DeepSeek: R1 0528 vs Meta: Llama 3.3 70B Instruct
Head-to-head API pricing and cost comparison between DeepSeek’s DeepSeek: R1 0528 and Meta’s Meta: Llama 3.3 70B Instruct. Prices auto-refresh daily from OpenRouter.
DeepSeek: R1 0528 is 15% cheaper for input tokens; Meta: Llama 3.3 70B Instruct wins on output tokens at $0.79/1M.
Side-by-side comparison
| Spec | DeepSeek: R1 0528 | Meta: Llama 3.3 70B Instruct |
|---|---|---|
| Input price (per 1M) | $0.50 | $0.59 |
| Cached input (per 1M) | $0.35 | $0.29 |
| Output price (per 1M) | $2.15 | $0.79 |
| Batch input (per 1M) | — | — |
| Batch output (per 1M) | — | — |
| Reasoning price (per 1M) | — | — |
| Context window | 164K | 131K |
| Vision support | No | No |
| Caching support | Yes | Yes |
| Batch API | No | No |
| Reasoning capability | No | No |
Monthly cost at volume
Estimated monthly API spend at common production traffic levels (input/output tokens per request shown).
| Volume | DeepSeek: R1 0528 | Meta: Llama 3.3 70B Instruct | Savings |
|---|---|---|---|
1K req/day 500in / 200out tokens | $20.40 | $13.59 | $6.81 Meta: Llama 3.3 70B Instruct wins |
10K req/day 1500in / 500out tokens | $547.50 | $384.00 | $163.50 Meta: Llama 3.3 70B Instruct wins |
100K req/day 3000in / 800out tokens | $9,660 | $7,206 | $2,454 Meta: Llama 3.3 70B Instruct wins |
1M req/day 8000in / 2000out tokens | $249,000 | $189,000 | $60,000 Meta: Llama 3.3 70B Instruct wins |
Adjust input/output token counts, request volume, batch & cached pricing.
Related comparisons
Frequently asked questions
Which is cheaper, DeepSeek: R1 0528 or Meta: Llama 3.3 70B Instruct?
For input tokens, DeepSeek: R1 0528 is roughly 15% cheaper at $0.50/1M vs $0.59/1M. For output tokens, Meta: Llama 3.3 70B Instruct wins at $0.79/1M. Real-world cost depends on your input/output ratio — use the calculator to model your actual workload.
What’s the context window difference?
DeepSeek: R1 0528 has a context window of 164K tokens. Meta: Llama 3.3 70B Instruct offers 131K tokens. Larger context windows are valuable for long documents, RAG pipelines, and multi-turn conversations — but they come with higher input-token bills if you fill them every request.
Should I use DeepSeek: R1 0528 or Meta: Llama 3.3 70B Instruct?
Choose DeepSeek: R1 0528 if you’re already on the DeepSeek stack, want broad ecosystem support, or prefer its lower input price. Choose Meta: Llama 3.3 70B Instruct for Meta’s ecosystem, or its cheaper output tokens. Run a small benchmark on your own prompts before committing — price is only one axis.
How are these prices kept current?
Prices are pulled directly from OpenRouter’s public models API once every 24 hours via a Convex cron job, then normalized to per-1M-token figures. Last refresh: Oct 16, 2026.