Which is cheaper for input tokens: Sao10k: Llama 3 Euryale 70B v2.1 or Google: Gemini 2.5 Pro Preview 05-06?
Google: Gemini 2.5 Pro Preview 05-06 is cheaper on input token cost by $0.23 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)
Sao10k: Llama 3 Euryale 70B v2.1 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 | Sao10k: Llama 3 Euryale 70B v2.1 | Google: Gemini 2.5 Pro Preview 05-06 |
|---|---|---|
| Developer | sao10k | |
| Context Window | 8,192 | 1,048,576 |
| Input Cost / 1M Tokens | $1.48 | $1.25 |
| Output Cost / 1M Tokens | $1.48 | $10.00 |
| Projected Monthly Cost | $266 | $750 |
| Vision | ❌ No | ✅ Yes |
| Tool Calling | ✅ Yes | ✅ Yes |
| Structured Output | ❌ No | ✅ Yes |
| Reasoning | ❌ No | ✅ Yes |
| MMLU Score | N/A | 83.7 |
| GPQA | N/A | 82.2 |
Price History
Current Input / 1M
$1.48
Current Output / 1M
$1.48
Price History
Current Input / 1M
$1.25
Current Output / 1M
$10.00
Adjust your workload to see projected monthly costs.
Sao10k: Llama 3 Euryale 70B v2.1
$266
per month
Lower costGoogle: Gemini 2.5 Pro Preview 05-06
$750
per month
Live Recommendation
Sao10k: Llama 3 Euryale 70B v2.1 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 Sao10k: Llama 3 Euryale 70B v2.1 vs Google: Gemini 2.5 Pro Preview 05-06 pricing decisions.
Google: Gemini 2.5 Pro Preview 05-06 is cheaper on input token cost by $0.23 per 1M tokens.
Sao10k: Llama 3 Euryale 70B v2.1 is cheaper or equal on output token cost by $8.52 per 1M tokens.
$484 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.