DeepSeek V4 dropped today — officially, openly, with weights on Hugging Face and API live as of April 24, 2026. Three things make this directly relevant to OpenClaw users: explicit official integration support, a Flash variant that runs locally via Ollama, and a hard retirement deadline for the old model names that will break configs in July.

⚠️ Action Required If You Use DeepSeek

deepseek-chat and deepseek-reasoner will be fully retired and inaccessible after July 24, 2026, 15:59 UTC.

They currently route to V4-Flash (non-thinking) and V4-Flash (thinking) respectively. But after July 24, the model names stop working entirely. If your OpenClaw config references either of these, update it now: deepseek-v4-flash or deepseek-v4-pro.

What DeepSeek V4 Actually Is

Two models, same architecture family, same 1M token context window:

Model Total Params Active Params Training Data Best For
V4-Pro 1.6T (MoE) 49B 33T tokens Complex reasoning, coding, world knowledge
V4-Flash 284B (MoE) 13B 32T tokens Fast agent tasks, local inference, cost-sensitive

Both are fully open-source with weights on Hugging Face. Both support the OpenAI ChatCompletions API and Anthropic API formats — which means OpenClaw works with both out of the box. Both support Thinking/Non-Thinking mode.

DeepSeek's own benchmark claims for V4-Pro: leads all open-source models on agentic coding, beats all open models on Math/STEM/Coding, trails only Gemini-3.1-Pro on world knowledge. That's a strong claim. Community testing on r/openclaw is confirming it holds for the agentic tasks that matter to OpenClaw users — long-context reading, code generation, and tool-calling reliability.

The Flash Tier: What "13B Active" Actually Means for Local Inference

V4-Flash is a Mixture-of-Experts model. The total parameter count is 284B, but only 13B parameters are activated per forward pass. This is the same architecture trick that makes MoE models efficient — you get quality closer to a large dense model while actually running something closer to a 13B model during inference.

In practice: V4-Flash is available on Ollama right now (ollama pull deepseek-v4-flash). On a machine with 16GB RAM — like the NucBoxM5Ultra or a Mac Mini M4 — it runs reasonably well for agent tasks that don't require frontier-level reasoning.

The r/openclaw community's take: "The flash tier is going to be huge for agent builders — most OpenClaw tasks are simple tool calls and message routing that don't need deep reasoning." That's correct. Heartbeat log entries, draft summaries, routing decisions, simple Q&A — V4-Flash handles all of this at $0/request locally.

Official OpenClaw Integration

DeepSeek's official launch announcement explicitly names OpenClaw as an integrated agent platform alongside Claude Code and OpenCode. This isn't community integration — DeepSeek has fine-tuned V4 for OpenClaw-style agentic workflows and says it's "already driving their in-house agentic coding."

What that means in practice: tool calling, multi-turn instruction following, and SOUL.md-style constraint adherence should be better tuned in V4 than in V3. The flash tier is described as performing "on par with V4-Pro on simple Agent tasks" — which covers the majority of what OpenClaw actually does in production.

Configuring DeepSeek V4 in OpenClaw

Three options depending on your use case:

Option 1: V4-Pro via API (complex reasoning tasks)

// openclaw.json — primary model
"model": "deepseek/deepseek-v4-pro"

// Or via OpenRouter for redundancy:
"model": "openrouter/deepseek/deepseek-v4-pro"

Option 2: V4-Flash via API (routine tasks, cost-sensitive)

"model": "deepseek/deepseek-v4-flash"

Option 3: V4-Flash locally via Ollama (free inference)

// Pull the model first:
ollama pull deepseek-v4-flash

// Then configure in OpenClaw:
"model": "ollama/deepseek-v4-flash"

Option 4: Tiered routing (recommended)

// Routine tasks → local V4-Flash (free)
// Complex reasoning → V4-Pro or Claude via API
"model": "deepseek/deepseek-v4-pro",
"heartbeat": { "model": "ollama/deepseek-v4-flash" },
"skills": { "defaultModel": "ollama/deepseek-v4-flash" }

How DeepSeek V4 Fits the Model Alternatives Picture

With Anthropic restricting Claude for some OpenClaw users and OpenAI business quotas becoming impractical, the community has been evaluating alternatives. DeepSeek V4 enters that conversation as the strongest open-source option yet — particularly because the Flash tier local inference path effectively eliminates API cost anxiety for a large portion of OpenClaw workloads.

Updated model routing recommendation for 2026:

The 1M context advantage: V4's 1M token context window is standard — not a paid tier. For OpenClaw users building complex memory architectures with large workspace files, this removes a real constraint. You can inject significantly more context per turn without hitting limits that force compaction.

The July 24 Deadline: Check Your Config Now

If you're running OpenClaw with DeepSeek models, this is the most important takeaway from today's launch: the old model names die on July 24.

Check your openclaw.json, any skill configs, and any SOUL.md references to model names. Three months sounds like a lot — but if your agent is running autonomously and you forget, it will silently fail on July 25.

Quick audit command: grep -r "deepseek-chat\|deepseek-reasoner" ~/.openclaw/ — any hits need updating before July 24.

Want Your Model Routing Set Up for 2026?

ClawReady sets up tiered model routing as part of every setup — frontier API for complex tasks, local models for routine work, with fallbacks configured. You get resilience built in from day one, using the best available options including DeepSeek V4-Flash locally.

See Setup Packages →