Compare
Aion-RP 1.0 (8B) vs gpt-oss-120b (batch)
This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.
Context window · side by side
Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.
gpt-oss-120b (batch) has about 4× the context window of the other in this pair.
gpt-oss-120b (batch) has 300% more context capacity (131K vs 32K tokens). gpt-oss-120b (batch) is 81% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use gpt-oss-120b (batch). Its 131K context fits entire documents without chunking (vs 32K).
RAG / high-volume retrieval
Use gpt-oss-120b (batch). Input tokens are 81% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use gpt-oss-120b (batch). Its 117K max output lets you generate complete artifacts in one request.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Aion-RP 1.0 (8B) | gpt-oss-120b (batch) |
|---|---|---|
| Context window | 32,768 tokens (32K) | 131,072 tokens (131K) |
| Max output tokens | 29,491 tokens (29K) | 117,964 tokens (117K) |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | Feb 2025 | Aug 2025 |
Pricing matrix
Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.
| Provider | Aion-RP 1.0 (8B) in | Aion-RP 1.0 (8B) out | gpt-oss-120b (batch) in | gpt-oss-120b (batch) out |
|---|---|---|---|---|
| Openrouter | $0.800/M | $1.60/M | $0.150/M | $0.600/M |
Frequently asked questions
Powered by Mem0
Use a smaller model.
Get better results.
Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.
Example: a multi-turn chat session
80% less to send — works with any model