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Minimax M2p1 vs Sonar
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.
Minimax M2p1 has about 1.6× the context window of the other in this pair.
Minimax M2p1 has 60% more context capacity (204K vs 128K tokens). Minimax M2p1 is 70% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Minimax M2p1. Its 204K context fits entire documents without chunking (vs 128K).
RAG / high-volume retrieval
Use Minimax M2p1. Input tokens are 70% cheaper — critical when sending large retrieved contexts.
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Minimax M2p1 | Sonar |
|---|---|---|
| Context window | 204,800 tokens (204K) | 128,000 tokens (128K) |
| Max output tokens | 204,800 tokens (204K) | N/A |
| Speed tier | Fast | Balanced |
| Vision | No | Yes |
| Function calling | Yes | No |
| Extended thinking | No | No |
| Prompt caching | Yes | No |
| Batch API | No | No |
| Release date | N/A | Jan 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 | Minimax M2p1 in | Minimax M2p1 out | Sonar in | Sonar out |
|---|---|---|---|---|
| Fireworks | $0.300/M | $1.20/M | — | — |
| Perplexity | — | — | $1.00/M | $1.00/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