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GPT-6 Luna vs Olmo 3.1 32B Instruct
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.
Model
GPT-6 Luna
Context window
922K
922,000 tokens · ~692K words
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-6 Luna has about 14.1× the context window of the other in this pair.
GPT-6 Luna has 1306% more context capacity (922K vs 65K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use GPT-6 Luna. Its 922K context fits entire documents without chunking (vs 65K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | GPT-6 Luna | Olmo 3.1 32B Instruct |
|---|---|---|
| Context window | 922,000 tokens (922K) | 65,536 tokens (65K) |
| Max output tokens | 128,000 tokens (128K) | N/A |
| Speed tier | Balanced | Balanced |
| Vision | Yes | No |
| Function calling | Yes | Yes |
| Extended thinking | Yes | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Sep 2026 | Jan 2026 |
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 | GPT-6 Luna in | GPT-6 Luna out | Olmo 3.1 32B Instruct in | Olmo 3.1 32B Instruct out |
|---|---|---|---|---|
| Azure | $0.100/M | $0.500/M | — | — |
| Openai | $0.100/M | $0.500/M | — | — |
| Openrouter | $0.100/M | $0.500/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