#Scenario Recommendations

This page recommends models from the validated set for chat, coding, and retrieval-augmented generation (RAG). The recommendation combines the public Artificial Analysis open-weight model leaderboard checked on 2026-08-24 with the deployment and workload evidence in this guide.

Artificial Analysis scores are capability signals, not a replacement for an application benchmark. This page uses the weights=open filter; it does not compare against hosted proprietary models. The open-weight leaderboard contains newer model releases than some of the checkpoints validated here. In particular, the leaderboard entry DeepSeek V4 Flash 0731 is used as a family-level signal for the validated DeepSeek-V4-Flash recipes; validate the exact checkpoint again before a production decision.

#At a glance

#Evidence from Artificial Analysis

The values below are the open-weight leaderboard's Intelligence Index (II), Coding Index, and Agentic Index. A blank value means that the leaderboard does not publish that index for the entry, rather than a score of zero.

These are the validated candidates only. The open-weight leaderboard's overall leaders include models that are not present in this deployment guide (for example, Kimi K3, Qwen3.8, and DeepSeek V4 Pro), so they are outside the recommendation scope until an Ascend deployment is validated here.

The leaderboard does not provide a dedicated RAG score. RAG selection therefore uses II as a response-quality proxy and combines it with the validated model's context length, prefix-cache behavior, and document modality. Retrieval quality, chunking, embedding choice, reranking, and citation evaluation still need a separate application-level test.

#Chat

Recommended: Qwen3.6-27B (W8A8). Its current leaderboard entry scores 37.7 on II, 53.7 on Coding Index, and 27.5 on Agentic Index. More importantly for a shared chat service, the validated recipe fits an 8-card Ascend 910B4 deployment as two TP=4 replicas and completed the 8k and 17.5k-input workloads with zero errors. It is the best balance in this validated set between capability, replica-level latency, and hardware footprint.

Use DeepSeek-V4-Flash (W8A8) when answer quality, reasoning, and long-context conversations are more important than the 16-card requirement. Its corresponding current leaderboard family entry is materially stronger (II 51.8), and the validated recipe supports 200k context plus cross-rank prefix reuse. Its larger deployment should not be presented as a lower-cost chat default.

#Coding

Recommended: DeepSeek-V4-Flash (W8A8). The current DeepSeek V4 Flash 0731 leaderboard entry has the strongest public coding signal in this set (Coding Index 69.1; Agentic Index 48.4). The validated deployment also exposes the DeepSeek tool and reasoning parsers, a 200k context window, and a long-context aggregation path. The score is a family/version signal: the exact Eco-Tech/DeepSeek-V4-Flash-w8a8-mtp checkpoint still requires Terminal-Bench or an equivalent coding-agent acceptance run before production sign-off.

Use MiniMax-M2.5 (W8A8) when an already exercised agent integration is the priority. Pi and OpenCode were driven through the MaaS gateway and the pinned vLLM-Ascend parser had no closing-tag pollution across 3,123 tool executions. The leaderboard marks M2.5 deprecated and publishes no Coding/Agentic Index for it, and the validation still records parser failures on complex chains; treat it as a POC fallback, not evidence that it outranks DeepSeek.

#RAG

Recommended: DeepSeek-V4-Flash (W8A8). There is no RAG-specific Artificial Analysis index, so this recommendation combines the highest II signal in the validated family with the deployment properties that matter for RAG: 200k context, effective cross-rank prefix reuse, and a validated 17.5k-token multi-turn workload. It is a good default when retrieved passages are long or repeated across users.

Use Gemma-4-31B-it for multimodal or document-heavy knowledge bases. Its validated text path has a 262k context window and its model supports multimodal inputs, while the P4+D4 recipe keeps prefill and decode isolated on eight 910B3 cards. Its AA II (29.7) is below DeepSeek's, so choose it for modality and operational fit rather than leaderboard quality.

For every RAG deployment, measure retrieval recall, grounded answer accuracy, citation correctness, and end-to-end latency with the target corpus. A high model leaderboard score cannot compensate for a poor retriever or incorrectly sized context budget.

#Revalidation rule

Re-run the scenario benchmark when the model revision, quantization, vLLM-Ascend image, prompt template, retriever, or context budget changes. Keep the leaderboard snapshot date with the benchmark result; Artificial Analysis updates its entries and may deprecate older releases.