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GPT-5 Chat vs ReMM SLERP 13B
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-5 Chat has about 20.8× the context window of the other in this pair.
GPT-5 Chat has 1983% more context capacity (128K vs 6K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use GPT-5 Chat. Its 128K context fits entire documents without chunking (vs 6K).
Long output (reports, code files)
Use GPT-5 Chat. Its 16K 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 | GPT-5 Chat | ReMM SLERP 13B |
|---|---|---|
| Context window | 128,000 tokens (128K) | 6,144 tokens (6K) |
| Max output tokens | 16,384 tokens (16K) | 4,096 tokens (4K) |
| Speed tier | Balanced | Fast |
| Vision | Yes | No |
| Function calling | No | No |
| Extended thinking | No | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | Aug 2025 | Jul 2023 |
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-5 Chat in | GPT-5 Chat out | ReMM SLERP 13B in | ReMM SLERP 13B out |
|---|---|---|---|---|
| Openrouter | — | — | $1.88/M | $1.88/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