Meta: Llama 3.3 70B Instruct vs Qwen: Qwen3 VL 30B A3B Thinking
Head-to-head API pricing and cost comparison between Meta’s Meta: Llama 3.3 70B Instruct and Qwen’s Qwen: Qwen3 VL 30B A3B Thinking. Prices auto-refresh daily from OpenRouter.
Meta: Llama 3.3 70B Instruct is 23% cheaper for input tokens; Meta: Llama 3.3 70B Instruct also wins on output tokens.
Side-by-side comparison
| Spec | Meta: Llama 3.3 70B Instruct | Qwen: Qwen3 VL 30B A3B Thinking |
|---|---|---|
| Input price (per 1M) | $0.10 | $0.13 |
| Cached input (per 1M) | — | — |
| Output price (per 1M) | $0.32 | $1.56 |
| Batch input (per 1M) | — | — |
| Batch output (per 1M) | — | — |
| Reasoning price (per 1M) | — | — |
| Context window | 131K | 131K |
| Vision support | No | Yes |
| Caching support | No | No |
| Batch API | No | No |
| Reasoning capability | No | Yes |
Monthly cost at volume
Estimated monthly API spend at common production traffic levels (input/output tokens per request shown).
| Volume | Meta: Llama 3.3 70B Instruct | Qwen: Qwen3 VL 30B A3B Thinking | Savings |
|---|---|---|---|
1K req/day 500in / 200out tokens | $3.42 | $11.31 | $7.89 Meta: Llama 3.3 70B Instruct wins |
10K req/day 1500in / 500out tokens | $93.00 | $292.50 | $199.50 Meta: Llama 3.3 70B Instruct wins |
100K req/day 3000in / 800out tokens | $1,668 | $4,914 | $3,246 Meta: Llama 3.3 70B Instruct wins |
1M req/day 8000in / 2000out tokens | $43,200 | $124,800 | $81,600 Meta: Llama 3.3 70B Instruct wins |
Adjust input/output token counts, request volume, batch & cached pricing.
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Frequently asked questions
Which is cheaper, Meta: Llama 3.3 70B Instruct or Qwen: Qwen3 VL 30B A3B Thinking?
For input tokens, Meta: Llama 3.3 70B Instruct is roughly 23% cheaper at $0.10/1M vs $0.13/1M. For output tokens, Meta: Llama 3.3 70B Instruct wins at $0.32/1M. Real-world cost depends on your input/output ratio — use the calculator to model your actual workload.
What’s the context window difference?
Meta: Llama 3.3 70B Instruct has a context window of 131K tokens. Qwen: Qwen3 VL 30B A3B Thinking 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 Meta: Llama 3.3 70B Instruct or Qwen: Qwen3 VL 30B A3B Thinking?
Choose Meta: Llama 3.3 70B Instruct if you’re already on the Meta stack, want broad ecosystem support, or prefer its lower input price. Choose Qwen: Qwen3 VL 30B A3B Thinking for Qwen’s ecosystem, native vision input, or its differentiated capabilities. 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: May 28, 2026.