Compare
Minimax Minimax M2 1 vs Phi 3 Small 8k
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
Minimax Minimax M2 1
Context window
196K
196,000 tokens · ~147K 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.
Minimax Minimax M2 1 has about 23.9× the context window of the other in this pair.
Minimax Minimax M2 1 has 2292% more context capacity (196K vs 8K tokens). Phi 3 Small 8k is 58% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Minimax Minimax M2 1. Its 196K context fits entire documents without chunking (vs 8K).
RAG / high-volume retrieval
Use Phi 3 Small 8k. Input tokens are 58% cheaper — critical when sending large retrieved contexts.
Long output (reports, code files)
Use Minimax Minimax M2 1. Its 8K 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 | Minimax Minimax M2 1 | Phi 3 Small 8k |
|---|---|---|
| Context window | 196,000 tokens (196K) | 8,192 tokens (8K) |
| Max output tokens | 8,192 tokens (8K) | 4,096 tokens (4K) |
| Speed tier | Fast | Balanced |
| Vision | No | No |
| Function calling | Yes | No |
| 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 | Minimax Minimax M2 1 in | Minimax Minimax M2 1 out | Phi 3 Small 8k in | Phi 3 Small 8k out |
|---|---|---|---|---|
| Aws Bedrock | $0.360/M | $1.44/M | — | — |
| Azure | — | — | $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