Cogito v2.1 671B is $0.75 cheaper per 1M input tokens (37.5% lower; 1.6x difference).
API cost decision in 10 seconds
NewGoogle Gemini Pro Latest vs Cogito v2.1 671B
Pick Cogito v2.1 671B for lower cost; pick Google Gemini Pro Latest only if the larger context window matters more.
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Data confirmed:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick Cogito v2.1 671B for lower cost; pick Google Gemini Pro Latest only if the larger context window matters more.
On the standard 1M input plus 500K output workload, Cogito v2.1 671B is estimated at $1.88 vs $8 for Google Gemini Pro Latest, saving $6.12 (76.6% lower).
Cost-first pickCogito v2.1 671B
Context-first pickGoogle Gemini Pro Latest
Sample savings$6.1276.6%
10x traffic gap$61.25
Google Gemini Pro Latest has more context, but Cogito v2.1 671B saves $6.12 on the standard workload. At 10x that traffic, the same price gap is about $61.25. Use the calculator below to replace the sample workload with your own token volume.
Cost sensitivity
Workload Sensitivity
Cogito v2.1 671B stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Google Gemini Pro Latest | Cogito v2.1 671B |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Cogito v2.1 671B | $16 | $6.88 |
| Balanced workload | 1M input + 1M output | Cogito v2.1 671B | $14 | $2.5 |
| Output-heavy chatbot | 1M input + 5M output | Cogito v2.1 671B | $62 | $7.5 |
Cogito v2.1 671B is $10.75 cheaper per 1M output tokens (89.6% lower; 9.6x difference).
Google Gemini Pro Latest has 920.58K more context (8.19x larger).
Cogito v2.1 671B is $6.12 cheaper on the standard workload (76.6% lower).
Estimate your workload cost
Your Workload Cost
This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.
Quick Decision
Cogito v2.1 671B has the lower input price; Cogito v2.1 671B has the lower output price; Google Gemini Pro Latest offers the larger context window. For the 1M input plus 500K output sample, Cogito v2.1 671B is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $8 for Google Gemini Pro Latest and $1.88 for Cogito v2.1 671B.
Choose Google Gemini Pro Latest when you care most about larger context window.
Choose Cogito v2.1 671B when you care most about lower input-token price, and lower output-token price.
- On the standard 1M input plus 500K output workload, Cogito v2.1 671B is estimated at $1.88 vs $8 for Google Gemini Pro Latest, saving $6.12 (76.6% lower).
- Cogito v2.1 671B is $6.12 cheaper on the standard workload (76.6% lower).
- Cogito v2.1 671B is $0.75 cheaper per 1M input tokens (37.5% lower; 1.6x difference).
- Cogito v2.1 671B is $10.75 cheaper per 1M output tokens (89.6% lower; 9.6x difference).
- Google Gemini Pro Latest has 920.58K more context (8.19x larger).
| Feature | NewGoogle Gemini Pro Latest (Google) | Cogito v2.1 671B (Deep Cogito) |
|---|---|---|
| Input Price prompt tokens per 1M | $2 | $1.25 |
| Completion Price per 1M tokens | $12 | $1.25 |
| Sample Workload Cost 1M input + 500K output | $8 | $1.88 |
| Context Window | 1.05M | 128K |
| Release Date |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Cogito v2.1 671B | On the standard 1M input plus 500K output workload, Cogito v2.1 671B is estimated at $1.88 vs $8 for Google Gemini Pro Latest, saving $6.12 (76.6% lower). |
| High-volume input processing | Cogito v2.1 671B | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Cogito v2.1 671B | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Google Gemini Pro Latest | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
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Cogito v2.1 671B MoE represents one of the strongest open models globally, matching performance of frontier closed and open models. This model is trained using self play with reinforcement learning...