Model Lifecycle and Changelog

• glm-5’s context window was raised from 300,000 to 1,048,576 tokens, the checkpoint’s full window — every glm-5 backend now serves --max-model-len 1048576. The alias, the endpoint, the checkpoint (Inferact/GLM-5.3-NVFP4), and the thinking behaviour are unchanged. Clients that pin a context window — VS Code’s maxInputTokens, OpenCode’s context, Crush’s context_window, CLAUDE_CODE_AUTO_COMPACT_WINDOW — should be raised to match.
• deepseek-v4-flash was changed from deepseek-ai/DeepSeek-V4-Flash-0731 to deepseek-ai/DeepSeek-V4-Flash-Vision-Exp, DeepSeek’s first multimodal checkpoint in the V4 family, and the model now accepts image input — image_url parts in the message content, the same way as on the other multimodal models. Video and audio are still not supported. The alias, the endpoint, and the 1,048,576-token context window are unchanged, so no client configuration has to change. The checkpoint is experimental (-Exp), on a model that is still evaluating.
• Thinking behaviour is unchanged by that swap, but the model card previously documented the wrong toggle. Thinking on deepseek-v4-flash is controlled by extra_body={"chat_template_kwargs": {"thinking": false}} — not enable_thinking, which this model’s template ignores — and its depth by reasoning_effort: low, high, or max, with max the default on the NRP deployment.

• qwen3 was changed from Qwen/Qwen3.5-397B-A17B-FP8 to Qwen/Qwen3.8-Flash-Next-FP8, an experimental preview of the architecture behind the coming Qwen4 family. Context size changed from 1,010,000 to 1,000,000 tokens, the documented ceiling for the model (262,144 natively, YaRN-extended). The alias, the endpoint, and the API are otherwise unchanged, so no client configuration has to change beyond the context window. The model is far smaller in what it activates — 180B total parameters with only 6B active per token, against 397B total and 17B active — and it scores higher: Artificial Analysis puts it at 55.8 on the Intelligence Index and 86.1% on Terminal-Bench v2.1, the best agentic-coding score in the catalog.
• Thinking behaviour on qwen3 changed with that upgrade. Reasoning depth is now tunable per request with the standard reasoning_effort parameter — low, medium, or xhigh, and xhigh is the model’s default — and reasoning from earlier turns is preserved by default (preserve_thinking). Both raise tokens per request relative to Qwen3.5, and the model is verbose to begin with. Requests that do not need reasoning should keep passing extra_body={"chat_template_kwargs": {"enable_thinking": false}}, which is unchanged.
• glm-5 (nvidia/GLM-5.2-NVFP4) was changed to GLM-5.3 (Inferact/GLM-5.3-NVFP4). The glm-5 alias, the endpoint, and the 300K context window are unchanged. Thinking is now always on — the old enable_thinking: false toggle no longer applies — and its depth is tunable per request with reasoning_effort: low, high, or max, with max the default.
• qwen3-small was changed from Qwen/Qwen3.6-27B to Qwen/Qwen3.8-27B. Context size changed from 1,010,000 to 1,000,000 tokens, the documented ceiling for the model. The alias, the endpoint, and the API are otherwise unchanged, so no client configuration has to change beyond the context window.
• Thinking behaviour on qwen3-small changed with that upgrade. Reasoning depth is now tunable per request with the standard reasoning_effort parameter — low, medium, or xhigh, and xhigh is the model’s default — and reasoning from earlier turns is preserved by default (preserve_thinking). Both raise tokens per request relative to Qwen3.6. Requests that do not need reasoning should keep passing extra_body={"chat_template_kwargs": {"enable_thinking": false}}, which is unchanged.

