API cost decision in 10 seconds
Laguna M.1 (free) vs 🔥gpt-oss-120b
Pick Laguna M.1 (free) when budget is the priority.
Pricing data updated:
Prices normalized to USD per 1M tokens
Sample workload: 1M input + 500K output
Budget verdict
Pick Laguna M.1 (free) when budget is the priority.
On the standard 1M input plus 500K output workload, Laguna M.1 (free) is estimated at $0 vs $0.13 for gpt-oss-120b, saving $0.13 (100% lower).
Cost-first pickLaguna M.1 (free)
Context-first pickTie
Sample savings$0.13100%
10x traffic gap$1.29
The reported context window is tied, so cost and provider fit carry more weight. 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.
Cost sensitivity
Workload Sensitivity
Laguna M.1 (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.
| Workload shape | Token mix | Better pick | Laguna M.1 (free) | gpt-oss-120b |
|---|---|---|---|---|
| Input-heavy / RAG | 5M input + 500K output | Laguna M.1 (free) | $0 | $0.29 |
| Balanced workload | 1M input + 1M output | Laguna M.1 (free) | $0 | $0.22 |
| Output-heavy chatbot | 1M input + 5M output | Laguna M.1 (free) | $0 | $0.94 |
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
Laguna M.1 (free) has the lower input price, Laguna M.1 (free) has the lower output price, and Tie offers the larger context window.
For a 1M input token plus 500K output token workload, the estimated API cost is $0 for Laguna M.1 (free) and $0.13 for gpt-oss-120b.
Choose Laguna M.1 (free) when you care most about lower input-token price, and lower output-token price.
Choose gpt-oss-120b when its provider, model quality, latency, or availability is more important than the numeric price/context winner.
| Feature | Laguna M.1 (free) (Poolside) | 🔥gpt-oss-120b (OpenAI) |
|---|---|---|
| Input Price prompt tokens per 1M | $0 | $0.04 |
| Completion Price per 1M tokens | $0 | $0.18 |
| Sample Workload Cost 1M input + 500K output | $0 | $0.13 |
| Context Window | 131.07K | 131.07K |
| Release Date | 2026-04-28 | 2025-08-05 |
| Popularity | #20 |
Laguna M.1 is the flagship coding agent model from [Poolside](https://poolside.ai), optimized for complex software engineering tasks. Designed for agentic coding workflows, it supports tool calling and reasoning, with a 128K...
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...
Use-Case Decision Matrix
| Use case | Better pick | Why |
|---|---|---|
| Budget-constrained production | Laguna M.1 (free) | On the standard 1M input plus 500K output workload, Laguna M.1 (free) is estimated at $0 vs $0.13 for gpt-oss-120b, saving $0.13 (100% lower). |
| High-volume input processing | Laguna M.1 (free) | Lower prompt-token price matters most when prompts or retrieved passages dominate the bill. |
| Long responses and chatbots | Laguna M.1 (free) | Lower output-token price matters most when assistants generate many completion tokens. |
| RAG or long-document work | Tie | A larger context window leaves more room for retrieved passages and source files. |