Docker (NVIDIA GPUs; also serves the NVFP4 variant nvidia/Qwen3.6-27B-NVFP4)
 docker pull lmsysorg/sglang:latest
-
-# For the NVFP4 variant (nvidia/Qwen3.6-27B-NVFP4), use the dedicated dev image
-docker pull lmsysorg/sglang:dev-cu13-dev-qwen36-27b-nvfp4
 ```
 
 For the full Docker setup and other installation methods, please refer to the [official SGLang installation guide](../../../docs/get-started/install).
diff --git a/docs_new/cookbook/autoregressive/StepFun/Step3.5.mdx b/docs_new/cookbook/autoregressive/StepFun/Step3.5.mdx
index 82c8a1d5a..1ff5a82b6 100644
--- a/docs_new/cookbook/autoregressive/StepFun/Step3.5.mdx
+++ b/docs_new/cookbook/autoregressive/StepFun/Step3.5.mdx
@@ -22,14 +22,14 @@ Step-3.5-Flash is currently available in SGLang via Docker image install.
 ### Docker (NVIDIA)
 ```bash Command
 # Pull the docker image
-docker pull lmsysorg/sglang:dev-pr-18084
+docker pull lmsysorg/sglang:latest
 
 # Launch the container
 docker run -it --gpus all \
   --shm-size=32g \
   --ipc=host \
   --network=host \
-  lmsysorg/sglang:dev-pr-18084 bash
+  lmsysorg/sglang:latest bash
 ```
 
 ### Docker (AMD ROCm)
diff --git a/docs_new/cookbook/autoregressive/Tencent/Hunyuan3-Preview.mdx b/docs_new/cookbook/autoregressive/Tencent/Hunyuan3-Preview.mdx
index 982a4e043..e84d756f4 100644
--- a/docs_new/cookbook/autoregressive/Tencent/Hunyuan3-Preview.mdx
+++ b/docs_new/cookbook/autoregressive/Tencent/Hunyuan3-Preview.mdx
@@ -65,17 +65,13 @@ Please refer to the [official SGLang installation guide](../../../docs/get-start
   </thead>
   <tbody>
     <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA H200 / B200</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:hy3-preview`</td>
-    </tr>
-    <tr>
-      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA B300 / GB300</td>
-      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:hy3-preview-cu130`</td>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}>NVIDIA H200 / B200 / B300 / GB300</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>`lmsysorg/sglang:latest`</td>
     </tr>
   </tbody>
 </table>
 
-The `hy3-preview` tag bundles the HYV3 model code, the `hunyuan` tool-call / reasoning parsers, and the MTP draft-module runtime.
+`lmsysorg/sglang:latest` bundles the HYV3 model code, the `hunyuan` tool-call / reasoning parsers, and the MTP draft-module runtime.
 
 For SGLang CPU installation, please refer to the [CPU version installation guide](../../../docs/hardware-platforms/cpu_server#installation).
 
diff --git a/docs_new/cookbook/autoregressive/Xiaomi/MiMo-V2-Flash.mdx b/docs_new/cookbook/autoregressive/Xiaomi/MiMo-V2-Flash.mdx
index ecd602190..fb6971bb5 100644
--- a/docs_new/cookbook/autoregressive/Xiaomi/MiMo-V2-Flash.mdx
+++ b/docs_new/cookbook/autoregressive/Xiaomi/MiMo-V2-Flash.mdx
@@ -23,14 +23,14 @@ MiMo-V2-Flash is currently available in SGLang via Docker image and pip install.
 
 ```bash Command
 # Pull the docker image
-docker pull lmsysorg/sglang:dev-pr-15207
+docker pull lmsysorg/sglang:latest
 
