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DeepSeek V3.1 vs Llama Guard 4 12B
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
Same context window size for both models.
DeepSeek V3.1 and Llama Guard 4 12B have identical context windows (163K tokens). Llama Guard 4 12B is 10% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
RAG / high-volume retrieval
Use Llama Guard 4 12B. Input tokens are 10% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | DeepSeek V3.1 | Llama Guard 4 12B |
|---|---|---|
| Context window | 163,840 tokens (163K) | 163,840 tokens (163K) |
| Max output tokens | 163,840 tokens (163K) | 163,840 tokens (163K) |
| Speed tier | Balanced | Balanced |
| Vision | No | Yes |
| Function calling | Yes | No |
| Extended thinking | Yes | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Aug 2025 | Apr 2025 |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | DeepSeek V3.1 in | DeepSeek V3.1 out | Llama Guard 4 12B in | Llama Guard 4 12B out |
|---|---|---|---|---|
| Deepinfra | — | — | $0.180/M | $0.180/M |
| Groq | — | — | $0.200/M | $0.200/M |
| Openrouter | $0.200/M | $0.800/M | — | — |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model