Which is cheaper for input tokens: Qwen: Qwen3 30B A3B Thinking 2507 or Meta: Llama 3.2 3B Instruct?
Qwen: Qwen3 30B A3B Thinking 2507 is cheaper or equal on input token cost by $0.00 per 1M tokens.
Head-to-Head Pricing Benchmark
Side-by-side pricing and context window comparison for production model selection.
Default Recommendation (120M input + 60M output)
Qwen: Qwen3 30B A3B Thinking 2507 is lower-cost for the default monthly workload scenario.
Adjust the workload in the calculator below to see a live recommendation for your usage.
| Metric | Qwen: Qwen3 30B A3B Thinking 2507 | Meta: Llama 3.2 3B Instruct |
|---|---|---|
| Developer | qwen | meta-llama |
| Context Window | 32,768 | 80,000 |
| Input Cost / 1M Tokens | $0.0510 | $0.0510 |
| Output Cost / 1M Tokens | $0.3400 | $0.3400 |
| Projected Monthly Cost | $27 | $27 |
| Vision | ❌ No | ❌ No |
| Tool Calling | ✅ Yes | ❌ No |
| Structured Output | ✅ Yes | ❌ No |
| Reasoning | ✅ Yes | ❌ No |
| MMLU Score | 80.5 | 34.7 |
| GPQA | 70.7 | 25.5 |
Price History
Current Input / 1M
$0.0510
Current Output / 1M
$0.3400
Price History
Current Input / 1M
$0.0510
Current Output / 1M
$0.3400
Adjust your workload to see projected monthly costs.
Qwen: Qwen3 30B A3B Thinking 2507
$27
per month
Lower costMeta: Llama 3.2 3B Instruct
$27
per month
Live Recommendation
Qwen: Qwen3 30B A3B Thinking 2507 is lower-cost at 120M input + 60M output tokens/month.
Continue evaluation with more “A vs B pricing” decision pages.
Quick Compare
Select two models to see a head-to-head pricing breakdown.
Common questions for Qwen: Qwen3 30B A3B Thinking 2507 vs Meta: Llama 3.2 3B Instruct pricing decisions.
Qwen: Qwen3 30B A3B Thinking 2507 is cheaper or equal on input token cost by $0.00 per 1M tokens.
Qwen: Qwen3 30B A3B Thinking 2507 is cheaper or equal on output token cost by $0.00 per 1M tokens.
$0.00 difference for the default scenario (120M input + 60M output tokens/month).
Use this page to compare context window and token pricing, then open each model page to evaluate additional alternatives and monthly workload fit.