DeepSeek R1 vs Claude Sonnet 4.5: A Task-by-Task Routing Decision Tree

The question isn't which model is better. It's which model is right for this task — at this cost point.

DeepSeek R1 runs at roughly $0.55 per million input tokens on OpenRouter. Claude Sonnet 4.5 runs at $3. That's a 5.5× gap. If you're not routing deliberately, you're paying Sonnet prices for DeepSeek-quality work, or — worse — you've defaulted to DeepSeek for everything and your output quality has drifted.

This is the decision tree we use at MatsyaFlow.

The decision tree

Is the task creative, requires judgment, or involves conflicting context?
├─ NO  → DeepSeek R1 (Tier A)
└─ YES →
        Does it require deep reasoning chains or code generation > 100 lines?
        ├─ NO  → Claude Sonnet (Tier B)
        └─ YES →
                Is output high-stakes (customer-facing, financial, legal)?
                ├─ NO  → Claude Sonnet (Tier B)
                └─ YES → Claude Opus (Tier C)

That's it. Three questions, three tiers.

Where DeepSeek R1 wins

Summarisation. Give it a 10,000-word document and ask for a 200-word summary. DeepSeek R1 is fast, cheap, and accurate. Sonnet adds nothing here.

Classification. Sentiment, intent detection, category tagging. These are pattern-matching tasks. DeepSeek handles them well at 20% of the cost.

Structured extraction. Pull fields from unstructured text, convert formats, normalise data. Deterministic-ish tasks with clear success criteria. DeepSeek is the right tool.

Drafting from a template. If you have a template and the model is filling in blanks, that's Tier A work.

Where Claude Sonnet wins

Multi-step reasoning. Tasks where the model needs to hold a chain of inferences — debugging, architecture review, strategy — Sonnet's advantage is real.

Code generation at scale. Under 50 lines, DeepSeek is competitive. Above that, Sonnet's coherence over longer contexts pays for itself.

Ambiguous instructions. When the prompt is underspecified and the model needs to infer intent from context, Sonnet handles it better. DeepSeek tends to take the literal path.

Tone and nuance. Customer-facing copy, sensitive communications, anything where register matters. Sonnet.

The failure modes

Over-routing to Sonnet. Sending summaries, lookups, and formatting tasks to Sonnet because "it's safer." This is expensive and unnecessary. A well-prompted DeepSeek R1 handles these reliably.

Under-routing with DeepSeek. Sending complex reasoning tasks to DeepSeek to save money. The output quality drifts, errors compound, and you spend more debugging than you saved.

Not having a fallback. If your Tier A model is unavailable, your system should escalate to Tier B automatically — not fail. Build the fallback into your routing config.

Practical numbers

On a typical OpenClaw deployment (4,000 requests/month):

Without routing	With routing (68% Tier A)
~$47/month	~$14/month
100% Sonnet	68% DeepSeek, 28% Sonnet, 4% Opus

The config that produces this split is in the hybrid routing guide. It includes the classification prompt, OpenClaw config blocks, and fallback chain setup.

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Next in this series: Track A vs Track B — when to run parallel model sessions and how to merge outputs.