Compare
Dolphin vs Moonshot V1 8k Vision Preview
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
Moonshot V1 8k Vision Preview
Context window
8K
8,192 tokens · ~6K 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.
Dolphin has about 2× the context window of the other in this pair.
Dolphin has 100% more context capacity (16K vs 8K tokens). Moonshot V1 8k Vision Preview is 60% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Dolphin. Its 16K context fits entire documents without chunking (vs 8K).
RAG / high-volume retrieval
Use Moonshot V1 8k Vision Preview. Input tokens are 60% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Dolphin. 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 | Dolphin | Moonshot V1 8k Vision Preview |
|---|---|---|
| Context window | 16,384 tokens (16K) | 8,192 tokens (8K) |
| Max output tokens | 16,384 tokens (16K) | 8,192 tokens (8K) |
| Speed tier | Balanced | Balanced |
| Vision | No | Yes |
| Function calling | No | Yes |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | N/A | N/A |
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 | Dolphin in | Dolphin out | Moonshot V1 8k Vision Preview in | Moonshot V1 8k Vision Preview out |
|---|---|---|---|---|
| Moonshot | — | — | $0.200/M | $2.00/M |
| Nlp Cloud | $0.500/M | $0.500/M | — | — |
Frequently asked questions
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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