Ring-2.6-1T (free) is free for input tokens while gpt-oss-120b costs $0.04 per 1M tokens.
API cost decision in 10 seconds
🔥Ring-2.6-1T (free)
vs
🔥gpt-oss-120b
Pick Ring-2.6-1T (free) when budget and context both matter.
Budget verdict
Pick Ring-2.6-1T (free) when budget and context both matter.
On the standard 1M input plus 500K output workload, Ring-2.6-1T (free) is estimated at $0 vs $0.13 for gpt-oss-120b, saving $0.13 (100% lower).
Ring-2.6-1T (free) is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $1.29. Use the calculator below to replace the sample workload with your own token volume.
Ring-2.6-1T (free) is free for output tokens while gpt-oss-120b costs $0.18 per 1M tokens.
Ring-2.6-1T (free) has 131.07K more context (2x larger).
Ring-2.6-1T (free) is free for the standard workload while the other model is estimated at $0.13.
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
Ring-2.6-1T (free) has the lower input price; Ring-2.6-1T (free) has the lower output price; Ring-2.6-1T (free) offers the larger context window. For the 1M input plus 500K output sample, Ring-2.6-1T (free) is cheaper for the standard workload.
For a 1M input token plus 500K output token workload, the estimated API cost is $0 for Ring-2.6-1T (free) and $0.13 for gpt-oss-120b.
Choose Ring-2.6-1T (free) when you care most about lower input-token price, lower output-token price, and larger context window.
Choose gpt-oss-120b when its provider, model quality, latency, or availability is more important than the numeric price/context winner.
- On the standard 1M input plus 500K output workload, Ring-2.6-1T (free) is estimated at $0 vs $0.13 for gpt-oss-120b, saving $0.13 (100% lower).
- Ring-2.6-1T (free) is free for the standard workload while the other model is estimated at $0.13.
- Ring-2.6-1T (free) is free for input tokens while gpt-oss-120b costs $0.04 per 1M tokens.
- Ring-2.6-1T (free) is free for output tokens while gpt-oss-120b costs $0.18 per 1M tokens.
- Ring-2.6-1T (free) has 131.07K more context (2x larger).
| Feature | (inclusionAI) | 🔥gpt-oss-120b (OpenAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0 | $0.039 |
| Completion Price per 1M tokens | $0 | $0.18 |
| Sample Workload Cost 1M input + 500K output | $0 | $0.13 |
| Context Window | 262.14K | 131.07K |
| Release Date | 2026-05-08 | 2025-08-05 |
| Popularity Rank current rank | #10 | #20 |
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Ring-2.6-1T (free) | On the standard 1M input plus 500K output workload, Ring-2.6-1T (free) is estimated at $0 vs $0.13 for gpt-oss-120b, saving $0.13 (100% lower). |
| High-volume input processing | Ring-2.6-1T (free) | Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill. |
| Long responses and chatbots | Ring-2.6-1T (free) | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Ring-2.6-1T (free) | A larger context window leaves more room for retrieved passages, conversation history, or source files. |
Related Alternatives
Cheaper alternatives
Review low-cost models ranked by a standard 1M input plus 500K output workload.
Open cheapest modelsLarger context alternatives
Find models with larger context windows for RAG, long documents, and codebase review.
Open largest context modelsProvider catalogs
Compare models within provider hubs before choosing a final API vendor.
Open provider hubsinclusionAI catalog
Review all tracked inclusionAI models before deciding whether this matchup is the right shortlist.
Open inclusionAI modelsOpenAI catalog
Check other OpenAI models with comparable pricing, context, or release timing.
Open OpenAI modelsRing-2.6-1T is a 1T-parameter-scale thinking model with 63B active parameters, built for real-world agent workflows that require both strong capability and operational efficiency. It is optimized for coding agents, tool...
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...