 # Launch the container
 docker run -it --gpus all \
   --shm-size=32g \
   --ipc=host \
   --network=host \
-  lmsysorg/sglang:dev-pr-15207 bash
+  lmsysorg/sglang:latest bash
 ```
 
 ### Pip Installation
diff --git a/docs_new/src/snippets/configs/MiniMaxAI/minimax-m3.jsx b/docs_new/src/snippets/configs/MiniMaxAI/minimax-m3.jsx
index 7263934db..2a0c47b62 100644
--- a/docs_new/src/snippets/configs/MiniMaxAI/minimax-m3.jsx
+++ b/docs_new/src/snippets/configs/MiniMaxAI/minimax-m3.jsx
@@ -92,14 +92,13 @@ sgl-eval run mmmu_pro \\
   ],
 
   dockerImages: {
-    // M3-specific dev images (multi-arch amd64+arm64). cu13 carries the sm_103
-    // (B300/GB300) + Grace arm64 builds; cu12 is the Hopper/CUDA-12 build;
-    // dev-minimax-m3 is the rolling default.
-    b200: "lmsysorg/sglang:dev-minimax-m3",
-    b300: "lmsysorg/sglang:dev-cu13-minimax-m3",
-    gb200: "lmsysorg/sglang:dev-cu13-minimax-m3",
-    gb300: "lmsysorg/sglang:dev-cu13-minimax-m3",
-    h200: "lmsysorg/sglang:dev-cu12-minimax-m3",
+    // lmsysorg/sglang:latest (cu13, multi-arch amd64+arm64) covers H200 + all
+    // Blackwell (incl. sm_103 B300/GB300 and Grace arm64).
+    b200: "lmsysorg/sglang:latest",
+    b300: "lmsysorg/sglang:latest",
+    gb200: "lmsysorg/sglang:latest",
+    gb300: "lmsysorg/sglang:latest",
+    h200: "lmsysorg/sglang:latest",
     // AMD ROCm images — published M3 builds, by arch (gfx942 -> mi30x, gfx950 -> mi35x).
     mi300x: "aigmkt/minimax-m3-sglang-rocm700-mi30x",
     mi325x: "aigmkt/minimax-m3-sglang-rocm700-mi30x",
@@ -199,7 +198,7 @@ sgl-eval run mmmu_pro \\
   // B300 / GB200 / GB300, tp8 on B200. fa4 + page 128 + deep_gemm are the M3
   // SM100 auto-defaults on current main, so this is also the bare-launch
   // behavior; they engage MiniMax's MSA sparse-attention kernel (fmha_sm100,
-  // pre-installed in the dev-minimax-m3 images; see Configuration Tips), Triton
+  // pre-installed in lmsysorg/sglang:latest; see Configuration Tips), Triton
   // fallback otherwise.
   // AMD: tp8. MI350X/MI355X (gfx950) serve MXFP8 natively (backends auto). MI300X/
   // MI325X (gfx942) need --attention-backend aiter + --moe-runner-backend triton,
diff --git a/docs_new/src/snippets/configs/poolside/laguna-m1.jsx b/docs_new/src/snippets/configs/poolside/laguna-m1.jsx
index 0c60fb3e0..99dc2b22e 100644
--- a/docs_new/src/snippets/configs/poolside/laguna-m1.jsx
+++ b/docs_new/src/snippets/configs/poolside/laguna-m1.jsx
@@ -100,16 +100,14 @@ sgl-eval run gsm8k \\
     ["gsm8k_pct", "GSM8K", "%"],
   ],
 
-  // Pinned nightly with the Laguna-M.1 build (PR #28400 + #28604 + #28649; cu13 covers H200 + all Blackwell).
-  // dev-cu13-618-nightly was generated after the FP8 g_proj fix (#28649) landed, so it serves FP8 too.
-  // (Equivalent pip nightly: 0.5.14.dev20260618+g97e3b8998d.) Blackwell FP8 additionally needs the
-  // --fp8-gemm-backend triton flag (in those cells) until PR #28662 merges.
+  // lmsysorg/sglang:latest (cu13) covers H200 + all Blackwell and carries the
+  // Laguna-M.1 build (PR #28400 + #28604 + #28649, incl. the FP8 g_proj fix).
   dockerImages: {
-    h200:  "lmsysorg/sglang:dev-cu13-618-nightly",
-    b200:  "lmsysorg/sglang:dev-cu13-618-nightly",
-    b300:  "lmsysorg/sglang:dev-cu13-618-nightly",
-    gb200: "lmsysorg/sglang:dev-cu13-618-nightly",
-    gb300: "lmsysorg/sglang:dev-cu13-618-nightly",
+    h200:  "lmsysorg/sglang:latest",
+    b200:  "lmsysorg/sglang:latest",
+    b300:  "lmsysorg/sglang:latest",
+    gb200: "lmsysorg/sglang:latest",
+    gb300: "lmsysorg/sglang:latest",
   },
 
   github: {
@@ -232,7 +230,7 @@ sgl-eval run gsm8k \\
     },
     {
       // VERIFIED on 8xH200 (FP8, tp8): GSM8K 93.25%. FP8 needs the g_proj fix (PR #28649, MERGED) on
-      // top of #28400+#28604 — the pinned dev-cu13-618-nightly image has it. Hopper does NOT hit the
+      // top of #28400+#28604 — lmsysorg/sglang:latest has it. Hopper does NOT hit the
       // Blackwell DeepGEMM UE8M0 issue, so no --fp8-gemm-backend flag here.
       match: { hw: "h200", variant: "default", quant: "fp8", strategy: "balanced", nodes: "single" },
       verified: true,
diff --git a/docs_new/src/snippets/configs/poolside/laguna-xs21.jsx b/docs_new/src/snippets/configs/poolside/laguna-xs21.jsx
index 4f88be2b5..8267b9d76 100644
--- a/docs_new/src/snippets/configs/poolside/laguna-xs21.jsx
+++ b/docs_new/src/snippets/configs/poolside/laguna-xs21.jsx
@@ -142,11 +142,11 @@ sgl-eval run aime25 \\
     ["aime25_pct", "AIME25", "%"],
   ],
 
-  // Dedicated image built for this cookbook page (PR #29446 + #29761 preinstalled on cu13).
+  // lmsysorg/sglang:latest (cu13) carries the Laguna-XS.2.1 build (PR #29446 + #29761).
   dockerImages: {
-    h200:  "lmsysorg/sglang:dev-cu13-laguna-xs-2-1",
-    b300:  "lmsysorg/sglang:dev-cu13-laguna-xs-2-1",
-    gb300: "lmsysorg/sglang:dev-cu13-laguna-xs-2-1",
+    h200:  "lmsysorg/sglang:latest",
+    b300:  "lmsysorg/sglang:latest",
+    gb300: "lmsysorg/sglang:latest",
   },
 
   github: {