• glm-5 (nvidia/GLM-5.1-NVFP4) was changed to GLM-5.2 (nvidia/GLM-5.2-NVFP4)
• kimi was changed from moonshotai/Kimi-K2.6 to moonshotai/Kimi-K2.7-Code.
• gemma was changed from google/gemma-4-31B-it to google/gemma-4-31B-it-qat-w4a16-ct.
• gemma-small was changed from google/gemma-4-E4B-it to google/gemma-4-12B-it-qat-w4a16-ct. Context size changed.

• glm-4.7 (zai-org/GLM-4.7-FP8) was updated to glm-5 (nvidia/GLM-5.1-NVFP4)
• olmo (allenai/Olmo-3.1-32B-Instruct) has been removed due to lack of usage.

• gemma3 was renamed to gemma and switched from google/gemma-3-27b-it to google/gemma-4-31B-it. Context size changed.
• qwen3-small switched from Qwen/Qwen3.5-27B to Qwen/Qwen3.6-27B; context size changed.
• gemma-small (google/gemma-4-E4B-it) was added with audio input support.
• minimax-m2 upgraded from MiniMax-M2.5 to MiniMax-M2.7.
• kimi upgraded from Kimi-K2.5 to Kimi-K2.6.

• qwen3-embedding (Qwen/Qwen3-VL-Embedding-8B) added on the AI Gateway.
• embed-mistral (intfloat/e5-mistral-7b-instruct) decommissioned and replaced with qwen3-embedding due to incompatibilities with Jupyter AI.
• llama3-sdsc (Llama-3.3-70B-Instruct) removed from the Envoy AI Gateway after a long deprecation.
• glm-v (GLM-4.6V multimodal route) removed from the Envoy AI Gateway. Use glm-4.7 for text and other multimodal options for vision/video.

• minimax-m2 changed from MiniMaxAI/MiniMax-M2.1 to MiniMaxAI/MiniMax-M2.5.
• qwen3 changed from Qwen3-VL-235B-A22B-Thinking-FP8 to Qwen3.5-397B-A17B-FP8.

Added/Changed

• glm-4.7 switched to the official zai-org/GLM-4.7-FP8; more GPUs allocated due to its position in the catalog.
• kimi switched to the multimodal moonshotai/Kimi-K2.5.

Removed

• olmo (allenai/OLMo-2-0325-32B-Instruct) and gorilla (gorilla-llm/gorilla-openfunctions-v2) removed — both had been broken for months without anyone reporting.

• glm-v upgraded to zai-org/GLM-4.6V-FP8 — larger context size.
• glm-4.6 renamed to glm-4.7 and changed to the QuantTrio/GLM-4.7-GPTQ-Int4-Int8Mix quant.
• minimax-m2 changed from MiniMaxAI/MiniMax-M2 to MiniMaxAI/MiniMax-M2.1.

Added/Changed

• qwen3 changed to Qwen3-VL-235B-A22B-Thinking-FP8 — adds state-of-the-art vision and video.
• kimi (moonshotai/Kimi-K2-Thinking) — frontier programming model comparable to Claude Sonnet 4.5 / GPT-5.
• glm-4.6 (QuantTrio/GLM-4.6-GPTQ-Int4-Int8Mix) — comparable to Claude Sonnet 4 / Gemini 2.5 Pro.
• minimax-m2 (MiniMaxAI/MiniMax-M2) — comparable to Sonnet 4 / Gemini 2.5 Pro, fits in four A100s.
• gpt-oss (openai/gpt-oss-120b) — capable agentic model on a single A100 or two RTX A6000 GPUs. LTS candidate, supersedes deprecated Llama3 models.
• gemma3 moved to 2× RTX A6000 GPUs. Sliding-window attention allows full context.
• glm-v switched to zai-org/GLM-4.5V-FP8 on 4× L40 GPUs.

Removed

• llama3 (meta-llama/Llama-3.2-90B-Vision-Instruct) — consumed 4 A100s while being outperformed by single-GPU models.
• deepseek-r1 — used 8 GPUs but was very slow (5–6 tokens/s) at larger contexts.
• watt (watt-ai/watt-tool-8B) — removed for inactivity.